Can Social Networks and E-Portfolio be Used together for Enhancing Learning Effects and Attitudes?

ERIC Educational Resources Information Center

Baris, M. Fatih; Tosun, Nilgun

2013-01-01

As the choices that information technologies offer has increased, efficiency of these at education, the area and time it covers increases, as well. E-portfolio and social networks are the latest choices that informational technologies offer. In this study, both technologies have been used at education and results have been analyzed. For that…

Using Deep Learning for Gamma Ray Source Detection at the First G-APD Cherenkov Telescope (FACT)

NASA Astrophysics Data System (ADS)

Bieker, Jacob

2018-06-01

Finding gamma-ray sources is of paramount importance for Imaging Air Cherenkov Telescopes (IACT). This study looks at using deep neural networks on data from the First G-APD Cherenkov Telescope (FACT) as a proof-of-concept of finding gamma-ray sources with deep learning for the upcoming Cherenkov Telescope Array (CTA). In this study, FACT’s individual photon level observation data from the last 5 years was used with convolutional neural networks to determine if one or more sources were present. The neural networks used various architectures to determine which architectures were most successful in finding sources. Neural networks offer a promising method for finding faint and extended gamma-ray sources for IACTs. With further improvement and modifications, they offer a compelling method for source detection for the next generation of IACTs.

An Introduction to Neural Networks for Hearing Aid Noise Recognition.

ERIC Educational Resources Information Center

Kim, Jun W.; Tyler, Richard S.

1995-01-01

This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

Modelling Blended Solutions for Higher Education: Teaching, Learning, and Assessment in the Network and Mobile Technology Era

ERIC Educational Resources Information Center

Bocconi, Stefania; Trentin, Guglielmo

2014-01-01

The article addresses the role of network and mobile technologies in enhancing blended solutions with a view to (a) enriching the teaching/learning processes, (b) exploiting the opportunities it offers for their observability, and hence for their monitoring and formative/summative assessment. It will also discuss how such potential can only be…

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

Real-World Examples: Developing a Departmental Alumni Network

ERIC Educational Resources Information Center

Ashline, George

2017-01-01

We describe the context for and implementation of a departmental alumni network. More than a database compiling facts about graduates, this network provides students with information and inspiration. It also offers a wonderful opportunity to support lifelong learning through the development of collaborative relationships between alumni and faculty…

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

The Use of Twitter in the Creation of Educational Professional Learning Opportunities

ERIC Educational Resources Information Center

Ross, Carrie R.; Maninger, Robert M.; LaPrairie, Kimberly N.; Sullivan, Sam

2015-01-01

This study sought to examine how educators are using Twitter to increase their professional learning opportunities beyond the boundaries of traditional professional development offers, and whether educators feel a greater sense of fulfillment receiving professional development through networking and community learning than they do through…

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

ERIC Educational Resources Information Center

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

2017-01-01

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

Integration of an Intelligent Tutoring System in a Course of Computer Network Design

ERIC Educational Resources Information Center

Verdú, Elena; Regueras, Luisa M.; Gal, Eran; de Castro, Juan P.; Verdú, María J.; Kohen-Vacs, Dan

2017-01-01

INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines…

The Impact of Social Media on Informal Learning in Museums

ERIC Educational Resources Information Center

Russo, Angelina; Watkins, Jerry; Groundwater-Smith, Susan

2009-01-01

This paper posits that social networking can take a central role in learning in informal environments such as museums, libraries and galleries. It argues that social media offers young people agency previously unavailable in informal learning environments in order to explore complex responses to and participation with cultural content. The paper…

Teachers Beware! The Dark Side of Social Networking

ERIC Educational Resources Information Center

Belch, Harry Ess

2012-01-01

Think teachers can post what they want on their own time? Think again. Many have lost their jobs over social networking gaffes in recent years. In this article, the author shares what he has learned about how school districts cope with teachers and online social networking sites, and offers recommendations to teachers who want to have an online…

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Social Networking and Democratic Practices as Spheres for Innovative Musical Learning

ERIC Educational Resources Information Center

Thorgersen, Cecilia Ferm; Georgii-Hemming, Eva

2012-01-01

This chapter takes into account and discusses innovative learning in the 21st digital and communicative century based on life-world-phenomenology and Hannah Arendt's view of democracy. From this point of view, the authors address and discuss how democratic practices can offer innovative musical learning in relation to what is taking place in…

Research Trends in the Study of ICT Based Learning Communities: A Bibliometric Analysis

ERIC Educational Resources Information Center

Hernández, Jonathan Bermúdez; Chalela, Salim; Arias, Jackeline Valencia; Arias, Alejandro Valencia

2017-01-01

The current opportunities to develop and acquire knowledge in the network, Information and Communications Technology (ICT) play a major role in the learning process. This research offers a bibliometric analysis in order to examine the state of the research activity carried out in relation to the learning communities based on ICT. The indicators…

Dynamic and Interactive Mathematics Learning Environments: Opportunities and Challenges for Future Research

ERIC Educational Resources Information Center

Olive, John

2013-01-01

New networking and social interaction technologies offer new media for learning and teaching both inside and outside the classroom. How and what kind of learning may take place in these new media is the main focus of this paper. An integrative theoretical framework for investigating these questions is posed based on the Didactic Tetrahedron (Olive…

Facilitating Participation: From the EML Web Site to the Learning Network for Learning Design

ERIC Educational Resources Information Center

Hummel, Hans G. K.; Tattersall, Colin; Burgos, Daniel; Brouns, Francis; Kurvers, Hub; Koper, Rob

2005-01-01

This article investigates conditions for increasing active participation in on-line communities. As a case study, we use three generations of facilities designed to promote learning in the area of Educational Modelling Languages. Following a description of early experience with a conventional web site and with a community site offering facilities…

Using the Facebook Group as a Learning Management System: An Exploratory Study

ERIC Educational Resources Information Center

Wang, Qiyun; Woo, Huay Lit; Quek, Choon Lang; Yang, Yuqin; Liu, Mei

2012-01-01

Facebook is a popular social networking site. It, like many other new technologies, has potential for teaching and learning because of its unique built-in functions that offer pedagogical, social and technological affordances. In this study, the Facebook group was used as a learning management system (LMS) in two courses for putting up…

Critical Practitioners, Developing Researchers: The Story of Practitioner Research in the Lifelong Learning Sector

ERIC Educational Resources Information Center

Hillier, Yvonne

2010-01-01

This article examines the growth of practitioner research in England through the creation of the Learning and Skills Research Network (LSRN) and identifies its effect on subsequent developments in what is generally known as the Lifelong Learning Sector (LLS). It offers an analysis of this development as a case study in developing practitioner…

Face to Facebook: Social Media and the Learning and Teaching Potential of Symmetrical, Synchronous Communication

ERIC Educational Resources Information Center

VanDoorn, George; Eklund, Antoinette A.

2013-01-01

Social networking offers teachers and learners exciting opportunities to communicate. Web 2.0 and its synchronous communications platforms provide new avenues for teachers to deliver curriculum and facilitate learning. Further, they provide new avenues for students to engage and intensify their own learning. Being able to chat in real-time with a…

Online People Tagging: Social (Mobile) Network(ing) Services and Work-Based Learning

ERIC Educational Resources Information Center

Cook, John; Pachler, Norbert

2012-01-01

Social and mobile technologies offer users unprecedented opportunities for communicating, interacting, sharing, meaning-making, content and context generation. And, these affordances are in constant flux driven by a powerful interplay between technological innovation and emerging cultural practices. Significantly, also, they are starting to…

A Network Neuroscience of Human Learning: Potential To Inform Quantitative Theories of Brain and Behavior

PubMed Central

Bassett, Danielle S.; Mattar, Marcelo G.

2017-01-01

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. PMID:28259554

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.

PubMed

Bassett, Danielle S; Mattar, Marcelo G

2017-04-01

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Five Central Psychological Challenges Facing Effective Mobile Learning

ERIC Educational Resources Information Center

Terras, Melody M.; Ramsay, Judith

2012-01-01

Web 2.0 technology not only offers the opportunity of massively parallel interconnected networks that support the provision of information and communication anytime and anywhere but also offers immense opportunities for collaboration and sharing of user-generated content. This information-rich environment may support both formal and informal…

ITV: Helping Teachers Teach and Students Learn. Staff Development Resources, 1988-89.

ERIC Educational Resources Information Center

South Carolina Educational Television Network Columbia.

This guide provides a listing, with descriptions and schedules, of the staff development programs offered over educational television and radio through the South Carolina Educational Television Network. The radio and/or television programs offered are listed by subject area: administration, adult education, arts, career education,…

Improving Audio Quality in Distance Learning Applications.

ERIC Educational Resources Information Center

Richardson, Craig H.

This paper discusses common causes of problems encountered with audio systems in distance learning networks and offers practical suggestions for correcting the problems. Problems and discussions are divided into nine categories: (1) acoustics, including reverberant classrooms leading to distorted or garbled voices, as well as one-dimensional audio…

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Pacesetter in Personalized Learning

ERIC Educational Resources Information Center

Jacobs, Joanne

2017-01-01

The Chicago International Charter School (CICS) Irving Park's middle school is one of 130 schools nationwide piloting the Summit Learning Program (SLP), developed--and offered entirely free--by Summit Public Schools, a high-performing charter network based in California. Summit's eight schools, two of them in Washington State, are known for an…

Designing Informal Learning Experiences for Early Career Academics Using a Knowledge Ecosystem Model

ERIC Educational Resources Information Center

Miller, Faye; Partridge, Helen; Bruce, Christine; Hemmings, Brian

2017-01-01

This article presents a "knowledge ecosystem" model of how early career academics experience using information to learn while building their social networks for developmental purposes. Developed using grounded theory methodology, the model offers a way of conceptualising how to empower early career academics through (1) agency…

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

Application of two neural network paradigms to the study of voluntary employee turnover.

PubMed

Somers, M J

1999-04-01

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Neural network submodel as an abstraction tool: relating network performance to combat outcome

NASA Astrophysics Data System (ADS)

Jablunovsky, Greg; Dorman, Clark; Yaworsky, Paul S.

2000-06-01

Simulation of Command and Control (C2) networks has historically emphasized individual system performance with little architectural context or credible linkage to `bottom- line' measures of combat outcomes. Renewed interest in modeling C2 effects and relationships stems from emerging network intensive operational concepts. This demands improved methods to span the analytical hierarchy between C2 system performance models and theater-level models. Neural network technology offers a modeling approach that can abstract the essential behavior of higher resolution C2 models within a campaign simulation. The proposed methodology uses off-line learning of the relationships between network state and campaign-impacting performance of a complex C2 architecture and then approximation of that performance as a time-varying parameter in an aggregated simulation. Ultimately, this abstraction tool offers an increased fidelity of C2 system simulation that captures dynamic network dependencies within a campaign context.

Building Social Capital through Action Learning: An Insight into the Entrepreneur

ERIC Educational Resources Information Center

Taylor, David W.; Jones, Oswald; Boles, Kevin

2004-01-01

According to Woolcock, social capital can be defined as the "norms and networks facilitating collective action for mutual benefit". Furthermore, Gabbay and Leenders suggest that social capital offers some potential for integrating the proliferation of network research that has been developed over the last 30 years. Examines an innovatory…

K-12 Networking: Breaking Down the Walls of the Learning Environment.

ERIC Educational Resources Information Center

Epler, Doris, Ed.

Networks can benefit school libraries by: (1) offering multiple user access to information; (2) managing and distributing information and data; (3) allowing resources to be shared; (4) improving and enabling communications; (5) improving the management of resources; and (6) creating renewed interest in the library and its resources. As a result,…

Enhance Your Twitter Experience

ERIC Educational Resources Information Center

Miller, Shannon McClintock

2010-01-01

The author has been encouraging teachers, students, and others to join Twitter and build their personal learning networks (PLNs) ever since she delved into this great social networking site. In this article, she offers a few other tools and tips that can improve the Twitter experience of those who have opened up an account and dabbled a bit but…

Tools for Schools. Volume 12, Number 4, May-June 2009

ERIC Educational Resources Information Center

von Frank, Valerie, Ed.

2009-01-01

This newsletter is published four times a year. It offers articles on school improvement, organizational planning, training, and managing change. This issue contains: (1) Link Up & Learn: Use Technology to Create a Personal Learning Network to Connect with Experts and Mentors Everywhere (Valerie von Frank); (2) NSDC Tool: Get Connected with…

The Metadata Education and Research Information Commons (MERIC): A Collaborative Teaching and Research Initiative

ERIC Educational Resources Information Center

Vellucci, Sherry L.; Hsieh-Yee, Ingrid; Moen, William E.

2007-01-01

The networked environment forced a sea change in Library and Information Science (LIS) education. Most LIS programs offer a mixed-mode of instruction that integrates online learning materials with more traditional classroom pedagogical methods and faculty are now responsible for developing content and digital learning objects. The teaching commons…

Dialogue and Connectivism: A New Approach to Understanding and Promoting Dialogue-Rich Networked Learning

ERIC Educational Resources Information Center

Ravenscroft, Andrew

2011-01-01

Connectivism offers a theory of learning for the digital age that is usually understood as contrasting with traditional behaviourist, cognitivist, and constructivist approaches. This article will provide an original and significant development of this theory through arguing and demonstrating how it can benefit from social constructivist…

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…

Learning with Portable Digital Devices in Australian Schools: 20 Years On!

ERIC Educational Resources Information Center

Newhouse, C. Paul

2014-01-01

Portable computing technologies such as laptops, tablets, smartphones, wireless networking, voice/stylus input, and plug and play peripheral devices, appear to offer the means of finally realising much of the long heralded vision for computers to support learning in schools. There is the possibility for the technology to finally become a…

Networked Learning in 70001 Programs.

ERIC Educational Resources Information Center

Fine, Marija Futchs

The 7000l Training and Employment Institute offers self-paced instruction through the use of computers and audiovisual materials to young people to improve opportunities for success in the work force. In 1988, four sites were equipped with Apple stand-alone software in an integrated learning system that included courses in reading and math, test…

Transforming the Classroom for Collaborative Learning in the 21st Century

ERIC Educational Resources Information Center

Christen, Amy

2009-01-01

Today's hyper-connected students live in a world of instant interpersonal communications and virtually infinite access to information and educational resources. But this networked world, and the powerful learning tools it offers, has yet to penetrate the typical classroom. In many ways educational institutions are spinning their curricular wheels,…

Medical education practice-based research networks: Facilitating collaborative research.

PubMed

Schwartz, Alan; Young, Robin; Hicks, Patricia J

2016-01-01

Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). We searched for extant networks through peer-reviewed literature and the world-wide web. We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks.

Medical education practice-based research networks: Facilitating collaborative research

PubMed Central

Schwartz, Alan; Young, Robin; Hicks, Patricia J.; APPD LEARN, For

2016-01-01

Abstract Background: Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. Aims: In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). Methods: We searched for extant networks through peer-reviewed literature and the world-wide web. Results: We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. Conclusions: We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks. PMID:25319404

Two web-based laboratories of the FisL@bs network: Hooke's and Snell's laws

NASA Astrophysics Data System (ADS)

de la Torre, L.; Sánchez, J.; Dormido, S.; Sánchez, J. P.; Yuste, M.; Carreras, C.

2011-03-01

FisL@bs is a network of remote and virtual laboratories for physics university education via the Internet that offers students the possibility of performing hands-on experiments in different fields of physics in two ways: simulation and real remote operation. This paper gives a detailed account of a novel way in physics in which distance learning students can gain practical experience autonomously. FisL@bs uses the same structure as AutomatL@bs, a network of virtual and remote laboratories for learning/teaching of control engineering, which has been in operation for four years. Students can experiment with the laboratories offered using an Internet connection and a Java-compatible web browser. This paper, specially intended for university educators but easily comprehensible even for undergraduate students, explains how the portal works and the hardware and software tools used to create it. In addition, it also describes two physics experiments already available: spring elasticity and the laws of reflection and refraction.

Providing QoS through machine-learning-driven adaptive multimedia applications.

PubMed

Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

2004-06-01

We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

The offer network protocol: Mathematical foundations and a roadmap for the development of a global brain

NASA Astrophysics Data System (ADS)

Heylighen, Francis

2017-01-01

The world is confronted with a variety of interdependent problems, including scarcity, unsustainability, inequality, pollution and poor governance. Tackling such complex challenges requires coordinated action. The present paper proposes the development of a self-organizing system for coordination, called an "offer network", that would use the distributed intelligence of the Internet to match the offers and needs of all human, technological and natural agents on the planet. This would maximize synergy and thus minimize waste and scarcity of resources. Implementing such coordination requires a protocol that formally defines agents, offers, needs, and the network of condition-action rules or reactions that interconnect them. Matching algorithms can then determine self-sustaining subnetworks in which each consumed resource (need) is also produced (offer). After sketching the elements of a mathematical foundation for offer networks, the paper proposes a roadmap for their practical implementation. This includes step-by-step integration with technologies such as the Semantic Web, ontologies, the Internet of Things, reputation and recommendation systems, reinforcement learning, governance through legal constraints and nudging, and ecosystem modeling. The resulting intelligent platform should be able to tackle nearly all practical and theoretical problems in a bottom-up, distributed manner, thus functioning like a Global Brain for humanity.

Towards a SIM-Less Existence: The Evolution of Smart Learning Networks

ERIC Educational Resources Information Center

Al-Khouri, Ali M.

2015-01-01

This article proposes that the widespread availability of wireless networks creates a case in which there is no real need for SIM cards. Recent technological developments offer the capability to outperform SIM cards and provide more innovative dimensions to current systems of mobility. In this context of changing realities in the domain of…

"The Social Network" and the Legal Environment of Business: An Opportunity for Student-Centered Learning

ERIC Educational Resources Information Center

McGill, Shelley

2013-01-01

Aaron Sorkin has a passion for words--his signature movie and television scripts are fast talking, jargon laced, word pictures that are instantly recognizable. "The Social Network," Sorkin's 2011 Academy Award Winning movie about the founding of Facebook, Inc., offers more than just witty banter; it provides an ideal teaching platform for…

Modeling language and cognition with deep unsupervised learning: a tutorial overview

PubMed Central

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

Modeling language and cognition with deep unsupervised learning: a tutorial overview.

PubMed

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

Using social media to enhance career development opportunities for health promotion professionals.

PubMed

Roman, Leah A

2014-07-01

For health promotion professionals, social media offers many ways to engage with a broader range of colleagues; participate in professional development events; promote expertise, products, or services; and learn about career-enhancing opportunities such as funding and fellowships. Previous work has recommended "building networking into what you are already doing." This article provides updated and new social media resources, as well as practical examples and strategies to promote effective use of social media. Social media offers health promotion professionals cost-effective opportunities to enhance their career by building communities of practice, participating in professional development events, and enriching classroom learning. Developing the skills necessary to use social media for networking is important in the public health workforce, especially as social media is increasingly used in academic and practice settings. © 2014 Society for Public Health Education.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

PubMed Central

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

PubMed

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

Deep Learning and Its Applications in Biomedicine.

PubMed

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Synchronous Writing Environments: Real-Time Interaction in Cyberspace (Technology Tidbits).

ERIC Educational Resources Information Center

Anderson-Inman, Lynne; And Others

1996-01-01

Discusses three types of synchronous writing environments, each offering teachers and students a vehicle for using electronic text to promote literacy-based learning communities: classroom collaboration, networked notetaking, and virtual communities. (SR)

Sharing is Winning: Cooperative Learning about Atmospheric Composition Change

NASA Astrophysics Data System (ADS)

Schuepbach, E.

2010-09-01

This contribution presents evolving good practice in disseminating the body of know-how, skills and competencies within the networked community of atmospheric scientists as established in ACCENT. The promotion of early-career scientists, and encouraging the next generation to move into the field were among the key issues addressed by the "Training and Education" programme in the European Network of Excellence in Atmospheric Composition Change (ACCENT). Dissemination avenues include a virtual knowledge train carrying the wealth of high-quality scientific learning material developed with experts involved in the ACCENT network. Learning opportunities on current research in atmospheric composition change in Europe were also created during face-to-face training workshops. Real-life examples of pressing air quality issues were addressed in meetings with stakeholder groups that offered opportunities for mutual learning in inspiring partnerships. In order to increase the expertise in atmospheric composition change across Europe, activities were organized with the general public (e.g., Café Scientifique), where the participating early-career scientists were confronted with questions from lay people. For interested teachers, didactic translations of compact overviews on air quality science topics developed in ACCENT offer links with the typical European science curriculum and go beyond school book content. Some of the educational events, methods and tools are described in a booklet published in 2009 ("We Care for Clean Air!", ISBN 978-88-95665-01-6). The electronic version and all training material can be downloaded from www.accent-network.org/portal/education - a valuable resource for teachers and learners around the globe.

YouTube as a participatory culture.

PubMed

Chau, Clement

2010-01-01

There is an explosion of youth subscriptions to original content-media-sharing Web sites such as YouTube. These Web sites combine media production and distribution with social networking features, making them an ideal place to create, connect, collaborate, and circulate. By encouraging youth to become media creators and social networkers, new media platforms such as YouTube offer a participatory culture in which youth can develop, interact, and learn. As youth development researchers, we must be cognizant of this context and critically examine what this platform offers that might be unique to (or redundant of) typical adolescent experiences in other developmental contexts.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Interprofessional learning, impression management, and spontaneity in the acute healthcare setting.

PubMed

Bell, Elaine; McAllister, Sue; Ward, Paul R; Russell, Alison

2016-09-01

Spontaneous learning is integral to definitions of interprofessional learning (IPL) because it has been suggested that spontaneous learning can be deeply connected with the work that people do in collaboration with colleagues via their professional networks. However, its nature and the processes involved are not well understood. Goffman's theory of impression management offers a useful theoretical framework to consider the way in which interaction in the workplace connects to spontaneous learning. This article explores the current literature to investigate the usefulness of this framework to better understand and identify spontaneous learning in the workplace. Aspects such as the connections between spontaneous learning occurring in formal and informal work activities, the spaces in which it occurs, and the influence of professional networking are considered. It is proposed that research directed to developing a better understanding of the nature of spontaneous learning in IPL will assist in connecting this learning to formal IPL curricula, enhancing IPL and patient outcomes.

A smart-pixel holographic competitive learning network

NASA Astrophysics Data System (ADS)

Slagle, Timothy Michael

Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization-multiplexed PCM readout, and gains of up to 60 were observed when a time-sequential read-out technique was used. Finally, the optical competitive learning laboratory system is described, including some necessary modifications to the previous architectures, and the data acquisition and control system developed for the system. Experimental results showing phase conjugation of the WTA outputs, holographic interconnect storage, associative storage between input images and WTA neuron outputs, and WTA array switching are presented, demonstrating the functions necessary for the operation of the optical learning system.

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

NASA Astrophysics Data System (ADS)

Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

2016-10-01

The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists and educators, and offer "just-in-time" opportunities to support constituents exploring emerging NASA STEM education, from diverse educators to the curious learner of any age.

Satellite -Based Networks for U-Health & U-Learning

NASA Astrophysics Data System (ADS)

Graschew, G.; Roelofs, T. A.; Rakowsky, S.; Schlag, P. M.

2008-08-01

The use of modern Information and Communication Technologies (ICT) as enabling tools for healthcare services (eHealth) introduces new ways of creating ubiquitous access to high-level medical care for all, anytime and anywhere (uHealth). Satellite communication constitutes one of the most flexible methods of broadband communication offering high reliability and cost-effectiveness of connections meeting telemedicine communication requirements. Global networks and the use of computers for educational purposes stimulate and support the development of virtual universities for e-learning. Especially real-time interactive applications can play an important role in tailored and personalised services.

How to Survive and Prosper in the Real World after Graduation: An Unconventional Approach to Achieving Your Success.

ERIC Educational Resources Information Center

Connors, Shawn M.

Unconventional advice to help college graduates be successful is offered. Ways to find sources of money (i.e., jobs) are described, including: using services or products offered through the mail, making phone calls to build a network of contacts in a particular industry, attending seminars to further one's knowledge of a field, learning about…

Deep learning with coherent nanophotonic circuits

NASA Astrophysics Data System (ADS)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Empirical Models of Social Learning in a Large, Evolving Network.

PubMed

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

Empirical Models of Social Learning in a Large, Evolving Network

PubMed Central

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

A Person-Centered, Registry-Based Learning Health System for Palliative Care: A Path to Coproducing Better Outcomes, Experience, Value, and Science

PubMed Central

Kamal, Arif H.; Kirkland, Kathryn B.; Meier, Diane E.; Nelson, Eugene C.; Pantilat, Steven Z.

2018-01-01

Abstract Background: Palliative care offers an approach to the care of people with serious illness that focuses on quality of life and aligning care with individual and family goals, and values in the context of what is medically achievable. Objective: Measurement of the impact of palliative care is critical for determining what works for which patients in what settings, to learn, improve care, and ensure access to high value care for people with serious illness. Methods: A learning health system that includes patients and families partnering with clinicians and care teams, is directly linked to a registry to support networks for improvement and research, and offers an ideal framework for measuring what matters to a range of stakeholders interested in improving care for this population. Measurements: Measurement focuses on the individual patient and family experience as the fundamental outcome of interest around which all care delivery is organized. Results: We describe an approach to codesigning and implementing a palliative care registry that functions as a learning health system, by combining patient and family inputs and clinical data to support person-centered care, quality improvement, accountability, transparency, and scientific research. Discussion: The potential for a palliative care learning health system that, by design, brings together enriched information environments to support coproduction of healthcare and facilitated peer networks to support patients and families, collaborative clinician networks to support palliative care program improvement, and collaboratories to support research and the application of research to benefit individual patients is immense. PMID:29091509

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.

Adiabatic quantum optimization for associative memory recall

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

Seddiqi, Hadayat; Humble, Travis S.

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

Adiabatic Quantum Optimization for Associative Memory Recall

NASA Astrophysics Data System (ADS)

Seddiqi, Hadayat; Humble, Travis

2014-12-01

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

Adiabatic quantum optimization for associative memory recall

DOE PAGES

Seddiqi, Hadayat; Humble, Travis S.

2014-12-22

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

Summary Report of the Title VI-G Network Third Annual Conference. (Minneapolis, Minnesota, December 5-8, 1977).

ERIC Educational Resources Information Center

Network of Innovative Schools, Inc., Andover, MA.

Presented is the summary report of the Third Annual Conference of the Title VI-G Network which was designed to update learning disability (LD) professionals on pertinent program and education developments, to inform them of future trends and plans, and to offer concrete help in their present work. Introductory sections provide a schedule of the…

Comment on 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study'.

PubMed

Valdes, Gilmer; Interian, Yannet

2018-03-15

The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study' with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.

Teaching a Foreign Language to Deaf People via Vodcasting & Web 2.0 Tools

NASA Astrophysics Data System (ADS)

Drigas, Athanasios; Vrettaros, John; Tagoulis, Alexandors; Kouremenos, Dimitris

This paper presents the design and development of an e-learning course in teaching deaf people in a foreign language, whose first language is the sign language. The course is based in e-material, vodcasting and web 2.0 tools such as social networking and blog The course has been designed especially for deaf people and it is exploring the possibilities that e-learning material vodcasting and web 2.0 tools can offer to enhance the learning process and achieve more effective learning results.

Virtual worlds: a new frontier for nurse education?

PubMed

Green, Janet; Wyllie, Aileen; Jackson, Debra

2014-01-01

Virtual worlds have the potential to offer nursing students social networking and, learning, opportunities through the use of collaborative and immersive learning. If nursing educators, are to stay, abreast of contemporary learning opportunities an exploration of the potential benefits of, virtual, worlds and their possibilities is needed. Literature was sourced that explored virtual worlds, and their, use in education, but nursing education specifically. It is clear that immersive learning has, positive, benefits for nursing, however the best way to approach virtual reality in nursing education, has yet to, be ascertained.

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Neural constraints on learning.

PubMed

Sadtler, Patrick T; Quick, Kristin M; Golub, Matthew D; Chase, Steven M; Ryu, Stephen I; Tyler-Kabara, Elizabeth C; Yu, Byron M; Batista, Aaron P

2014-08-28

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

Social learning within a community of practice: Investigating interactions about evaluation among zoo education professionals.

PubMed

Khalil, Kathayoon; Ardoin, Nicole M; Wojcik, Deborah

2017-04-01

The accessibility and ubiquity of zoos and aquariums-which reach over 700 million people worldwide annually-make them critical sites for science and environmental learning. Through educational offerings, these sites can generate excitement and curiosity about nature and motivate stewardship behavior, but only if their programs are high quality and meet the needs of their audiences. Evaluation is, therefore, critical: knowing what works, for whom, and under what conditions must be central to these organizations. Yet, many zoo and aquarium educators find evaluation to be daunting, and they are challenged to implement evaluations and/or use the findings iteratively in program development and improvement. This article examines how zoo education professionals engage with one another in a learning community related to evaluation. We use a communities of practice lens and social network analysis to understand the structure of this networked learning community, considering changes over time. Our findings suggest that individuals' roles in a networked learning community are influenced by factors such as communicative convenience and one's perceptions of others' evaluation expertise, which also contribute to forming and sustaining professional relationships. This study illuminates how project-based professional networks can become communities of practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Integration of the Internet into medical education].

PubMed

Taradi, Suncana Kukolja

2002-01-01

The Internet promises dramatic changes in the way we learn and teach, the way we interact as a society. Networked technologies introduce interactivity and multimedia into the educational process. The student of the 21st century will use his/her PC as a learning station, as a tutoring system, as an information provider and as a communication center. Therefore the passive classroom (teacher-centered teaching) will evolve into active studio learning (student-centered learning). This will be achieved by new teaching techniques and standards of quality. The role of the new generation of educators is to create exploratory learning environments that offer a wide range of views on many subject areas and encourage active lifelong learning. This will be achieved by 1) placing courseware on the web where it can be accessed by remote students and by 2) finding and reviewing teaching materials obtained from www for possible integration into the local lecture material. The paper suggests strategies for introducing medical educators to networked teaching.

Building Your Undergraduate Physics Career

NASA Astrophysics Data System (ADS)

2014-03-01

In this interactive event for undergraduates, students will learn important lessons about career preparation, including skill building, networking, and developing ``soft skills.'' Our expert panel of working physicists will be on hand to answer questions, offer advice, and share their stories. Light refreshments will be served.

Deep learning in color: towards automated quark/gluon jet discrimination

DOE PAGES

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

2017-01-25

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, themore » deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.« less

Deep learning in color: towards automated quark/gluon jet discrimination

NASA Astrophysics Data System (ADS)

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

2017-01-01

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding.

PubMed

Li, Yujie; Chen, Hanbo; Jiang, Xi; Li, Xiang; Lv, Jinglei; Peng, Hanchuan; Tsien, Joe Z; Liu, Tianming

2017-12-01

Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways, and/or anatomical regions.

Deep learning in color: towards automated quark/gluon jet discrimination

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

Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. Here, to establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, themore » deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.« less

Is There a Role for Social Networking Sites in Education?

NASA Astrophysics Data System (ADS)

Santos, Ieda M.; Hammond, Michael; Durli, Zenilde; Chou, Shiao-Yuh

Social networking sites such as Facebook and MySpace have become popular among millions of users including students of all ages. There are ongoing discussions over the potential of these sites to support teaching and learning, particularly to complement traditional or online classroom activities. This paper explores whether social networking have a place in teaching and learning by investigating how students use these sites and whether they find opportunities to discuss study related activities with their peers. Two small scale studies were carried out in a face-to-face undergraduate course in Singapore and students enrolled in a face-to-face Master’s programme in Brazil. Data were collected using surveys and interviews; findings were mixed. Many of the Brazilian students used social networking sites to both socialize and discuss their studies while the Singaporean students used such sites for social interactions only. The paper discusses these differences and offers suggestions for further research.

Predicting the dissolution kinetics of silicate glasses using machine learning

NASA Astrophysics Data System (ADS)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Coding Classroom Interactions for Collective and Individual Engagement

ERIC Educational Resources Information Center

Ryu, Suna; Lombardi, Doug

2015-01-01

This article characterizes "engagement in science learning" from a sociocultural perspective and offers a mixed method approach to measuring engagement that combines critical discourse analysis (CDA) and social network analysis (SNA). Conceptualizing engagement from a sociocultural perspective, the article discusses the advantages of a…

A knowledge-based system with learning for computer communication network design

NASA Technical Reports Server (NTRS)

Pierre, Samuel; Hoang, Hai Hoc; Tropper-Hausen, Evelyne

1990-01-01

Computer communication network design is well-known as complex and hard. For that reason, the most effective methods used to solve it are heuristic. Weaknesses of these techniques are listed and a new approach based on artificial intelligence for solving this problem is presented. This approach is particularly recommended for large packet switched communication networks, in the sense that it permits a high degree of reliability and offers a very flexible environment dealing with many relevant design parameters such as link cost, link capacity, and message delay.

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

PubMed

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Comment on ‘Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study’

NASA Astrophysics Data System (ADS)

Valdes, Gilmer; Interian, Yannet

2018-03-01

The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read ‘Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study’ with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.

Graduate Inquiry: Social Capital in Online Courses

ERIC Educational Resources Information Center

Mays, Thomas

2016-01-01

As colleges and universities increase their online course offerings, student social experiences in online learning environments require further examination, specifically for nonresidential students who may already be less integrated into college social networks. A social capital framework was used to guide this qualitative study of 17…

After High School: The Status of Youth with Emotional and Behavioral Disorders.

ERIC Educational Resources Information Center

Walker, Rhonda; Bunsen, Teresa D.

1995-01-01

This literature review examined the current status of young adults with emotional/behavioral disorders (EBD) two to five years after leaving high school, in employment, residential environment, social and interpersonal networks, dependency, and learned helplessness. Suggestions for school-implemented community adjustment programs are offered. (DB)

The Reality of Virtual Learning.

ERIC Educational Resources Information Center

Berman, Sheldon

1999-01-01

Through a $7.5 million U.S. Department of Education grant, students at the Hudson (Massachusetts) Public Schools "attend" Virtual High School--a network of 30 schools in 10 states. Kids attend classes any time, work collaboratively, and choose among innovative, timely, technologically rich course offerings. Other sites are described.…

The New Literacies: Multiple Perspectives on Research and Practice

ERIC Educational Resources Information Center

Baker, Elizabeth A., Ed.

2010-01-01

With contributions from leading scholars, this compelling volume offers fresh insights into literacy teaching and learning--and the changing nature of literacy itself--in today's K-12 classrooms. The focus is on varied technologies and literacies such as social networking sites, text messaging, and online communities. Cutting-edge approaches to…

Report to the Governor and Legislature, June 30, 2009

ERIC Educational Resources Information Center

Hyde, Elizabeth M.

2009-01-01

This report offers an update on the work of the Department of Early Learning (DEL). This report contains sections that respond specifically to information requested in the 2008 supplemental operating budget regarding: (1) Washington State Child Care Resource & Referral Network; and (2) Washington State Training and Registry System (STARS). The…

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

PubMed

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

2016-01-01

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

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

PubMed Central

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

2016-01-01

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

Adaptive Leadership in Times of Crisis

DTIC Science & Technology

2011-12-01

HAyASHI AnD AMEy Soo Chiemi Hayashi is Research Director of the Risk Response Network at the World Economic Forum . Amey Soo is a Researcher at the...and the resulting complexities of this world. after each crisis it must be the goal of author- ities to learn from the lessons that emerged and...digital media, which offers essential tools that anyone aspiring to lead in the 21st century must master. Learning from its past mistakes, the

Deep Learning for Image-Based Cassava Disease Detection.

PubMed

Ramcharan, Amanda; Baranowski, Kelsee; McCloskey, Peter; Ahmed, Babuali; Legg, James; Hughes, David P

2017-01-01

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

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

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

PubMed

Chen, C L Philip; Liu, Zhulin

2018-01-01

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

A Retrospective Look at 20 Years of ASM Education Programs (1990-2010) and a Prospective Look at the Next 20 Years (2011-2030).

PubMed

Chang, Amy

2011-01-01

Professional societies provide visibility and legitimacy to the work of their post secondary educator members, advocate best practices in courses and sponsored student research, and establish deep networks and communities that catalyze members to collectively engage in undergraduate teaching and learning scholarship. Within the American Society for Microbiology (ASM), the Education Board, established in the mid-1970s, assumes this role. I have been fortunate enough to watch several pivotal programs support our growth and change the status quo by providing opportunities for biology educators to flourish. In this retrospective review, the background and details I offer about each initiative help explain ASM Education offerings, how our growth has been supported and how the status quo has changed. In this prospective look, I offer my vision of the future in post secondary education where classroom learning is student-centered and focused on global problems affecting our health and environment. For the profession to proliferate, the ASM must provide members as many opportunities in learning biology as they do with advancing biology to new frontiers.

Electronic Resource Sharing in Community Colleges: A Snapshot of Florida, Wisconsin, Texas, and Louisiana.

ERIC Educational Resources Information Center

Mahoney, Brian D.

2000-01-01

States that several states are establishing networks for resource sharing. Florida offers these resources through the Florida Distance Learning Library Initiative, Wisconsin has BadgerLink and WISCAT, TexShare provides library resource sharing in Texas, and Louisiana has LOUIS and LLN. These are some of the states successfully demonstrating…

A Resource Center for the Stimulation of Post Secondary Education Innovation via Computer Network.

ERIC Educational Resources Information Center

Savin, William

The goal of the project described here was to improve the quality of postsecondary education by offering institutions of higher learning information on currently funded educational projects through an interactive database, the Educational Resources Directory (ERD), which contains information on new methods, curricula, and educational technology.…

Quality through Networking--From Reactive Administration to Proactive Cooperation

ERIC Educational Resources Information Center

Karttu, Petri; Kiilunen, Liisa; Laulajainen, Tiina

2012-01-01

The Language Centre functions as an independent institute within the University of Helsinki. It provides services to all the faculties and units of the university by offering language services and doing its share in creating an international learning environment. Together, the Language Centre and the faculties have one goal in common, namely, a…

Existing and Emerging Technologies in Education: A Descriptive Overview. CREATE Monograph Series.

ERIC Educational Resources Information Center

Bakke, Thomas W.

Second in a series of six monographs on the use of new technologies in the instruction of learning disabled students, the paper offers a descriptive overview of new technologies. Topics addressed include the following: (1) techniques for sharing computer resources (including aspects of networking, sharing information through databases, and the use…

Deep Learning in Medical Imaging: General Overview

PubMed Central

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

Deep Learning in Medical Imaging: General Overview.

PubMed

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

2017-01-01

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice.

PubMed

Centeno, Tonatiuh Pena; Shomroni, Orr; Hennion, Magali; Halder, Rashi; Vidal, Ramon; Rahman, Raza-Ur; Bonn, Stefan

2016-10-11

Recent evidence suggests that the formation and maintenance of memory requires epigenetic changes. In an effort to understand the spatio-temporal extent of learning and memory-related epigenetic changes we have charted genome-wide histone and DNA methylation profiles, in two different brain regions, two cell types, and three time-points, before and after learning. In this data descriptor we provide detailed information on data generation, give insights into the rationale of experiments, highlight necessary steps to assess data quality, offer guidelines for future use of the data and supply ready-to-use code to replicate the analysis results. The data provides a blueprint of the gene regulatory network underlying short- and long-term memory formation and maintenance. This 'healthy' gene regulatory network of learning can now be compared to changes in neurological or psychiatric diseases, providing mechanistic insights into brain disorders and highlighting potential therapeutic avenues.

Validating module network learning algorithms using simulated data.

PubMed

Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves

2007-05-03

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.

Neurobiological Basis of Language Learning Difficulties.

PubMed

Krishnan, Saloni; Watkins, Kate E; Bishop, Dorothy V M

2016-09-01

In this paper we highlight why there is a need to examine subcortical learning systems in children with language impairment and dyslexia, rather than focusing solely on cortical areas relevant for language. First, behavioural studies find that children with these neurodevelopmental disorders perform less well than peers on procedural learning tasks that depend on corticostriatal learning circuits. Second, fMRI studies in neurotypical adults implicate corticostriatal and hippocampal systems in language learning. Finally, structural and functional abnormalities are seen in the striatum in children with language disorders. Studying corticostriatal networks in developmental language disorders could offer us insights into their neurobiological basis and elucidate possible modes of compensation for intervention. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2018-02-06

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

American Government: An Introduction Using MicroCase with Distance Learners.

ERIC Educational Resources Information Center

Rosberg, William H.

In spring 1997, a project used MicroCase, a computer-based statistical analysis and data retrieval system, to offer an American Government class to distance students at Iowa's Kirkwood Community College (KCC). Students participated in the course at one of the college's 10 learning centers via a statewide fiber-optic network. MicroCase provided the…

Just-in-Time Online Professional Development Activities for an Innovation in Small Rural Schools

ERIC Educational Resources Information Center

Hamel, Christine; Allaire, Stephane; Turcotte, Sandrine

2012-01-01

This article describes the just-in-time online professional development offered to teachers in the Remote Networked Schools (RNS), a systemic initiative funded by the Quebec Ministry of Education (Canada), which aims at enriching the learning environment of small rural schools with the use of information and communication technologies (ICTs). The…

Exploring the use of grounded theory as a methodological approach to examine the 'black box' of network leadership in the national quality forum.

PubMed

Hoflund, A Bryce

2013-01-01

This paper describes how grounded theory was used to investigate the "black box" of network leadership in the creation of the National Quality Forum. Scholars are beginning to recognize the importance of network organizations and are in the embryonic stages of collecting and analyzing data about network leadership processes. Grounded theory, with its focus on deriving theory from empirical data, offers researchers a distinctive way of studying little-known phenomena and is therefore well suited to exploring network leadership processes. Specifically, this paper provides an overview of grounded theory, a discussion of the appropriateness of grounded theory to investigating network phenomena, a description of how the research was conducted, and a discussion of the limitations and lessons learned from using this approach.

Pencil-and-Paper Neural Networks: An Undergraduate Laboratory Exercise in Computational Neuroscience

PubMed Central

Crisp, Kevin M.; Sutter, Ellen N.; Westerberg, Jacob A.

2015-01-01

Although it has been more than 70 years since McCulloch and Pitts published their seminal work on artificial neural networks, such models remain primarily in the domain of computer science departments in undergraduate education. This is unfortunate, as simple network models offer undergraduate students a much-needed bridge between cellular neurobiology and processes governing thought and behavior. Here, we present a very simple laboratory exercise in which students constructed, trained and tested artificial neural networks by hand on paper. They explored a variety of concepts, including pattern recognition, pattern completion, noise elimination and stimulus ambiguity. Learning gains were evident in changes in the use of language when writing about information processing in the brain. PMID:26557791

Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

PubMed Central

Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

2016-01-01

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

Feasibility of Using Distributed Wireless Mesh Networks for Medical Emergency Response

PubMed Central

Braunstein, Brian; Trimble, Troy; Mishra, Rajesh; Manoj, B. S.; Rao, Ramesh; Lenert, Leslie

2006-01-01

Achieving reliable, efficient data communications networks at a disaster site is a difficult task. Network paradigms, such as Wireless Mesh Network (WMN) architectures, form one exemplar for providing high-bandwidth, scalable data communication for medical emergency response activity. WMNs are created by self-organized wireless nodes that use multi-hop wireless relaying for data transfer. In this paper, we describe our experience using a mesh network architecture we developed for homeland security and medical emergency applications. We briefly discuss the architecture and present the traffic behavioral observations made by a client-server medical emergency application tested during a large-scale homeland security drill. We present our traffic measurements, describe lessons learned, and offer functional requirements (based on field testing) for practical 802.11 mesh medical emergency response networks. With certain caveats, the results suggest that 802.11 mesh networks are feasible and scalable systems for field communications in disaster settings. PMID:17238308

The Next Era: Deep Learning in Pharmaceutical Research.

PubMed

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

MATE: Machine Learning for Adaptive Calibration Template Detection

PubMed Central

Donné, Simon; De Vylder, Jonas; Goossens, Bart; Philips, Wilfried

2016-01-01

The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves—typically in the form of chessboard corners—need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups. PMID:27827920

NASA’s Universe of Learning: Connecting Scientists, Educators, and Learners

NASA Astrophysics Data System (ADS)

Smith, Denise A.; Lestition, Kathleen; Squires, Gordon K.; Greene, W. M.; Biferno, Anya A.; Cominsky, Lynn R.; Goodman, Irene; Walker, Allyson; Universe of Learning Team

2017-01-01

NASA’s Universe of Learning (UoL) is one of 27 competitively awarded education programs selected by NASA’s Science Mission Directorate (SMD) in its newly restructured education effort. Through these 27 programs, SMD aims to infuse NASA science experts and content more effectively and efficiently into learning environments serving audiences of all ages. UoL is a unique partnership between the Space Telescope Science Institute, Chandra X-ray Center, IPAC at Caltech, Jet Propulsion Laboratory Exoplanet Exploration Program, and Sonoma State University that will connect the scientists, engineers, science, technology and adventure of NASA Astrophysics with audience needs, proven infrastructure, and a network of partners to advance SMD education objectives. External evaluation is provided through a partnership with Goodman Research Group and Cornerstone Evaluation Associates. The multi-institutional team is working to develop and deliver a unified, consolidated and externally evaluated suite of education products, programs, and professional development offerings that spans the full spectrum of NASA Astrophysics, including the Cosmic Origins, Physics of the Cosmos, and Exoplanet Exploration themes. Products and programs focus on out-of-school-time learning environments and include enabling educational use of Astrophysics mission data and offering participatory experiences; creating multimedia and immersive experiences; designing exhibits and community programs; and producing resources for special needs and underserved/underrepresented audiences. The UoL team also works with a network of partners to provide professional learning experiences for informal educators, pre-service educators, and undergraduate instructors. This presentation will provide an overview of the UoL team’s approach to partnering scientists and educators to engage learners in Astrophysics discoveries and data; progress to date; and pathways for science community involvement.

A novel neural-wavelet approach for process diagnostics and complex system modeling

NASA Astrophysics Data System (ADS)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

Hierarchically clustered adaptive quantization CMAC and its learning convergence.

PubMed

Teddy, S D; Lai, E M K; Quek, C

2007-11-01

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization.

Mission Possible: How the Secrets of the Success Academies Can Work in Any School

ERIC Educational Resources Information Center

Moskowitz, Eva; Lavinia, Arin

2012-01-01

Eva Moskowitz (the founder and CEO of the Success Charter Network in Harlem) and Arin Lavinia offer practical, classroom-tested ideas for dramatically improving teaching and learning. Moskowitz and Lavinia reveal how a charter school in the middle of Harlem, enrolling neighborhood children selected at random, emerged as one of the top schools in…

The Influence of Collective Asynchronous Discourse Elaborated Online by Pre-Service Teachers on Their Educational Interventions in the Classroom

ERIC Educational Resources Information Center

Allaire, Stéphane

2015-01-01

Networked learning communities are growing and they offer new opportunities for reflection on practice in education. Many authors have studied the processes followed and the contents produced by such communities. On the other hand, few have observed how collective asynchronous discourse can be enacted in the classroom. This objective was pursued…

Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

DTIC Science & Technology

2015-03-26

7 VRT Vulnerability Research Team...and the Talos (formerly the Vulnerability Research Team ( VRT )) [7] 7 ruleset libraries are the two leading rulesets in use. Both libraries offer paid...rule sets to load for the signature-based IDS. Snort is selected as the IDS engine using the “ VRT and ET No/GPL” rule set. The total rule count in the

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Still searching for the engram.

PubMed

Eichenbaum, Howard

2016-09-01

For nearly a century, neurobiologists have searched for the engram-the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories.

Network analysis of physics discussion forums and links to course success

NASA Astrophysics Data System (ADS)

Traxler, Adrienne; Gavrin, Andrew; Lindell, Rebecca

2017-01-01

Large introductory science courses tend to isolate students, with negative consequences for long-term retention in college. Many active learning courses build collaboration and community among students as an explicit goal, and social network analysis has been used to track the development and beneficial effects of these collaborations. Here we supplement such work by conducting network analysis of online course discussion forums in two semesters of an introductory physics class. Online forums provide a tool for engaging students with each other outside of class, and offer new opportunities to commuter or non-traditional students with limited on-campus time. We look for correlations between position in the forum network (centrality) and final course grades. Preliminary investigation has shown weak correlations in the very dense full-semester network, so we will consider reduced ''backbone'' networks that highlight the most consistent links between students. Future work and implications for instruction will also be discussed.

Team Learning: New Insights Through a Temporal Lens.

PubMed

Lehmann-Willenbrock, Nale

2017-04-01

Team learning is a complex social phenomenon that develops and changes over time. Hence, to promote understanding of the fine-grained dynamics of team learning, research should account for the temporal patterns of team learning behavior. Taking important steps in this direction, this special issue offers novel insights into the dynamics of team learning by advocating a temporal perspective. Based on a symposium presented at the 2016 Interdisciplinary Network for Group Research (INGRoup) Conference in Helsinki, the four empirical articles in this special issue showcase four different and innovative approaches to implementing a temporal perspective in team learning research. Specifically, the contributions highlight team learning dynamics in student teams, self-managing teams, teacher teams, and command and control teams. The articles cover a broad range of methods and designs, including both qualitative and quantitative methodologies, and longitudinal as well as micro-temporal approaches. The contributors represent four countries and five different disciplines in group research.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

NASA Astrophysics Data System (ADS)

Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-01

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses.

PubMed

Lin, Yu-Pu; Bennett, Christopher H; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

2016-09-07

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Virtual patients: practical advice for clinical authors using Labyrinth.

PubMed

Begg, Michael

2010-09-01

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

Launching Light: Beyond the Bulb for the United Nations' International Year of Light 2015

NASA Astrophysics Data System (ADS)

Arcand, K. K.; Watzke, M.

2015-09-01

In astronomy, light is the language used to understand the Universe. From radio waves to gamma rays, light in all its forms delivers information that helps astronomers learn about the Universe. When the United Nations declared 2015 to be the International Year of Light and Light-based Technologies (IYL2015), it presented an opportunity to share the role that light plays in astronomy and beyond. The IYL2015 also offered a chance to build on experiences and sustain networks from the International Year of Astronomy in 2009. Light: Beyond the Bulb is an IYL2015 project that melds both of these goals. The project takes the form of an exhibit that showcases what light can do, from here on Earth and across the vastness of space, hosted by volunteer networks in public spaces for informal science learning.

Health 2.0-Lessons Learned: Social Networking With Patients for Health Promotion.

PubMed

Sharma, Suparna; Kilian, Reena; Leung, Fok-Han

2014-07-01

The advent of social networking as a major platform for human interaction has introduced a new dimension into the physician-patient relationship, known as Health 2.0. The concept of Health 2.0 is young and evolving; so far, it has meant the use of social media by health professionals and patients to personalize health care and promote health education. Social networking sites like Facebook and Twitter offer promising platforms for health care providers to engage patients. Despite the vast potential of Health 2.0, usage by health providers remains relatively low. Using a pilot study as an example, this commentary reviews the ways in which physicians can effectively harness the power of social networking to meaningfully engage their patients in primary prevention. © The Author(s) 2014.

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

The Next Era: Deep Learning in Pharmaceutical Research

PubMed Central

Ekins, Sean

2016-01-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique. PMID:27599991

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

PubMed

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

2018-02-01

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

Building online learning communities in a graduate dental hygiene program.

PubMed

Rogo, Ellen J; Portillo, Karen M

2014-08-01

The literature abounds with research related to building online communities in a single course; however, limited evidence is available on this phenomenon from a program perspective. The intent of this qualitative case study inquiry was to explore student experiences in a graduate dental hygiene program contributing or impeding the development and sustainability of online learning communities. Approval from the IRB was received. A purposive sampling technique was used to recruit participants from a stratification of students and graduates. A total of 17 participants completed semi-structured interviews. Data analysis was completed through 2 rounds - 1 for coding responses and 1 to construct categories of experiences. The participants' collective definition of an online learning community was a complex synergistic network of interconnected people who create positive energy. The findings indicated the development of this network began during the program orientation and was beneficial for building a foundation for the community. Students felt socially connected and supported by the network. Course design was another important category for participation in weekly discussions and group activities. Instructors were viewed as active participants in the community, offering helpful feedback and being a facilitator in discussions. Experiences impeding the development of online learning communities related to the poor performance of peers and instructors. Specific categories of experiences supported and impeded the development of online learning communities related to the program itself, course design, students and faculty. These factors are important to consider in order to maximize student learning potential in this environment. Copyright © 2014 The American Dental Hygienists’ Association.

Literacy Networks: Following the Circulation of Texts, Bodies, and Objects in the Schooling and Online Gaming of One Youth

ERIC Educational Resources Information Center

Leander, Kevin M.; Lovvorn, Jason F.

2006-01-01

In this article, we offer an approach to conceiving of the relation between literacy practices and space-time. Literacy, embedded in other forms of activity, has a unique role in producing and organizing space-time relations, and such relations provide for different forms of cognition and learning. Closely examining how literacy practices produce…

Climate change education in informal settings: Using boundary objects to frame network dissemination

NASA Astrophysics Data System (ADS)

Steiner, Mary Ann

This study of climate change education dissemination takes place in the context of a larger project where institutions in four cities worked together to develop a linked set of informal learning experiences about climate change. Each city developed an organizational network to explore new ways to connect urban audiences with climate change education. The four city-specific networks shared tools, resources, and knowledge with each other. The networks were related in mission and goals, but were structured and functioned differently depending on the city context. This study illustrates how the tools, resources, and knowledge developed in one network were shared with networks in two additional cities. Boundary crossing theory frames the study to describe the role of objects and processes in sharing between networks. Findings suggest that the goals, capacity and composition of networks resulted in a different emphasis in dissemination efforts, in one case to push the approach out to partners for their own work and in the other to pull partners into a more collaborative stance. Learning experiences developed in each city as a result of the dissemination reflected these differences in the city-specific emphasis with the push city diving into messy examples of the approach to make their own examples, and the pull city offering polished experiences to partners in order to build confidence in the climate change messaging. The networks themselves underwent different kinds of growth and change as a result of dissemination. The emphasis on push and use of messy examples resulted in active use of the principles of the approach and the pull emphasis with polished examples resulted in the cultivation of partnerships with the hub and the potential to engage in the educational approach. These findings have implications for boundary object theory as a useful grounding for dissemination designs in the context of networks of informal learning organizations to support a shift in communication approach, particularly when developing interventions for wicked socio-scientific issues such as climate change.

Neural networks within multi-core optic fibers

PubMed Central

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-01-01

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. PMID:27383911

Neural networks within multi-core optic fibers.

PubMed

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

Surgical education and training in an outer metropolitan hospital: a qualitative study of surgical trainers and trainees.

PubMed

Nestel, Debra; Harlim, Jennifer; Bryant, Melanie; Rampersad, Rajay; Hunter-Smith, David; Spychal, Bob

2017-08-01

The landscape of surgical training is changing. The anticipated increase in the numbers of surgical trainees and the shift to competency-based surgical training places pressures on an already stretched health service. With these pressures in mind, we explored trainers' and trainees' experiences of surgical training in a less traditional rotation, an outer metropolitan hospital. We considered practice-based learning theories to make meaning of surgical training in this setting, in particular Actor-network theory. We adopted a qualitative approach and purposively sampled surgical trainers and trainees to participate in individual interviews and focus groups respectively. Transcripts were made and thematically analysed. Institutional human research ethics approval was obtained. Four surgical trainers and fourteen trainees participated. Almost without exception, participants' report training needs to be well met. Emergent inter-related themes were: learning as social activity; learning and programmatic factors; learning and physical infrastructure; and, learning and organizational structure. This outer metropolitan hospital is suited to the provision of surgical training with the current rotational system for trainees. The setting offers experiences that enable consolidation of learning providing a rich and varied overall surgical training program. Although relational elements of learning were paramount they occurred within a complex environment. Actor-network theory was used to give meaning to emergent themes acknowledging that actors (both people and objects) and their interactions combine to influence training quality, shifting the focus of responsibility for learning away from individuals to the complex interactions in which they work and learn.

Still searching for the engram

PubMed Central

Eichenbaum, Howard

2016-01-01

For nearly a century neurobiologists have searched for the engram - the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories. PMID:26944423

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

Building an undergraduate physics program with Learning Assistants

NASA Astrophysics Data System (ADS)

Price, Edward

2013-04-01

In 2007, the CSUSM Physics Department began offering a B.S. in Applied Physics, its first physics bachelors degree program. The program has grown from 11 majors in 2008 to over 80 in 2012, due in part to recruiting students from local high schools and community colleges. More broadly, because most CSUSM students come from the local region, the longer-term health of the Department is coupled with the vitality and strength of local high school physics education. In addition, establishing a new physics degree required curriculum development and offered the opportunity to incorporate recent innovations in physics education when developing courses. A Learning Assistants (LA) Program, established by the Department in 2008, has been a critical component in these efforts to recruit students, build local educational networks, and implement innovative curricula. In an LA Program, undergraduate Learning Assistants assist faculty in class, meet regularly with the course instructor, and participate in a weekly seminar on teaching and learning, which provides guidance on effective instruction and an opportunity to reflect on their experiences in the classroom. The LA program promotes course transformation, improved student learning, and teacher recruitment. This talk will describe the CSUSM LA Program and its role in support of our growing applied physics degree program.

Evolving neural networks through augmenting topologies.

PubMed

Stanley, Kenneth O; Miikkulainen, Risto

2002-01-01

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning.

PubMed

Korcsmaros, Tamas; Dunai, Zsuzsanna A; Vellai, Tibor; Csermely, Peter

2013-09-01

The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

How do German veterinarians use social networks? A study, using the example of the 'NOVICE' veterinary medicine network.

PubMed

Schaper, Elisabeth; Forrest, Neil D; Tipold, Andrea; Ehlers, Jan P

2013-01-01

NOVICE (Network Of Veterinary ICT in Education, http://www.noviceproject.eu/), is a professional online social network for veterinarians, lecturers and students of veterinary medicine as well as for e-Learning advisers and others working in establishments that teach veterinary medicine. This study sets out to investigate to what extent German veterinarians, lecturers, students of veterinary medicine and e-Learning representatives would accept a specialist network, what requirements would have to be met by an online social network, how to use web 2.0 tools [21], [30] and what advantages a specialist network could offer. The investigation was carried out by analysing data from the Elgg platform database as well as using Google Analytics. Annual focus group surveys and individual interviews were carried out in order to perform an analysis of acceptance among network users. 1961 users from 73 different countries registered on the NOVICE site between 1 September 2010 and 21 March 2012. Germany represents the biggest user group, with 565 users (28.81%). During this period, most individual hits on the website came from Germany too. In total, 24.83% of all members are active, while 19.22% of German members participate actively. In terms of gender, there are significantly more female members than male members, both in the NOVICE network as a whole as well as in Germany. The most used web 2.0 tools are chat and email messaging services as well as writing wikis and contributing to forum discussions. The focus group surveys showed that respondents generally make use of other online communities too. Active members generally use more web 2.0 tools than in other networks, while passive members are generally more reluctant in all networks. All participants of the survey welcomed the idea of having a network specifically set up for the profession and believe that it could be very useful for veterinary medicine. The network and its membership figures developed very positively during the assessed time period. Until now, the focus of the content of contributions in NOVICE (Network of Veterinary ICT in Education) has been on veterinary medicine teaching supported by e-Learning. An increase in the number of members would, however, be beneficial in order to further develop the network so that valuable exchange of information and informal learning can also take place in other specialist areas of veterinary medicine.

How do German veterinarians use social networks? A study, using the example of the 'NOVICE' veterinary medicine network

PubMed Central

Schaper, Elisabeth; Forrest, Neil D.; Tipold, Andrea; Ehlers, Jan P.

2013-01-01

Objective: NOVICE (Network Of Veterinary ICT in Education, http://www.noviceproject.eu/), is a professional online social network for veterinarians, lecturers and students of veterinary medicine as well as for e-Learning advisers and others working in establishments that teach veterinary medicine. This study sets out to investigate to what extent German veterinarians, lecturers, students of veterinary medicine and e-Learning representatives would accept a specialist network, what requirements would have to be met by an online social network, how to use web 2.0 tools [21], [30] and what advantages a specialist network could offer. Methodology: The investigation was carried out by analysing data from the Elgg platform database as well as using Google Analytics. Annual focus group surveys and individual interviews were carried out in order to perform an analysis of acceptance among network users. Results: 1961 users from 73 different countries registered on the NOVICE site between 1 September 2010 and 21 March 2012. Germany represents the biggest user group, with 565 users (28.81%). During this period, most individual hits on the website came from Germany too. In total, 24.83% of all members are active, while 19.22% of German members participate actively. In terms of gender, there are significantly more female members than male members, both in the NOVICE network as a whole as well as in Germany. The most used web 2.0 tools are chat and email messaging services as well as writing wikis and contributing to forum discussions. The focus group surveys showed that respondents generally make use of other online communities too. Active members generally use more web 2.0 tools than in other networks, while passive members are generally more reluctant in all networks. All participants of the survey welcomed the idea of having a network specifically set up for the profession and believe that it could be very useful for veterinary medicine. Conclusions: The network and its membership figures developed very positively during the assessed time period. Until now, the focus of the content of contributions in NOVICE (Network of Veterinary ICT in Education) has been on veterinary medicine teaching supported by e-Learning. An increase in the number of members would, however, be beneficial in order to further develop the network so that valuable exchange of information and informal learning can also take place in other specialist areas of veterinary medicine. PMID:23467464

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.

PubMed

Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

2016-08-11

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

PubMed Central

Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

2016-01-01

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. PMID:27529225

One Family's Journey: Medical Home and the Network of Supports It Offers Children and Youth with Special Health Care Needs--Parent Partners

ERIC Educational Resources Information Center

Townsend, Sabra; Pola-Money, Gina; Gatto, Molly

2007-01-01

In this 12 installment Medical Home series, "EP" will present a case study about the American Academy of Pediatrics' Medical Home Initiative. A "Medical Home" is not a building. It is an approach to providing healthcare services to children with special healthcare needs. This article presents Part 3 in the series as readers learn more about Amita…

Waiting in the wings: what can we learn about gene co-option from the diversification of butterfly wing patterns?

PubMed

Jiggins, Chris D; Wallbank, Richard W R; Hanly, Joseph J

2017-02-05

A major challenge is to understand how conserved gene regulatory networks control the wonderful diversity of form that we see among animals and plants. Butterfly wing patterns are an excellent example of this diversity. Butterfly wings form as imaginal discs in the caterpillar and are constructed by a gene regulatory network, much of which is conserved across the holometabolous insects. Recent work in Heliconius butterflies takes advantage of genomic approaches and offers insights into how the diversification of wing patterns is overlaid onto this conserved network. WntA is a patterning morphogen that alters spatial information in the wing. Optix is a transcription factor that acts later in development to paint specific wing regions red. Both of these loci fit the paradigm of conserved protein-coding loci with diverse regulatory elements and developmental roles that have taken on novel derived functions in patterning wings. These discoveries offer insights into the 'Nymphalid Ground Plan', which offers a unifying hypothesis for pattern formation across nymphalid butterflies. These loci also represent 'hotspots' for morphological change that have been targeted repeatedly during evolution. Both convergent and divergent evolution of a great diversity of patterns is controlled by complex alleles at just a few genes. We suggest that evolutionary change has become focused on one or a few genetic loci for two reasons. First, pre-existing complex cis-regulatory loci that already interact with potentially relevant transcription factors are more likely to acquire novel functions in wing patterning. Second, the shape of wing regulatory networks may constrain evolutionary change to one or a few loci. Overall, genomic approaches that have identified wing patterning loci in these butterflies offer broad insight into how gene regulatory networks evolve to produce diversity.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'. © 2016 The Author(s).

Waiting in the wings: what can we learn about gene co-option from the diversification of butterfly wing patterns?

PubMed Central

Wallbank, Richard W. R.; Hanly, Joseph J.

2017-01-01

A major challenge is to understand how conserved gene regulatory networks control the wonderful diversity of form that we see among animals and plants. Butterfly wing patterns are an excellent example of this diversity. Butterfly wings form as imaginal discs in the caterpillar and are constructed by a gene regulatory network, much of which is conserved across the holometabolous insects. Recent work in Heliconius butterflies takes advantage of genomic approaches and offers insights into how the diversification of wing patterns is overlaid onto this conserved network. WntA is a patterning morphogen that alters spatial information in the wing. Optix is a transcription factor that acts later in development to paint specific wing regions red. Both of these loci fit the paradigm of conserved protein-coding loci with diverse regulatory elements and developmental roles that have taken on novel derived functions in patterning wings. These discoveries offer insights into the ‘Nymphalid Ground Plan’, which offers a unifying hypothesis for pattern formation across nymphalid butterflies. These loci also represent ‘hotspots’ for morphological change that have been targeted repeatedly during evolution. Both convergent and divergent evolution of a great diversity of patterns is controlled by complex alleles at just a few genes. We suggest that evolutionary change has become focused on one or a few genetic loci for two reasons. First, pre-existing complex cis-regulatory loci that already interact with potentially relevant transcription factors are more likely to acquire novel functions in wing patterning. Second, the shape of wing regulatory networks may constrain evolutionary change to one or a few loci. Overall, genomic approaches that have identified wing patterning loci in these butterflies offer broad insight into how gene regulatory networks evolve to produce diversity. This article is part of the themed issue ‘Evo-devo in the genomics era, and the origins of morphological diversity’. PMID:27994126

A broadband multimedia TeleLearning system

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

Wang, Ruiping; Karmouch, A.

1996-12-31

In this paper we discuss a broadband multimedia TeleLearning system under development in the Multimedia Information Research Laboratory at the University of Ottawa. The system aims at providing a seamless environment for TeleLearning using the latest telecommunication and multimedia information processing technology. It basically consists of a media production center, a courseware author site, a courseware database, a courseware user site, and an on-line facilitator site. All these components are distributed over an ATM network and work together to offer a multimedia interactive courseware service. An MHEG-based model is exploited in designing the system architecture to achieve the real-time, interactive,more » and reusable information interchange through heterogeneous platforms. The system architecture, courseware processing strategies, courseware document models are presented.« less

Developing Built Environment Programs in Local Health Departments: Lessons Learned From a Nationwide Mentoring Program

PubMed Central

Rube, Kate; Veatch, Maggie; Huang, Katy; Lent, Megan; Goldstein, Gail P.; Lee, Karen K.

2014-01-01

Local health departments (LHDs) have a key role to play in developing built environment policies and programs to encourage physical activity and combat obesity and related chronic diseases. However, information to guide LHDs’ effective engagement in this arena is lacking. During 2011–2012, the New York City Department of Health and Mental Hygiene (DOHMH) facilitated a built environment peer mentoring program for 14 LHDs nationwide. Program objectives included supporting LHDs in their efforts to achieve built environment goals, offering examples from DOHMH’s built environment work to guide LHDs, and building a healthy built environment learning network. We share lessons learned that can guide LHDs in developing successful healthy built environment agendas. PMID:24625166

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

PubMed Central

Fors, Uno; Tedre, Matti; Nouri, Jalal

2018-01-01

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

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

PubMed

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

2018-01-01

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

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Elastic network model of learned maintained contacts to predict protein motion

PubMed Central

Putz, Ines

2017-01-01

We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein’s contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a “deformation-invariant” contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase. PMID:28854238

Music Making as a Tool for Promoting Brain Plasticity across the Life Span

PubMed Central

Wan, Catherine Y.; Schlaug, Gottfried

2010-01-01

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging. PMID:20889966

Music making as a tool for promoting brain plasticity across the life span.

PubMed

Wan, Catherine Y; Schlaug, Gottfried

2010-10-01

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging.

Performance management of multiple access communication networks

NASA Astrophysics Data System (ADS)

Lee, Suk; Ray, Asok

1993-12-01

This paper focuses on conceptual design, development, and implementation of a performance management tool for computer communication networks to serve large-scale integrated systems. The objective is to improve the network performance in handling various types of messages by on-line adjustment of protocol parameters. The techniques of perturbation analysis of Discrete Event Dynamic Systems (DEDS), stochastic approximation (SA), and learning automata have been used in formulating the algorithm of performance management. The efficacy of the performance management tool has been demonstrated on a network testbed. The conceptual design presented in this paper offers a step forward to bridging the gap between management standards and users' demands for efficient network operations since most standards such as ISO (International Standards Organization) and IEEE address only the architecture, services, and interfaces for network management. The proposed concept of performance management can also be used as a general framework to assist design, operation, and management of various DEDS such as computer integrated manufacturing and battlefield C(sup 3) (Command, Control, and Communications).

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Outcomes of Interorganizational Networks in Canada for Chronic Disease Prevention: Insights From a Concept Mapping Study, 2015.

PubMed

Willis, Cameron; Kernoghan, Alison; Riley, Barbara; Popp, Janice; Best, Allan; Milward, H Brinton

2015-11-19

We conducted a mixed methods study from June 2014 to March 2015 to assess the perspectives of stakeholders in networks that adopt a population approach for chronic disease prevention (CDP). The purpose of the study was to identify important and feasible outcome measures for monitoring network performance. Participants from CDP networks in Canada completed an online concept mapping exercise, which was followed by interviews with network stakeholders to further understand the findings. Nine concepts were considered important outcomes of CDP networks: enhanced learning, improved use of resources, enhanced or increased relationships, improved collaborative action, network cohesion, improved system outcomes, improved population health outcomes, improved practice and policy planning, and improved intersectoral engagement. Three themes emerged from participant interviews related to measurement of the identified concepts: the methodological difficulties in measuring network outcomes, the dynamic nature of network evolution and function and implications for outcome assessment, and the challenge of measuring multisectoral engagement in CDP networks. Results from this study provide initial insights into concepts that can be used to describe the outcomes of networks for CDP and may offer foundations for strengthening network outcome-monitoring strategies and methodologies.

Outcomes of Interorganizational Networks in Canada for Chronic Disease Prevention: Insights From a Concept Mapping Study, 2015

PubMed Central

Kernoghan, Alison; Riley, Barbara; Popp, Janice; Best, Allan; Milward, H. Brinton

2015-01-01

Introduction We conducted a mixed methods study from June 2014 to March 2015 to assess the perspectives of stakeholders in networks that adopt a population approach for chronic disease prevention (CDP). The purpose of the study was to identify important and feasible outcome measures for monitoring network performance. Methods Participants from CDP networks in Canada completed an online concept mapping exercise, which was followed by interviews with network stakeholders to further understand the findings. Results Nine concepts were considered important outcomes of CDP networks: enhanced learning, improved use of resources, enhanced or increased relationships, improved collaborative action, network cohesion, improved system outcomes, improved population health outcomes, improved practice and policy planning, and improved intersectoral engagement. Three themes emerged from participant interviews related to measurement of the identified concepts: the methodological difficulties in measuring network outcomes, the dynamic nature of network evolution and function and implications for outcome assessment, and the challenge of measuring multisectoral engagement in CDP networks. Conclusion Results from this study provide initial insights into concepts that can be used to describe the outcomes of networks for CDP and may offer foundations for strengthening network outcome-monitoring strategies and methodologies. PMID:26583571

Capitalizing on Social Media for Career Development.

PubMed

Escoffery, Cam; Kenzig, Melissa; Hyden, Christel; Hernandez, Kristen

2018-01-01

Social media is powerful and has effective tools for career advancement. Health promotion professionals at all stages of their career can employ social media to develop their profile, network with a range of colleagues, and learn about jobs and other career-enhancing opportunities. This article focuses on several social media resources, describes their key functions for career development, and offers strategies for effective use. Steps in using social media include creating a personal profile, sharing products such as newsletters or publications, and locating volunteer and job opportunities. Learning skills to use social media effectively is important to advancing careers and to the expansion of the public health workforce.

Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge

PubMed Central

2014-01-01

Background Combining different sources of knowledge to build improved structure activity relationship models is not easy owing to the variety of knowledge formats and the absence of a common framework to interoperate between learning techniques. Most of the current approaches address this problem by using consensus models that operate at the prediction level. We explore the possibility to directly combine these sources at the knowledge level, with the aim to harvest potentially increased synergy at an earlier stage. Our goal is to design a general methodology to facilitate knowledge discovery and produce accurate and interpretable models. Results To combine models at the knowledge level, we propose to decouple the learning phase from the knowledge application phase using a pivot representation (lingua franca) based on the concept of hypothesis. A hypothesis is a simple and interpretable knowledge unit. Regardless of its origin, knowledge is broken down into a collection of hypotheses. These hypotheses are subsequently organised into hierarchical network. This unification permits to combine different sources of knowledge into a common formalised framework. The approach allows us to create a synergistic system between different forms of knowledge and new algorithms can be applied to leverage this unified model. This first article focuses on the general principle of the Self Organising Hypothesis Network (SOHN) approach in the context of binary classification problems along with an illustrative application to the prediction of mutagenicity. Conclusion It is possible to represent knowledge in the unified form of a hypothesis network allowing interpretable predictions with performances comparable to mainstream machine learning techniques. This new approach offers the potential to combine knowledge from different sources into a common framework in which high level reasoning and meta-learning can be applied; these latter perspectives will be explored in future work. PMID:24959206

The interactive use of networking multimedia--innovative education resource for professionals and patients.

PubMed

Matthies, H K; Walter, G F; Brandis, A; Stan, A C; Ammann, A; von Jan, U; Porth, A J

1999-01-01

The combination of new and rapidly developing interactive multimedia computers and applications with electronic networks will require a restructuring of our traditional approach to strategic planning and organizational structure. Worldwide telecommunication networks (using satellites, cable) are now facilitating the global pooling of healthcare information and medical knowledge independent of location. The development of multimedia information and communication systems demands cooperative working teams of authors, who are able to master several areas of medical knowledge as well as the presentation of these in different multimedia forms. The assemblage of telematics and services offers a base for multimedia applications, for example teleteaching, telelearning, telepublishing, teleconsulting, teleconferencing, telemedicine etc. The expansion of the internet will also lead to the formation of interdisciplinary "Global Education Networks". The theory and practice of education are undergoing dramatic changes. Lifelong learning and adaptation of medical practice to new knowledge and new techniques will be even more important in the future.

Organisational aspects and benchmarking of e-learning initiatives: a case study with South African community health workers.

PubMed

Reisach, Ulrike; Weilemann, Mitja

2016-06-01

South Africa desperately needs a comprehensive approach to fight HIV/AIDS. Education is crucial to reach this goal and Internet and e-learning could offer huge opportunities to broaden and deepen the knowledge basis. But due to the huge societal and digital divide between rich and poor areas, e-learning is difficult to realize in the townships. Community health workers often act as mediators and coaches for people seeking medical and personal help. They could give good advice regarding hygiene, nutrition, protection of family members in case of HIV/AIDS and finding legal ways to earn one's living if they were trained to do so. Therefore they need to have a broader general knowledge. Since learning opportunities in the townships are scarce, a system for e-learning has to be created in order to overcome the lack of experience with computers or the Internet and to enable them to implement a network of expertise. The article describes how the best international resources on basic medical knowledge, HIV/AIDS as well as on basic economic and entrepreneurial skills were benchmarked to be integrated into an e-learning system. After tests with community health workers, researchers developed recommendations on building a self-sustaining system for learning, including a network of expertise and best practice sharing. The article explains the opportunities and challenges for community health workers, which could provide information for other parts of the world with similar preconditions of rural poverty. © The Author(s) 2015.

Learning online social support: an investigation of network information technology based on UTAUT.

PubMed

Lin, Chieh-Peng; Anol, Bhattacherjee

2008-06-01

Extending the unified theory of acceptance and use of technology (UTAUT) model, this study postulates a model of online social support. The model is empirically tested using data from undergraduates in Taiwan regarding their usage of instant messaging (IM). The test results indicate that all model paths are significant, except that the path between online social support and facilitating conditions is insignificant. This study offers limitations and implications.

Collaborative networks for both improvement and research.

PubMed

Clancy, Carolyn M; Margolis, Peter A; Miller, Marlene

2013-06-01

Moving significant therapeutic discoveries beyond early biomedical translation or T1 science and into practice involves: (1) T2 science, identifying "the right treatment for the right patient in the right way at the right time" (eg, patient-centered outcomes research) and tools to implement this knowledge (eg, guidelines, registries); and (2) T3 studies addressing how to achieve health care delivery change. Collaborative improvement networks can serve as large-scale, health system laboratories to engage clinicians, researchers, patients, and parents in testing approaches to translate research into practice. Improvement networks are of particular importance for pediatric T2 and T3 research, as evidence to establish safety and efficacy of therapeutic interventions in children is often lacking. Networks for improvement and research are also consistent with the Institute of Medicine's Learning Healthcare Systems model in which learning networks provide a system for improving care and outcomes and generate new knowledge in near real-time. Creation of total population registries in collaborative network sites provides large, representative study samples with high-quality data that can be used to generate evidence and to inform clinical decision-making. Networks use collaboration, data, and quality-improvement methods to standardize practice. Therefore, variation in outcomes due to unreliable and unnecessary care delivery is reduced, increasing statistical power, and allowing a consistent baseline from which to test new strategies. In addition, collaborative networks for improvement and research offer the opportunity to not only make improvements but also to study improvements to determine which interventions and combination of strategies work best in what settings.

NASA's Universe of Learning: Engaging Learners in Discovery

NASA Astrophysics Data System (ADS)

Cominsky, L.; Smith, D. A.; Lestition, K.; Greene, M.; Squires, G.

2016-12-01

NASA's Universe of Learning is one of 27 competitively awarded education programs selected by NASA's Science Mission Directorate (SMD) to enable scientists and engineers to more effectively engage with learners of all ages. The NASA's Universe of Learning program is created through a partnership between the Space Telescope Science Institute, Chandra X-ray Center, IPAC at Caltech, Jet Propulsion Laboratory Exoplanet Exploration Program, and Sonoma State University. The program will connect the scientists, engineers, science, technology and adventure of NASA Astrophysics with audience needs, proven infrastructure, and a network of over 500 partners to advance the objectives of SMD's newly restructured education program. The multi-institutional team will develop and deliver a unified, consolidated suite of education products, programs, and professional development offerings that spans the full spectrum of NASA Astrophysics, including the Exoplanet Exploration theme. Program elements include enabling educational use of Astrophysics mission data and offering participatory experiences; creating multimedia and immersive experiences; designing exhibits and community programs; providing professional development for pre-service educators, undergraduate instructors, and informal educators; and, producing resources for special needs and underserved/underrepresented audiences. This presentation will provide an overview of the program and process for mapping discoveries to products and programs for informal, lifelong, and self-directed learning environments.

SCENERY: a web application for (causal) network reconstruction from cytometry data

PubMed Central

Papoutsoglou, Georgios; Athineou, Giorgos; Lagani, Vincenzo; Xanthopoulos, Iordanis; Schmidt, Angelika; Éliás, Szabolcs; Tegnér, Jesper

2017-01-01

Abstract Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/. PMID:28525568

Professional development through attending conferences: reflections of a health librarian.

PubMed

Jenkins, Ruth

2015-06-01

In this article, guest writer Ruth Jenkins from Berkshire Heathcare Foundation Trust reflects on two conferences she attended in 2014, LILAC and SLA. Through the process of reflection, she considers the benefits that attending conferences can have to library and information professionals in the health sector. In particular, she discusses the opportunities and areas for learning and professional development that conferences can offer including evidence-based practice and current awareness, gaining new knowledge and objectivity, and networking and the unexpected benefits of conferences. Ruth also offers some practical hints and tips on ways to facilitate your attendance at conferences, including through awards and funding. H.S. © 2015 Health Libraries Group.

Social media use and educational preferences among first-year pharmacy students.

PubMed

Clauson, Kevin A; Singh-Franco, Devada; Sircar-Ramsewak, Feroza; Joseph, Shine; Sandars, John

2013-01-01

Social media may offer a means to engage students, facilitate collaborative learning, and tailor educational delivery for diverse learning styles. The purpose of this study is to characterize social media awareness among pharmacy students and determine perceptions toward integrating these tools in education. A 23-item survey was administered to 1st-year students at a multicampus college of pharmacy. Students (95% response rate; N = 196) most commonly used wikis (97%), social networking (91%), and videosharing (84%). Tools reported as never used or unknown included social bookmarking (89%), collaborative writing (84%), and RSS readers (73%). Respondents indicated that educational integration of social media would impact their ability to learn in a positive/very positive manner (75%) and make them feel connected/very connected (68%). Selectively targeting social media for educational integration and instructing pharmacy students how to employ a subset of these tools may be useful in engaging them and encouraging lifelong learning.

[The inter-university learning website: a national university network for online teaching of pathology].

PubMed

Gauchotte, Guillaume; Ameisen, David; Boutonnat, Jean; Battistella, Maxime; Copie, Christiane; Garcia, Stéphane; Rigau, Valérie; Galateau-Sallé, Françoise; Terris, Benoit; Vergier, Béatrice; Wendum, Dominique; Bertheau, Philippe

2013-06-01

Building online teaching materials is a highly time and energy consuming task for teachers of a single university. With the help of the Collège des pathologistes, we initiated a French national university network for building mutualized online teaching pathology cases, tests and other pedagogic resources. Nineteen French universities are associated to this project, initially funded by UNF3S (http://www.unf3s.org/). One national e-learning Moodle platform (http://virtual-slides.univ-paris7.fr/moodle/) contains texts, medias and URL pointing toward decentralized virtual slides. The Moodle interface has been explained to the teachers since september 2011 using web-based conferences with screen-sharing. The following contents have been created: 20 clinical cases, several tests with multiple choices and short answer questions, and gross examination videos. A survey with 16 teachers and students showed a 94 % satisfaction rate, most of the 16 participants being favorable to the development of e-learning, in parallel with other courses in classroom. These tools will be further developed for the different study levels of pathology. In conclusion, these tools offer very interesting perspectives for pathology teaching. The organization of a national inter-university network is a useful way to create and share numerous and good-quality pedagogic resources. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

The Internet for Librarians

NASA Astrophysics Data System (ADS)

Grothkopf, U.

Librarianship is currently undergoing major changes. New information sources, accessible via the "network of networks", the Internet, offer opportunities which were previously unknown, but which require continuous ongoing learning. The Internet seems to be organized badly or not at all. The poor appearance might lead to an underestimation of its value. In the following, an introduction to the main functions will be given in order to facilitate understanding and use of the Internet. E-Mail, FTP (File Transfer Protocol) and Telnet will be covered, as well as Mailing lists, Newsgroups and the tools Archie, Gopher, Veronica, WAIS (Wide Area Information Server) and the World Wide Web (WWW). Examples will be given to show possible applications for library services.

Social networking for adolescents with severe haemophilia.

PubMed

Khair, K; Holland, M; Carrington, S

2012-05-01

Access to modern treatments allows adolescents with haemophilia to manage their haemophilia at home, with improved treatment outcomes and quality of life, but has reduced peer support and the potential for experiential learning from older peers. Social networking, aided by modern communication technologies, may offer health benefits through peer support. We sought to assess whether or not disease-specific social networking could benefit adolescents with severe haemophilia. A total of 150 adolescents (aged 10-18) with severe haemophilia A or B from 11 UK treatment centres or those who had attended focus groups to explore the potential for a social network designed specifically for their use were surveyed. Teenage boys with severe haemophilia in the UK who responded to an online and paper questionnaire (n = 47; 31% response rate) rarely knew of or socialized with others with haemophilia outside their families. Two-thirds of respondents said they would like to meet others. For 70% of boys, parents were the major source of information about haemophilia, yet more than half said they often had trouble finding answers to their questions. These boys frequently used online social networks to chat with friends. Adolescents with severe haemophilia frequently have limited contact with others and many wish to have greater contact. They may benefit from peer support and experiential learning gained through online social networking. The SixVibe restricted access social network is to be launched in 2011. It includes features designed to promote and facilitate the development of peer-to peer disease management skills for adolescents with severe haemophilia. © 2011 Blackwell Publishing Ltd.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

PubMed

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

PubMed Central

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

One Family's Journey: Medical Home and the Network of Supports It Offers Children and Youth with Special Health Care Needs. Part One

ERIC Educational Resources Information Center

MacDonald, Sarah; Gatto, Molly; Walker, Deb; Turchi, Renee

2007-01-01

This is the first article in a year long series that presents the experiences of a fictitious couple, Amita and Samir, as they learn to adapt to the reality of having a premature baby with special needs. Doris, the fictitious nurse who took care of baby Anjali in the neonatal intensive care unit (NICU), has had ten years of experience working in…

Enhancing Diversity in the Geosciences through National Dissemination of the AMS Online Weather Studies Distance Learning Course

NASA Astrophysics Data System (ADS)

Weinbeck, R. S.; Geer, I. W.; Mills, E. W.; Porter, W. A.; Moran, J. M.

2002-12-01

Our nation faces a serious challenge in attracting young people to science and science-related careers (including teaching). This is particularly true for members of groups underrepresented in science, mathematics, engineering, and technology and is especially acute in the number of minority college students majoring in the geosciences. A formidable obstacle in attracting undergraduates to the geosciences is lack of access, that is, no opportunity to enroll in an introductory geoscience course simply because none is offered at their college or university. Often introductory or survey courses are a student's first exposure to the geosciences. To help alleviate this problem, the American Meteorological Society (AMS) through its Education Program developed and implemented nationally an introductory weather and climate course, Online Weather Studies, which can be added to an institution's menu of general education course offerings. This highly successful course will be offered at 130 colleges and universities nationwide, including 30 minority-serving institutions, 20 of which have joined the AMS Online Weather Studies Diversity Program during 2002. The AMS encourages course adoption by more institutions serving large numbers of minority students through support from the National Science Foundation (NSF) Opportunities for Enhancing Diversity in the Geosciences (OEDG) and Course, Curriculum and Laboratory Improvement-National Dissemination (CCLI-ND) programs. Online Weather Studies is an innovative, 12- to 15-week introductory college-level, online distance-learning course on the fundamentals of atmospheric science. Learner-formatted current weather data are delivered via the Internet and coordinated with investigations keyed to the day's weather. The principal innovation of Online Weather Studies is that students learn about weather as it happens in near real-time-a highly motivational learning experience. The AMS Education Program designed and services this course and makes it available to colleges and universities as a user-friendly turnkey package with electronic and printed components. The AMS Diversity Program, in cooperation with the National Weather Service (NWS) facilitates institutional participation in Online Weather Studies. Prior to an instructor's initial offering of the course, he or she is invited to attend a one-week course implementation workshop at the NWS Training Center at Kansas City, MO. Participants then join an interactive network to share best practices ideas in science content and teaching strategies related to their offering of Online Weather Studies. They participate in a mentoring program that networks students with professional meteorologists and provides opportunities for internships, summer research, and career counseling. Meteorologists-in-Charge at NWS Weather Forecast Offices across the nation have volunteered their time to help make these opportunities possible. Also, participants are invited to attend the Educational Symposium of the AMS Annual Meeting where they will attend a special Diversity Session and are encouraged to present a paper or poster.

Building Intrusion Detection with a Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Wälchli, Markus; Braun, Torsten

This paper addresses the detection and reporting of abnormal building access with a wireless sensor network. A common office room, offering space for two working persons, has been monitored with ten sensor nodes and a base station. The task of the system is to report suspicious office occupation such as office searching by thieves. On the other hand, normal office occupation should not throw alarms. In order to save energy for communication, the system provides all nodes with some adaptive short-term memory. Thus, a set of sensor activation patterns can be temporarily learned. The local memory is implemented as an Adaptive Resonance Theory (ART) neural network. Unknown event patterns detected on sensor node level are reported to the base station, where the system-wide anomaly detection is performed. The anomaly detector is lightweight and completely self-learning. The system can be run autonomously or it could be used as a triggering system to turn on an additional high-resolution system on demand. Our building monitoring system has proven to work reliably in different evaluated scenarios. Communication costs of up to 90% could be saved compared to a threshold-based approach without local memory.

Addressing uncertainty in atomistic machine learning.

PubMed

Peterson, Andrew A; Christensen, Rune; Khorshidi, Alireza

2017-05-10

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems.

PubMed

Bashor, Caleb J; Horwitz, Andrew A; Peisajovich, Sergio G; Lim, Wendell A

2010-01-01

The living cell is an incredibly complex entity, and the goal of predictively and quantitatively understanding its function is one of the next great challenges in biology. Much of what we know about the cell concerns its constituent parts, but to a great extent we have yet to decode how these parts are organized to yield complex physiological function. Classically, we have learned about the organization of cellular networks by disrupting them through genetic or chemical means. The emerging discipline of synthetic biology offers an additional, powerful approach to study systems. By rearranging the parts that comprise existing networks, we can gain valuable insight into the hierarchical logic of the networks and identify the modular building blocks that evolution uses to generate innovative function. In addition, by building minimal toy networks, one can systematically explore the relationship between network structure and function. Here, we outline recent work that uses synthetic biology approaches to investigate the organization and function of cellular networks, and describe a vision for a synthetic biology toolkit that could be used to interrogate the design principles of diverse systems.

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

PubMed Central

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

PubMed

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

High efficacy and students' satisfaction after voluntary vs mandatory use of an e-learning program in traumatology and orthopedics--a follow-up study.

PubMed

Back, David Alexander; Haberstroh, Nicole; Sostmann, Kai; Schmidmaier, Gerhard; Putzier, Michael; Perka, Carsten; Hoff, Eike

2014-01-01

Within the last decade, e-learning has gained a consistent place in surgical teaching. However, as the use of new programs is often voluntary, more information on the implications of the data regarding user acceptance and knowledge with mandatory use is desirable, especially in the context of the long-term developments of courses. Starting in 2009, the e-learning program Network for Students in Traumatology and Orthopedics was offered in a voluntary blended learning context. Students' satisfaction and increase in knowledge were evaluated using questionnaires and written tests. With proven effectiveness, the program became a mandatory part of the curriculum, and students' attitudes and gain of knowledge were re-evaluated in 2010 and 2011 to detect differences in voluntary vs mandatory use. In the evaluation questionnaires (n = 108 voluntary vs n = 361 mandatory), the overall appreciation regarding the offerings remained high. Significantly more students felt better prepared for clinical situations (p < 0.001) and asked for e-tutoring (p = 0.025) with mandatory use. In written tests, both voluntary (n = 70) and mandatory (n = 147) users showed significantly increased knowledge (p < 0.001). Starting with a lower base level (p < 0.001), mandatory users had a significantly higher absolute increase compared with voluntary users (p = 0.015), leading to a similar final level. The presented blended learning concept was an efficient way to teach students orthopedics and traumatology. Data can support the assumption that even if the voluntary evaluation of e-learning offerings might be subject to a selection bias, the results can serve as a representative impression for the students' overall mood and their gain in knowledge. However, as changes would have to be anticipated when shifting to mandatory use, users' perceptions should be constantly evaluated. © 2013 Published by Association of Program Directors in Surgery on behalf of Association of Program Directors in Surgery.

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Template-based procedures for neural network interpretation.

PubMed

Alexander, J A.; Mozer, M C.

1999-04-01

Although neural networks often achieve impressive learning and generalization performance, their internal workings are typically all but impossible to decipher. This characteristic of the networks, their opacity, is one of the disadvantages of connectionism compared to more traditional, rule-oriented approaches to artificial intelligence. Without a thorough understanding of the network behavior, confidence in a system's results is lowered, and the transfer of learned knowledge to other processing systems - including humans - is precluded. Methods that address the opacity problem by casting network weights in symbolic terms are commonly referred to as rule extraction techniques. This work describes a principled approach to symbolic rule extraction from standard multilayer feedforward networks based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to the actual weights of a unit. Depending on the requirements of the application domain, the approach can accommodate n-ary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k(2)) complexity, or more general classes of recursive n-of-m expressions with O(k(L+2)) complexity, where k is the number of inputs to an unit and L the recursion level of the expression class. Compared to other approaches in the literature, our method of rule extraction offers benefits in simplicity, computational performance, and overall flexibility. Simulation results on a variety of problems demonstrate the application of our procedures as well as the strengths and the weaknesses of our general approach.

Grounding cognitive control in associative learning.

PubMed

Abrahamse, Elger; Braem, Senne; Notebaert, Wim; Verguts, Tom

2016-07-01

Cognitive control covers a broad range of cognitive functions, but its research and theories typically remain tied to a single domain. Here we outline and review an associative learning perspective on cognitive control in which control emerges from associative networks containing perceptual, motor, and goal representations. Our review identifies 3 trending research themes that are shared between the domains of conflict adaptation, task switching, response inhibition, and attentional control: Cognitive control is context-specific, can operate in the absence of awareness, and is modulated by reward. As these research themes can be envisaged as key characteristics of learning, we propose that their joint emergence across domains is not coincidental but rather reflects a (latent) growth of interest in learning-based control. Associative learning has the potential for providing broad-scaled integration to cognitive control theory, and offers a promising avenue for understanding cognitive control as a self-regulating system without postulating an ill-defined set of homunculi. We discuss novel predictions, theoretical implications, and immediate challenges that accompany an associative learning perspective on cognitive control. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Geophysical phenomena classification by artificial neural networks

NASA Technical Reports Server (NTRS)

Gough, M. P.; Bruckner, J. R.

1995-01-01

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

SCENERY: a web application for (causal) network reconstruction from cytometry data.

PubMed

Papoutsoglou, Georgios; Athineou, Giorgos; Lagani, Vincenzo; Xanthopoulos, Iordanis; Schmidt, Angelika; Éliás, Szabolcs; Tegnér, Jesper; Tsamardinos, Ioannis

2017-07-03

Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Five Years of BEACO2N: First Results and Lessons Learned

NASA Astrophysics Data System (ADS)

Shusterman, A.; Cohen, R. C.

2017-12-01

The BErkeley Atmospheric CO2 Observation Network (BEACO2N) is an ongoing greenhouse gas and air quality monitoring campaign based in the San Francisco Bay Area of Northern California. BEACO2N is a distributed network instrument consisting of low- to moderate-cost commercial sensors for CO2 and other pollutants installed on top of schools, museums, and other outreach-minded institutions. The reduced cost of each individual sensor "node" enables the deployment of a larger volume of total nodes, resulting in a web of approximately 50 sites with an average node-to-node distance of 2 km. Operating in some variation of this configuration since 2012, BEACO2N offers greater spatio-temporal coverage than any other fixed CO2 monitoring network to date. This high-resolution information allows us to faithfully represent the true heterogeneity of urban emission processes and distinguish between specific sources that are often regulated independently, but typically treated en masse by sparser, conventional surface monitors. However, maintaining and appropriately interpreting a network of BEACO2N's size presents a number of unique data quality and data coverage challenges. Here we describe the quantitative capabilities of the BEACO2N platform, first results from initial attempts at constraining greenhouse gas emission estimates, as well as other lessons learned over the first five years of operation.

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

PubMed

González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

2017-06-02

Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

An energy-aware routing protocol for query-based applications in wireless sensor networks.

PubMed

Ahvar, Ehsan; Ahvar, Shohreh; Lee, Gyu Myoung; Crespi, Noel

2014-01-01

Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

PubMed Central

González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G.; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

2017-01-01

Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances. PMID:28574426

An Energy-Aware Routing Protocol for Query-Based Applications in Wireless Sensor Networks

PubMed Central

Crespi, Noel

2014-01-01

Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption. PMID:24696640

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

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.

A novel application of deep learning for single-lead ECG classification.

PubMed

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

A Strategy to Learn How to Build Scientific-Education and Outreach Partnerships in the Ocean Sciences: COSEE Ocean Learning Communities.

NASA Astrophysics Data System (ADS)

Keil, R. G.; Bell, P. L.; Bittner, M. S.; Robigou, V.; Sider, K.

2005-12-01

The College of Ocean and Fishery Sciences and the College of Education at the University of Washington, the Seattle Aquarium, and the California Maritime Academy formed a partnership to establish a Center for Ocean Sciences Education Excellence (COSEE) labeled "Ocean Learning Communities." The COSEE-OLC will join the national network of NSF-funded centers that provide a catalytic environment in which partnerships between ocean researchers and educators flourish. The COSEE network contributes to the national advancement of ocean science education by sharing high-quality K-12 or informal education programs, best practices and methodologies, and offering exemplary courses through the network and at national professional meetings. Building on the successes and lessons of the existing COSEE centers, the COSEE-OLC will foster collaborations among the oceanography research community, the science of learning community, informal and formal educators, the general public, and the maritime industry in the Northwest region and the West coast. The concept for this partnership is based on reaching out to traditionally underserved populations (from the businesses that use the sea or for which economic success depends on the oceans to the united native tribes), listening to their concerns and needs and how these can be addressed within the context of ocean-based research. The challenges of integrating education and outreach with scientific research programs are addressed by the center's main catalytic activity to create Ocean Learning Communities. These communities will be gatherings of traditionally disparate stakeholders including scientists, educators, representatives of businesses with a connection to the oceans, and citizens who derive economic or recreational sustenance from the oceans. The center's principal goal is to, through time and structured learning activities, support various communities 1) to develop a common language and 2) to make a commitment to creating collaborations that will improve ocean research and public awareness at the regional scale. Researchers in the science of learning will evaluate and study the successes and challenges of these regional approaches to better understand the development and sustainability of productive partnerships and to develop learning models to share and apply at the national level.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

A smarter way to network.

PubMed

Cross, Rob; Thomas, Robert

2011-01-01

The adage "It's not what you know, it's who you know" is true. The right social network can have a huge impact on your success. But many people have misguided ideas about what makes a network strong: They believe the key is having a large circle filled with high-powered contacts. That's not the right approach, say Cross, of UVA's McIntire School of Commerce, and Thomas, of the Accenture Institute for High Performance. The authors, who have spent years researching how organizations can capitalize on employees' social networks, have seen that the happiest, highest-performing executives have a different kind of network: select but diverse, made up of high-quality relationships with people who come from varying spheres and from up and down the corporate ladder. Effective networks typically range in size from 12 to 18 people. They help managers learn, make decisions with less bias, and grow personally. Cross and Thomas have found that they include six critical kinds of connections: people who provide information, ideas, or expertise; formally and informally powerful people, who offer mentoring and political support; people who give developmental feedback; people who lend personal support; people who increase your sense of purpose or worth; and people who promote work/life balance. Moreover, the best kind of connections are "energizers"--positive, trustworthy individuals who enjoy other people and always see opportunities, even in challenging situations. If your network doesn't look like this, you can follow a four-step process to improve it. You'll need to identify who your connections are and what they offer you, back away from redundant and energy-draining connections, fill holes in your network with the right kind of people, and work to make the most of your contacts. Do this, and in due course, you'll have a network that steers the best opportunities, ideas, and talent your way.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Supporting clinical leadership through action: The nurse consultant role.

PubMed

Rosser, Elizabeth; Grey, Rachael; Neal, Deborah; Reeve, Julie; Smith, Caroline; Valentine, Janine

2017-12-01

To evaluate the effectiveness of an action learning set to enhance clinical leadership and extend their scope and confidence more strategically. As the most senior clinical role in most healthcare systems, the consultant nurse role is a solitary one. They are required to develop personal resilience, commitment and a belief in their ability to lead, with new consultants needing a strong support network to succeed. Following a 2-year action learning set, four nurse consultants, one therapy consultant, and a university educationalist engaged in a cooperative inquiry approach using four cycles of discussion, reflection, analysis and action over an 18-month period from March 2015-July 2016, to learn how to change and enhance their working practices. Data were analysed thematically. Four themes emerged where the action learning set (i) offered structure and support, (ii) enabled a wider influence and (iii) empowered them to lead. The cooperative inquiry helped them realise how much they had gained from their collective learning and they felt empowered to lead. Their motivation to "make a difference" remains palpable. The outcomes of the cooperative inquiry included an enhanced understanding of the importance of openness and trust and a willingness to share and learn from each other in a respectful and confidential environment with a receptiveness to change. Self-leadership has clearly been accepted and embraced, and their collaboration has improved communication across the organisation, enhanced their strategic leadership capability and given confidence to disseminate externally. The action learning set offered structure to support these clinical leaders to keep them focused across the breadth of their role. Additionally, peer review with external facilitation has enabled these clinical leaders to gain a wider influence and empowered them to lead. © 2017 John Wiley & Sons Ltd.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

ERIC Educational Resources Information Center

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

ERIC Educational Resources Information Center

van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

2018-01-01

Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

Up the ANTe: Understanding Entrepreneurial Leadership Learning through Actor-Network Theory

ERIC Educational Resources Information Center

Smith, Sue; Kempster, Steve; Barnes, Stewart

2017-01-01

This article explores the role of educators in supporting the development of entrepreneurial leadership learning by creating peer learning networks of owner-managers of small businesses. Using actor-network theory, the authors think through the process of constructing and maintaining a peer learning network (conceived of as an actor-network) and…

Use of social media in graduate-level medical humanities education: two pilot studies from Penn State College of Medicine.

PubMed

George, Daniel R; Dellasega, Cheryl

2011-01-01

Social media strategies in education have gained attention for undergraduate students, but there has been relatively little application with graduate populations in medicine. To use and evaluate the integration of new social media tools into the curricula of two graduate-level medical humanities electives offered to 4th-year students at Penn State College of Medicine. Instructors selected five social media tools--Twitter, YouTube, Flickr, blogging and Skype--to promote student learning. At the conclusion of each course, students provided quantitative and qualitative course evaluation. Students gave high favourability ratings to both courses, and expressed that the integration of social media into coursework augmented learning and collaboration. Others identified challenges including: demands on time, concerns about privacy and lack of facility with technology. Integrating social media tools into class activities appeared to offer manifold benefits over traditional classroom methods, including real-time communication outside of the classroom, connecting with medical experts, collaborative opportunities and enhanced creativity. Social media can augment learning opportunities within humanities curriculum in medical schools, and help students acquire tools and skill-sets for problem solving, networking, and collaboration. Command of technologies will be increasingly important to the practice of medicine in the twenty-first century.

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics

PubMed Central

Sinapayen, Lana; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle “Learning by Stimulation Avoidance” (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system. PMID:28158309

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

PubMed

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

Citizen social science: a methodology to facilitate and evaluate workplace learning in continuing interprofessional education.

PubMed

Dadich, Ann

2014-05-01

Workplace learning in continuing interprofessional education (CIPE) can be difficult to facilitate and evaluate, which can create a number of challenges for this type of learning. This article presents an innovative method to foster and investigate workplace learning in CIPE - citizen social science. Citizen social science involves clinicians as co-researchers in the systematic examination of social phenomena. When facilitated by an open-source online social networking platform, clinicians can participate via computer, smartphone, or tablet in ways that suit their needs and preferences. Furthermore, as co-researchers they can help to reveal the dynamic interplay that facilitates workplace learning in CIPE. Although yet to be tested, citizen social science offers four potential benefits: it recognises and accommodates the complexity of workplace learning in CIPE; it has the capacity to both foster and evaluate the phenomena; it can be used in situ, capturing and having direct relevance to the complexity of the workplace; and by advancing both theoretical and methodological debates on CIPE, it may reveal opportunities to improve and sustain workplace learning. By describing an example situated in the youth health sector, this article demonstrates how these benefits might be realised.

From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers

PubMed Central

Purkayastha, S.; Biswas, R.; Jai Ganesh, A.U.; Otero, P.

2015-01-01

Summary Objective To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care. Introduction This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting. Methods We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence. Conclusion UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all. PMID:26123908

From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers. Contribution of the Health Informatics Education Working Group.

PubMed

Purkayastha, S; Price, A; Biswas, R; Jai Ganesh, A U; Otero, P

2015-08-13

To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care. This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting. We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence. UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

PubMed

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

eHealth for Remote Regions: Findings from Central Asia Health Systems Strengthening Project.

PubMed

Sajwani, Afroz; Qureshi, Kiran; Shaikh, Tehniat; Sayani, Saleem

2015-01-01

Isolated communities in remote regions of Afghanistan, Kyrgyz Republic, Pakistan and Tajikistan lack access to high-quality, low-cost health care services, forcing them to travel to distant parts of the country, bearing an unnecessary financial burden. The eHealth Programme under Central Asia Health Systems Strengthening (CAHSS) Project, a joint initiative between the Aga Khan Foundation, Canada and the Government of Canada, was initiated in 2013 with the aim to utilize Information and Communication Technologies to link health care institutions and providers with rural communities to provide comprehensive and coordinated care, helping minimize the barriers of distance and time. Under the CAHSS Project, access to low-cost, quality health care is provided through a regional hub and spoke teleconsultation network of government and non-government health facilities. In addition, capacity building initiatives are offered to health professionals. By 2017, the network is expected to connect seven Tier 1 tertiary care facilities with 14 Tier 2 secondary care facilities for teleconsultation and eLearning. From April 2013 to September 2014, 6140 teleconsultations have been provided across the project sites. Additionally, 52 new eLearning sessions have been developed and 2020 staff members have benefitted from eLearning sessions. Ethics and patient rights are respected during project implementation.

Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease

PubMed Central

Yelshyna, Darya; Bicho, Estela

2016-01-01

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics. PMID:28074090

Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease.

PubMed

Costa, Luís; Gago, Miguel F; Yelshyna, Darya; Ferreira, Jaime; David Silva, Hélder; Rocha, Luís; Sousa, Nuno; Bicho, Estela

2016-01-01

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

Accelerating Innovation Through Coopetition: The Innovation Learning Network Experience.

PubMed

McCarthy, Chris; Ford Carleton, Penny; Krumpholz, Elizabeth; Chow, Marilyn P

Coopetition, the simultaneous pursuit of cooperation and competition, is a growing force in the innovation landscape. For some organizations, the primary mode of innovation continues to be deeply secretive and highly competitive, but for others, a new style of shared challenges, shared purpose, and shared development has become a superior, more efficient way of working to accelerate innovation capabilities and capacity. Over the last 2 decades, the literature base devoted to coopetition has gradually expanded. However, the field is still in its infancy. The majority of coopetition research is qualitative, primarily consisting of case studies. Few studies have addressed the nonprofit sector or service industries such as health care. The authors believe that this article may offer a unique perspective on coopetition in the context of a US-based national health care learning alliance designed to accelerate innovation, the Innovation Learning Network or ILN. The mission of the ILN is to "Share the joy and pain of innovation," accelerating innovation by sharing solutions, teaching techniques, and cultivating friendships. These 3 pillars (sharing, teaching, and cultivating) form the foundation for coopetition within the ILN. Through the lens of coopetition, we examine the experience of the ILN over the last 10 years and provide case examples that illustrate the benefits and challenges of coopetition in accelerating innovation in health care.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Inference of Spatio-Temporal Functions Over Graphs via Multikernel Kriged Kalman Filtering

NASA Astrophysics Data System (ADS)

Ioannidis, Vassilis N.; Romero, Daniel; Giannakis, Georgios B.

2018-06-01

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge of selecting the appropriate kernel, the proposed filter is combined with a multi-kernel selection module. Such a data-driven method selects a kernel attuned to the signal dynamics on-the-fly within the linear span of a pre-selected dictionary. The novel multi-kernel learning algorithm exploits the eigenstructure of Laplacian kernel matrices to reduce computational complexity. Numerical tests with synthetic and real data demonstrate the superior reconstruction performance of the novel approach relative to state-of-the-art alternatives.

Tele-education as method of medical education.

PubMed

Masic, Izet; Pandza, Haris; Kulasin, Igor; Masic, Zlatan; Valjevac, Salih

2009-01-01

Development of computer networks and introduction and application of new technologies in all aspects of human activity needs to be followed by universities in their transformation on how to approach scientific, research, and education teaching curricula. Development and increased use of distance learning (DL) over the past decade have clearly shown the potential and efficiency of information technology applied in education. Use of information technology in medical education is where medical informatics takes its place as important scientific discipline which ensures benefit from IT in teaching and learning process involved. Definition of telemedicine as "use of technologies based on health care delivered on distance" covers areas such as electronic health, tele-health (eHealth), telematics, but also tele-education. Web based medical education today is offered in different forms--from online lectures, online exams, web based continuous education programs, use of electronic libraries, online medical and scientific databases etc. Department of Medical Informatics of Medical Faculty of University of Sarajevo has taken many steps to introduce distance learning in medical curricula--from organising professional--scientific events (congresses, workshop etc), organizing first tele-exam at the faculty and among first at the university, to offering online lectures and online education material at the Department's website (www.unsa-medinfo.org). Distance learning in medical education, as well as telemedicine, significantly influence health care in general and are shaping the future model of medical practice. Basic computer and networks skills must be a part of all future medical curricula. The impact of technical equipment on patient-doctor relationship must be taken into account, and doctors have to be trained and prepared for diagnosing or consulting patients by use of IT. Telemedicine requires special approach in certain medical fields--tele-consultation, tele-surgery, tele-radiology and other specific telemedicine applications should be introduced to the curricula. Telemedicine and distance learning are best suited for medical education and doctor-to-doctor consultation--first contact between doctor and a patient should stay face-to-face when possible. In this paper, we present the results of the project Introduction and Implementation of Distance Learning at the Medical Faculty of University of Sarajevo and compare it with the following expected outcomes: development and integration of information technology in medical education; creation of flexible infrastructure which will enable access to e-learning to all students and teaching staff; improvement of digital literacy of academic population; ensuring high educational standards to students and teaching staff; helping medical staffto develop "life-long learning" approach in work and education.

Radio Galaxy Zoo: compact and extended radio source classification with deep learning

NASA Astrophysics Data System (ADS)

Lukic, V.; Brüggen, M.; Banfield, J. K.; Wong, O. I.; Rudnick, L.; Norris, R. P.; Simmons, B.

2018-05-01

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. In the context of radio-interferometric imaging in astronomy, we looked for ways to identify multiple components of individual sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers, and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple-component extended sources. We found that a three-convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4 per cent on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test accuracy of 93.5 per cent. The best-performing convolutional neural network set-up has been verified against RGZ Data Release 1 where a final test accuracy of 94.8 per cent was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.

Microwave vision for robots

NASA Technical Reports Server (NTRS)

Lewandowski, Leon; Struckman, Keith

1994-01-01

Microwave Vision (MV), a concept originally developed in 1985, could play a significant role in the solution to robotic vision problems. Originally our Microwave Vision concept was based on a pattern matching approach employing computer based stored replica correlation processing. Artificial Neural Network (ANN) processor technology offers an attractive alternative to the correlation processing approach, namely the ability to learn and to adapt to changing environments. This paper describes the Microwave Vision concept, some initial ANN-MV experiments, and the design of an ANN-MV system that has led to a second patent disclosure in the robotic vision field.

Constructing of Research-Oriented Learning Mode Based on Network Environment

ERIC Educational Resources Information Center

Wang, Ying; Li, Bing; Xie, Bai-zhi

2007-01-01

Research-oriented learning mode that based on network is significant to cultivate comprehensive-developing innovative person with network teaching in education for all-around development. This paper establishes a research-oriented learning mode by aiming at the problems existing in research-oriented learning based on network environment, and…

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Social network analysis of a project-based introductory physics course

NASA Astrophysics Data System (ADS)

Oakley, Christopher

2016-03-01

Research suggests that students benefit from peer interaction and active engagement in the classroom. The quality, nature, effect of these interactions is currently being explored by Physics Education Researchers. Spelman College offers an introductory physics sequence that addresses content and research skills by engaging students in open-ended research projects, a form of Project-Based Learning. Students have been surveyed at regular intervals during the second semester of trigonometry-based course to determine the frequency of interactions in and out of class. These interactions can be with current or past students, tutors, and instructors. This line of inquiry focuses on metrics of Social Network analysis, such as centrality of participants as well as segmentation of groups. Further research will refine and highlight deeper questions regarding student performance in this pedagogy and course sequence.

Scotland's knowledge network: a progress report on Knowledge into Action.

PubMed

Wales, Ann; Boyle, Derek

2015-11-01

Launched in 2012, Knowledge into Action is the national knowledge management strategy for the health and social care workforce in Scotland. It is transforming the role of the national digital knowledge service--NHS Education for Scotlands' Knowledge Network--and the NHSS librarian role to offer more active, tailored support for translating knowledge into frontline clinical practice. This includes the development of a national evidence search and summary service, help with converting knowledge into practical and usable formats for easy use at point of care and with using digital tools to share clinicians' learning, experience and expertise. Through this practical support, Knowledge into Action is contributing to quality and safety outcomes across NHS Scotland, building clinicians' capacity and capability in applying knowledge in frontline practice and service improvement. © The Author(s) 2015.

A self-organizing neural network for job scheduling in distributed systems

NASA Astrophysics Data System (ADS)

Newman, Harvey B.; Legrand, Iosif C.

2001-08-01

The aim of this work is to describe a possible approach for the optimization of the job scheduling in large distributed systems, based on a self-organizing Neural Network. This dynamic scheduling system should be seen as adaptive middle layer software, aware of current available resources and making the scheduling decisions using the "past experience." It aims to optimize job specific parameters as well as the resource utilization. The scheduling system is able to dynamically learn and cluster information in a large dimensional parameter space and at the same time to explore new regions in the parameters space. This self-organizing scheduling system may offer a possible solution to provide an effective use of resources for the off-line data processing jobs for future HEP experiments.

The Affective Core of Emotion: Linking Pleasure, Subjective Well-Being, and Optimal Metastability in the Brain

PubMed Central

Kringelbach, Morten L.; Berridge, Kent C.

2017-01-01

Arguably, emotion is always valenced—either pleasant or unpleasant—and dependent on the pleasure system. This system serves adaptive evolutionary functions; relying on separable wanting, liking, and learning neural mechanisms mediated by mesocorticolimbic networks driving pleasure cycles with appetitive, consummatory, and satiation phases. Liking is generated in a small set of discrete hedonic hotspots and coldspots, while wanting is linked to dopamine and to larger distributed brain networks. Breakdown of the pleasure system can lead to anhedonia and other features of affective disorders. Eudaimonia and well-being are difficult to study empirically, yet whole-brain computational models could offer novel insights (e.g., routes to eudaimonia such as caregiving of infants or music) potentially linking eudaimonia to optimal metastability in the pleasure system. PMID:28943891

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

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

Improving diagnostic recognition of primary hyperparathyroidism with machine learning.

PubMed

Somnay, Yash R; Craven, Mark; McCoy, Kelly L; Carty, Sally E; Wang, Tracy S; Greenberg, Caprice C; Schneider, David F

2017-04-01

Parathyroidectomy offers the only cure for primary hyperparathyroidism, but today only 50% of primary hyperparathyroidism patients are referred for operation, in large part, because the condition is widely under-recognized. The diagnosis of primary hyperparathyroidism can be especially challenging with mild biochemical indices. Machine learning is a collection of methods in which computers build predictive algorithms based on labeled examples. With the aim of facilitating diagnosis, we tested the ability of machine learning to distinguish primary hyperparathyroidism from normal physiology using clinical and laboratory data. This retrospective cohort study used a labeled training set and 10-fold cross-validation to evaluate accuracy of the algorithm. Measures of accuracy included area under the receiver operating characteristic curve, precision (sensitivity), and positive and negative predictive value. Several different algorithms and ensembles of algorithms were tested using the Weka platform. Among 11,830 patients managed operatively at 3 high-volume endocrine surgery programs from March 2001 to August 2013, 6,777 underwent parathyroidectomy for confirmed primary hyperparathyroidism, and 5,053 control patients without primary hyperparathyroidism underwent thyroidectomy. Test-set accuracies for machine learning models were determined using 10-fold cross-validation. Age, sex, and serum levels of preoperative calcium, phosphate, parathyroid hormone, vitamin D, and creatinine were defined as potential predictors of primary hyperparathyroidism. Mild primary hyperparathyroidism was defined as primary hyperparathyroidism with normal preoperative calcium or parathyroid hormone levels. After testing a variety of machine learning algorithms, Bayesian network models proved most accurate, classifying correctly 95.2% of all primary hyperparathyroidism patients (area under receiver operating characteristic = 0.989). Omitting parathyroid hormone from the model did not decrease the accuracy significantly (area under receiver operating characteristic = 0.985). In mild disease cases, however, the Bayesian network model classified correctly 71.1% of patients with normal calcium and 92.1% with normal parathyroid hormone levels preoperatively. Bayesian networking and AdaBoost improved the accuracy of all parathyroid hormone patients to 97.2% cases (area under receiver operating characteristic = 0.994), and 91.9% of primary hyperparathyroidism patients with mild disease. This was significantly improved relative to Bayesian networking alone (P < .0001). Machine learning can diagnose accurately primary hyperparathyroidism without human input even in mild disease. Incorporation of this tool into electronic medical record systems may aid in recognition of this under-diagnosed disorder. Copyright © 2016 Elsevier Inc. All rights reserved.

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Advancing Professional Development Through a Community of Practice: the New England Network for Faculty Affairs.

PubMed

Power, Christine M; Thorndyke, Luanne E; Milner, Robert J; Lowney, Kathleen; Irvin, Charles G; Fonseca-Kelly, Zoe; Benjamin, Emelia J; Bhasin, Robina M; Connelly, Maureen T

2018-01-01

In an era of competing priorities, funding is increasingly restricted for offices of faculty affairs and development. Opportunities for professional staff to grow and network through attendance at national meetings and to share best practices are limited. We sought to describe a community of practice established to enhance the professional development of faculty affairs professionals and to document its impact. We outlined the process of formation of the New England Network for Faculty Affairs (NENFA), reviewed the pedagogical approaches to professional development, and surveyed members to evaluate the impact of NENFA on their activities, professional network and their institutions. After a successful 2011 initial meeting, NENFA created an organizing committee and conducted a needs assessment among potential members. NENFA's charter, mission, goals, and structure were based on survey results. NENFA's regional community of practice grew to 31 institutions and held 10 meetings over 5 years. Meetings have examined a faculty development topic in depth using multiple learning formats to engage participants from academic medical centers and allied professions. Results from a 2015 member survey confirmed the value of NENFA. Multiple members documented changes in practice as a result of participating. NENFA has been sustained by volunteer leadership, collaboration, and the value that the group has brought to its members. We propose that a "community of practice" offers an effective model for collaborative learning among individuals at different institutions within a competitive health care environment. We recommend that the approach be replicated in other regions.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

PubMed

Walter, Florian; Röhrbein, Florian; Knoll, Alois

2015-12-01

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

Design and simulation of programmable relational optoelectronic time-pulse coded processors as base elements for sorting neural networks

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lazareva, Maria V.

2010-05-01

In the paper we show that the biologically motivated conception of time-pulse encoding usage gives a set of advantages (single methodological basis, universality, tuning simplicity, learning and programming et al) at creation and design of sensor systems with parallel input-output and processing for 2D structures hybrid and next generations neuro-fuzzy neurocomputers. We show design principles of programmable relational optoelectronic time-pulse encoded processors on the base of continuous logic, order logic and temporal waves processes. We consider a structure that execute analog signal extraction, analog and time-pulse coded variables sorting. We offer optoelectronic realization of such base relational order logic element, that consists of time-pulse coded photoconverters (pulse-width and pulse-phase modulators) with direct and complementary outputs, sorting network on logical elements and programmable commutation blocks. We make technical parameters estimations of devices and processors on such base elements by simulation and experimental research: optical input signals power 0.2 - 20 uW, processing time 1 - 10 us, supply voltage 1 - 3 V, consumption power 10 - 100 uW, extended functional possibilities, learning possibilities. We discuss some aspects of possible rules and principles of learning and programmable tuning on required function, relational operation and realization of hardware blocks for modifications of such processors. We show that it is possible to create sorting machines, neural networks and hybrid data-processing systems with untraditional numerical systems and pictures operands on the basis of such quasiuniversal hardware simple blocks with flexible programmable tuning.

Monitoring the Performance of a Neuro-Adaptive Controller

NASA Technical Reports Server (NTRS)

Schumann, Johann; Gupta, Pramod

2004-01-01

Traditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.

Peer Learning Network: Implementing and Sustaining Cooperative Learning by Teacher Collaboration

ERIC Educational Resources Information Center

Miquel, Ester; Duran, David

2017-01-01

This article describes an in-service teachers', staff-development model "Peer Learning Network" and presents results about its efficiency. "Peer Learning Network" promotes three levels of peer learning simultaneously (among pupils, teachers, and schools). It supports pairs of teachers from several schools, who are linked…

The Integration of Personal Learning Environments & Open Network Learning Environments

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

2012-01-01

Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

The World as Functional Learning Environment: An Intercultural Learning Network. Interactive Technology Laboratory Report #7.

ERIC Educational Resources Information Center

Cohen, Moshe; And Others

Electronic networks provide new opportunities to create functional learning environments which allow students in many different locations to carry out joint educational activities. A set of participant observation studies was conducted in the context of a cross-cultural, cross-language network called the Intercultural Learning Network in order to…

Education Partnerships in Teacher Training: McDonald Observatory and the Giant Magellan Telescope Organization.

NASA Astrophysics Data System (ADS)

Finkelstein, Keely; Preston, Sandra Lee; Hemenway, Mary; Malasarn, Davin; Wetzel, Marc

2015-08-01

McDonald Observatory in remote, west Texas has a long history of providing K-12 teacher professional development (PD) through workshops at the observatory. Recently, we have started a new teacher PD program in partnership with the Giant Magellan Telescope Organization (GMTO) to provide teacher training and establish a network of teachers and students engaged in the Giant Magellan Telescope. The GMT Teacher workshop has been offered to 30 teachers during the summers of 2014 and 2015; continued engagement has been offered to the teachers through online resources, networking, participation in other teacher PD opportunities and conferences. Evaluation has been conducted using several metrics immediately post workshops, and long term followup evaluation methods. At the close of the 2014 workshop teachers reported learning about the telescopes and nighttime observing, and the promise of the GMT. Consensus statements at the close of the workshop also spoke about passion for and appreciation of astronomy. The major source of recommendation during the year one workshop was to provide more detailed information or activities on the GMT during the workshops. While this does prove challenging to incorporate a full slate of activities on the GMT before the telescope is even built, we are currently working to produce more take-home materials which are GMT specific, continue to make general connections to telescope technology and science that are applicable to the GMT with the teachers. McDonald Observatory and GMTO will continue to partner to offer teacher PD related to the GMT, and increase the network of teachers and students engaged in the GMT, up and through the beginning of its operations in 2020. We will present the current highlights, evaulation outcome results, and future outlook for this program and collaboration.

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

NASA Astrophysics Data System (ADS)

Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

2017-09-01

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Learning in a Network: A "Third Way" between School Learning and Workplace Learning?

ERIC Educational Resources Information Center

Bottrup, Pernille

2005-01-01

Purpose--The aim of this article is to examine network-based learning and discuss how participation in network can enhance organisational learning. Design/methodology/approach--In recent years, companies have increased their collaboration with other organisations, suppliers, customers, etc., in order to meet challenges from a globalised market.…

How Neural Networks Learn from Experience.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

1992-01-01

Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…

Using Online Learning for At-Risk Students and Credit Recovery. Promising Practices in Online Learning

ERIC Educational Resources Information Center

Watson, John; Gemin, Butch

2008-01-01

Online learning programs are designed to expand high-quality educational opportunities and to meet the needs of diverse students. While the primary reason online courses are offered in school districts is to expand offerings to courses that would otherwise be unavailable, the second most commonly cited reason for offering online learning is to…

The importance of situation-specific encodings: analysis of a simple connectionist model of letter transposition effects

NASA Astrophysics Data System (ADS)

Fang, Shin-Yi; Smith, Garrett; Tabor, Whitney

2018-04-01

This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of "modality-specific" encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Quantitative learning strategies based on word networks

NASA Astrophysics Data System (ADS)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

The Structural Underpinnings of Policy Learning: A Classroom Policy Simulation

NASA Astrophysics Data System (ADS)

Bird, Stephen

This paper investigates the relationship between the centrality of individual actors in a social network structure and their policy learning performance. In a dynamic comparable to real-world policy networks, results from a classroom simulation demonstrate a strong relationship between centrality in social learning networks and grade performance. Previous research indicates that social network centrality should have a positive effect on learning in other contexts and this link is tested in a policy learning context. Second, the distinction between collaborative learning versus information diffusion processes in policy learning is examined. Third, frequency of interaction is analyzed to determine whether consistent, frequent tics have a greater impact on the learning process. Finally, the data arc analyzed to determine if the benefits of centrality have limitations or thresholds when benefits no longer accrue. These results demonstrate the importance of network structure, and support a collaborative conceptualization of the policy learning process.

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

Rethinking the learning of belief network probabilities

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

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Learning in Artificial Neural Systems

NASA Technical Reports Server (NTRS)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

Investigating Patterns of Interaction in Networked Learning and Computer-Supported Collaborative Learning: A Role for Social Network Analysis

ERIC Educational Resources Information Center

de Laat, Maarten; Lally, Vic; Lipponen, Lasse; Simons, Robert-Jan

2007-01-01

The focus of this study is to explore the advances that Social Network Analysis (SNA) can bring, in combination with other methods, when studying Networked Learning/Computer-Supported Collaborative Learning (NL/CSCL). We present a general overview of how SNA is applied in NL/CSCL research; we then go on to illustrate how this research method can…

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Getting Online: A Friendly Guide for Teachers, Students, and Parents.

ERIC Educational Resources Information Center

Educational Resources Information Center (ED), Washington, DC.

This brochure provides teachers, students, and parents with information on how to connect to a computer network; describes some of the education offerings available to network users; and offers hints to help make exploration of computer networks easy and successful. The brochure explains the equipment needed to connect to a computer network; ways…

Spanish Museum Libraries Network.

ERIC Educational Resources Information Center

Lopez de Prado, Rosario

This paper describes the creation of an automated network of museum libraries in Spain. The only way in which the specialized libraries in the world today can continue to be active and to offer valid information is to automate the service they offer, and create network libraries with cooperative plans. The network can be configured with different…

[CIRRNET® - learning from errors, a success story].

PubMed

Frank, O; Hochreutener, M; Wiederkehr, P; Staender, S

2012-06-01

CIRRNET® is the network of local error-reporting systems of the Swiss Patient Safety Foundation. The network has been running since 2006 together with the Swiss Society for Anaesthesiology and Resuscitation (SGAR), and network participants currently include 39 healthcare institutions from all four different language regions of Switzerland. Further institutions can join at any time. Local error reports in CIRRNET® are bundled at a supraregional level, categorised in accordance with the WHO classification, and analysed by medical experts. The CIRRNET® database offers a solid pool of data with error reports from a wide range of medical specialist's areas and provides the basis for identifying relevant problem areas in patient safety. These problem areas are then processed in cooperation with specialists with extremely varied areas of expertise, and recommendations for avoiding these errors are developed by changing care processes (Quick-Alerts®). Having been approved by medical associations and professional medical societies, Quick-Alerts® are widely supported and well accepted in professional circles. The CIRRNET® database also enables any affiliated CIRRNET® participant to access all error reports in the 'closed user area' of the CIRRNET® homepage and to use these error reports for in-house training. A healthcare institution does not have to make every mistake itself - it can learn from the errors of others, compare notes with other healthcare institutions, and use existing knowledge to advance its own patient safety.

Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Gul, M. Shahzeb Khan; Gunturk, Bahadir K.

2018-05-01

Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement.

Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks.

PubMed

Gul, M Shahzeb Khan; Gunturk, Bahadir K

2018-05-01

Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement.

Development of Methodologies for IV and V of Neural Networks

NASA Technical Reports Server (NTRS)

Taylor, Brian; Darrah, Marjorie

2003-01-01

Non-deterministic systems often rely upon neural network (NN) technology to "lean" to manage flight systems under controlled conditions using carefully chosen training sets. How can these adaptive systems be certified to ensure that they will become increasingly efficient and behave appropriately in real-time situations? The bulk of Independent Verification and Validation (IV&V) research of non-deterministic software control systems such as Adaptive Flight Controllers (AFC's) addresses NNs in well-behaved and constrained environments such as simulations and strict process control. However, neither substantive research, nor effective IV&V techniques have been found to address AFC's learning in real-time and adapting to live flight conditions. Adaptive flight control systems offer good extensibility into commercial aviation as well as military aviation and transportation. Consequently, this area of IV&V represents an area of growing interest and urgency. ISR proposes to further the current body of knowledge to meet two objectives: Research the current IV&V methods and assess where these methods may be applied toward a methodology for the V&V of Neural Network; and identify effective methods for IV&V of NNs that learn in real-time, including developing a prototype test bed for IV&V of AFC's. Currently. no practical method exists. lSR will meet these objectives through the tasks identified and described below. First, ISR will conduct a literature review of current IV&V technology. TO do this, ISR will collect the existing body of research on IV&V of non-deterministic systems and neural network. ISR will also develop the framework for disseminating this information through specialized training. This effort will focus on developing NASA's capability to conduct IV&V of neural network systems and to provide training to meet the increasing need for IV&V expertise in such systems.

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .

Listening and Learning in a Master Class

ERIC Educational Resources Information Center

Hanken, Ingrid Maria

2015-01-01

Most higher education institutions offering programmes in music performance organise master classes for their students. It is commonly agreed that master classes offer valuable learning opportunities for the students performing, but what do they offer for students in the audience? In this article, theories of learning by observation as well as…

Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice

ERIC Educational Resources Information Center

Transue, Beth M.

2013-01-01

Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Back to the biology in systems biology: what can we learn from biomolecular networks?

PubMed

Huang, Sui

2004-02-01

Genome-scale molecular networks, including protein interaction and gene regulatory networks, have taken centre stage in the investigation of the burgeoning disciplines of systems biology and biocomplexity. What do networks tell us? Some see in networks simply the comprehensive, detailed description of all cellular pathways, others seek in networks simple, higher-order qualities that emerge from the collective action of the individual pathways. This paper discusses networks from an encompassing category of thinking that will hopefully help readers to bridge the gap between these polarised viewpoints. Systems biology so far has emphasised the characterisation of large pathway maps. Now one has to ask: where is the actual biology in 'systems biology'? As structures midway between genome and phenome, and by serving as an 'extended genotype' or an 'elementary phenotype', molecular networks open a new window to the study of evolution and gene function in complex living systems. For the study of evolution, features in network topology offer a novel starting point for addressing the old debate on the relative contributions of natural selection versus intrinsic constraints to a particular trait. To study the function of genes, it is necessary not only to see them in the context of gene networks, but also to reach beyond describing network topology and to embrace the global dynamics of networks that will reveal higher-order, collective behaviour of the interacting genes. This will pave the way to understanding how the complexity of genome-wide molecular networks collapses to produce a robust whole-cell behaviour that manifests as tightly-regulated switching between distinct cell fates - the basis for multicellular life.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

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

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

PubMed

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

Critical Thinking Theory to Practice: Using the Expert's Thought Process as Guide for Learning and Assessment.

PubMed

Marshall, Teresa A; Marchini, Leonardo; Cowen, Howard; Hartshorn, Jennifer E; Holloway, Julie A; Straub-Morarend, Cheryl L; Gratton, David; Solow, Catherine M; Colangelo, Nicholas; Johnsen, David C

2017-08-01

Critical thinking skills are essential for the successful dentist, yet few explicit skillsets in critical thinking have been developed and published in peer-reviewed literature. The aims of this article are to 1) offer an assessable critical thinking teaching model with the expert's thought process as the outcome, learning guide, and assessment instrument and 2) offer three critical thinking skillsets following this model: for geriatric risk assessment, technology decision making, and situation analysis/reflections. For the objective component, the student demonstrates delivery of each step in the thought process. For the subjective component, the student is judged to have grasped the principles as applied to the patient or case. This article describes the framework and the results of pilot tests in which students in one year at this school used the model in the three areas, earning scores of 90% or above on the assessments. The model was thus judged to be successful for students to demonstrate critical thinking skillsets in the course settings. Students consistently delivered each step of the thought process and were nearly as consistent in grasping the principles behind each step. As more critical thinking skillsets are implemented, a reinforcing network develops.

Teen Science Cafés: A Model for Addressing Broader Impacts, Diversity, and Recruitment

NASA Astrophysics Data System (ADS)

Hall, M.; Mayhew, M. A.

2017-12-01

Teen Science Café programs (TeenScienceCafe.org) are a free and fun way for teens to explore science and technology affecting their lives. Through lively presentations, conversation, and activities to explore a topic deeply, Café programs open doors for teens to learn from experts about exciting and rewarding STEM career pathways. The programs are local and led by teens with the help of an adult mentor. The Teen Science Café Network (teensciencecafe.org) provides mentoring and resources, including small grants, to help organizations get started with and then maintain successful "teen café" programs. Through membership in the Network, more than 80 Teen Science Cafés have sprung up across the country, from rural towns to major cities. They serve a critical need for teens - meeting and engaging with STEM professionals, learning about their career paths, and seeing their passion for the work they do. Teen Science Café programs can offer geoscience departments a substantive, yet low cost, way to meet the challenges many of them face: finding ways to increase enrollment, helping faculty satisfy the broader impacts requirements of funding agencies, connecting with the surrounding communities, and providing opportunities for faculty and graduate students to learn how to communicate their science effectively to the public audience. The typical experience of scientists who have presented in teen cafés throughout the Network is that the communication skills learned spill over into their courses, proposals, and presentations to administrators and program officers. A department might partner with one or more organizations in their surrounding communities—libraries, for example—and engage its faculty and its graduate students—and even its undergraduates—in providing geoscience programming across multiple disciplines to local teens. Besides the internal benefits to the department's personnel and the value of establishing connections with community organizations, the impact of such engagement might well be attracting students to the department. We seek geoscience departments that are interested in this concept and willing to join the Teen Science Café Network (TeenScienceCafe.org) and participate in a study of how Teen Science Cafés may impact undergraduate recruitment to their departments.

A forecast-based STDP rule suitable for neuromorphic implementation.

PubMed

Davies, S; Galluppi, F; Rast, A D; Furber, S B

2012-08-01

Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that the STDP TTS algorithm allows the neural network to adapt and detect the incoming pattern with improvements both in the reliability of, and the time required for, consistent output. Through the approximations we suggest in this paper, we introduce a learning rule that is easy to implement both in event-driven simulators and in dedicated hardware, reducing computational complexity relative to the standard STDP rule. Such a rule offers a promising solution, complementary to standard STDP evaluation algorithms, for real-time learning using spiking neural networks in time-critical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

The end of an era? Midwifery conferences.

PubMed

Vilain, Annette Dalsgaard; Stewart, Sarah

2012-12-01

It has long been accepted that conferences are a useful mode of continuous professional development (CPD) (Russell 2010). Midwives welcome the chance to learn about recent practice developments, and the opportunity to network with each other in a face to face environment. However, barriers such as geographical isolation, time and financial constraints restrict midwives' ability to attend conferences (McIntosh 2007; Patterson and Davis 2007). At the same time, the effectiveness of conferences for CPD has been questioned (Guskey 2000). In these days of financial retrenchment, CPD has to be innovative and creative, offering ongoing support and learning in communities of practice that meet individual learning needs. The Virtual International Day of the Midwife (VIDM) is one such innovation. It is an annual 24 hour international synchronous online conference that celebrates the International Day of the Midwife on 5th May, and is freely open to all. Using the VIDM as a case study, this article discusses how online conferences may support and provide CPD for midwives.

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

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…

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

ERIC Educational Resources Information Center

Harding, Ansie; Engelbrecht, Johann

2015-01-01

"Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

A Role for YouTube in Telerehabilitation

PubMed Central

Manasco, M. Hunter; Barone, Nicholas; Brown, Amanda

2010-01-01

YouTube (http://youtube.com) is a free video sharing website that allows users to post and view videos. Although there are definite limitations in the applicability of this website to telerehabilitation, the YouTube technology offers potential uses that should not be overlooked. For example, some types of therapy, such as errorless learning therapy for certain language and cognitive deficits can be provided remotely via YouTube. In addition, the website’s social networking capabilities, via the asynchronous posting of comments and videos in response to posted videos, enables individuals to gain valuable emotional support by communicating with others with similar health and rehabilitation challenges. This article addresses the benefits and limitations of YouTube in the context of telerehabilitation and reports patient feedback on errorless learning therapy for aphasia delivered via videos posted on YouTube. PMID:25945173

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

NASA Astrophysics Data System (ADS)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

Experience preferred: insights from our newest public health professionals on how internships/practicums promote career development.

PubMed

Hernandez, Kristen E; Bejarano, Sandra; Reyes, Francis J; Chavez, Margarita; Mata, Holly

2014-01-01

Universities offering undergraduate degrees in health promotion or health education and/or graduate degrees in public health typically require an internship, practicum, or fieldwork experience. This type of mentored experience is an important aspect of career development for the next generation of public health professionals and benefits not only the students but also the profession and the communities in which they work. This article provides perspectives from four public health professionals who have recently graduated from designated minority-serving institutions and highlights the ways in which internship, practicum, or fieldwork experiences have contributed to their career development. From a career development perspective, internships provide unique opportunities to develop professional networks, practice competencies learned in the classroom, gain experience in different environments, and share lessons learned with others in our field. The diversification of the public health research and practice workforce is increasingly recognized as crucial in building health equity. Internship programs that focus specifically on the academic and professional development of students underrepresented in public health provide experiences that meet or supplement academic requirements, and provide students with real-world experience and an expanded network of mentors and role models.

Recovery in Scotland: Beyond service development

PubMed Central

2012-01-01

Over the last ten years there has been significant activity related to the promotion and support of recovery in Scotland, much of it linked to the work of the Scottish Recovery Network. A range of government policies have consistently identified recovery as a guiding principle of both service design and mental health improvement efforts. New learning has been developed and shared, workforce competencies reviewed and training developed, and a range of national initiatives put in place. In Scotland, as elsewhere, these efforts have tended to focus primarily on ensuring that mental health services offer environments and practices that support personal recovery. While service improvement is crucial, a wider challenge is ensuring that opportunities and support for self-directed recovery are enhanced outside statutory services. Providing examples, this paper will look at the development of recovery in Scotland – including the work of the Scottish Recovery Network – and consider the potential for building on progress made by rebalancing efforts to support personal recovery, highlighting the importance of public attitudes and community-based learning approaches. We will also touch on the role of identity in personal recovery and consider cultural issues related to the promotion of recovery in Scotland. PMID:22385428

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.

How and What Do Academics Learn through Their Personal Networks

ERIC Educational Resources Information Center

Pataraia, Nino; Margaryan, Anoush; Falconer, Isobel; Littlejohn, Allison

2015-01-01

This paper investigates the role of personal networks in academics' learning in relation to teaching. Drawing on in-depth interviews with 11 academics, this study examines, first, how and what academics learn through their personal networks; second, the perceived value of networks in relation to academics' professional development; and, third,…

Statewide Work-Based Learning Intermediary Network: Fiscal Year 2014 Report

ERIC Educational Resources Information Center

Iowa Department of Education, 2014

2014-01-01

The Statewide Work-based Learning Intermediary Network Fiscal Year 2014 Report summarizes fiscal year 2014 (FY14) work-based learning activities of the 15 regional intermediary networks. This report includes activities which occurred between October 1, 2013, to June 30, 2014. It is notable that some intermediary regional networks have been in…

Networking for Teacher Learning: Toward a Theory of Effective Design.

ERIC Educational Resources Information Center

McDonald, Joseph P.; Klein, Emily J.

2003-01-01

Examines how teacher networks design for teacher learning, describing several dynamic tensions inherent in the designs of a sample of teacher networks and assessing the relationships of these tensions to teacher learning. The paper illustrates these design concepts with reference to the work of seven networks that aim to revamp teacher' knowledge…

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Network reciprocity by coexisting learning and teaching strategies

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Brede, Markus; Yamauchi, Atsuo

2012-03-01

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

Peer Apprenticeship Learning in Networked Learning Communities: The Diffusion of Epistemic Learning

ERIC Educational Resources Information Center

Jamaludin, Azilawati; Shaari, Imran

2016-01-01

This article discusses peer apprenticeship learning (PAL) as situated within networked learning communities (NLCs). The context revolves around the diffusion of technologically-mediated learning in Singapore schools, where teachers begin to implement inquiry-oriented learning, consistent with 21st century learning, among students. As these schools…

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

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

Just the Facts: Personal Learning Networks

ERIC Educational Resources Information Center

Nussbaum-Beach, Sheryl

2013-01-01

One has heard about personal learning networks (PLNs), but what are they and how are they different than professional learning communities (PLCs)? Find out how PLNs can help a teacher pursue his/her own professional interests and be a better teacher. This article answers questions related to PLNs such as: (1) What are personal learning networks?;…

A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory.

PubMed

Chicca, E; Badoni, D; Dante, V; D'Andreagiovanni, M; Salina, G; Carota, L; Fusi, S; Del Giudice, P

2003-01-01

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

PubMed

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

Meeting the Needs of Rural Special Education in the Information Age: Using TI-IN Network's Interactive Satellite Based Educational Network.

ERIC Educational Resources Information Center

Pease, Pamela S.; Kitchen, Lillian

The TI-IN Network is an interactive, satellite-based educational system offering a technological alternative to face-to-face classroom instruction. Developed through a cooperative venture between private enterprise and public education agencies, the TI-IN Network offers a total systems approach by providing the entire programming and hardware…

Dynamite Networking for Dynamite Jobs. 101 Interpersonal, Telephone and Electronic Techniques for Getting Job Leads, Interviews and Offers.

ERIC Educational Resources Information Center

Krannich, Caryl Rae; Krannich, Ronald L.

This book guides job seekers in using communication approaches that will generate useful information, advice, and referrals that lead to job interviews and offers. The book provides guidance on how to do the following: organize effective job networks; prospect for job leads; write networking letters; make cold calls; join electronic networks;…

Changing the culture of academic medicine: the C-Change learning action network and its impact at participating medical schools.

PubMed

Krupat, Edward; Pololi, Linda; Schnell, Eugene R; Kern, David E

2013-09-01

The culture of academic medicine has been described as hierarchical, competitive, and not highly supportive of female or minority faculty. In response to this, the authors designed the Learning Action Network (LAN), which was part of the National Initiative on Gender, Culture and Leadership in Medicine (C-Change). The LAN is a five-school consortium aimed at changing the organizational culture of its constituent institutions. The authors selected LAN schools to be geographically diverse and representative of U.S. medical schools. Institutional leaders and faculty representatives from constituent schools met twice yearly for four years (2006-2010), forming a cross-institutional learning community. Through their quarterly listing of institutional activities, schools reported a wide array of actions. Most common were increased faculty development and/or mentoring, new approaches to communication, and adoption of new policies and procedures. Other categories included data collection/management, engagement of key stakeholders, education regarding gender/diversity, and new/expanded leadership positions. Through exit interviews, most participants reported feeling optimistic about maintaining the momentum of change. However, some, especially in schools with leadership changes, expressed uncertainty. Participants reported that they felt that the LAN enabled, empowered, facilitated, and/or caused the reported actions.For others who might want to work toward changing the culture of academic medicine, the authors offer several lessons learned from their experiences with C-Change. Most notably, people, structures, policies, and reward systems must be put into place to support cultural values, and broad-based support should be created in order for changes to persist when inevitable transitions in leadership occur.

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

PubMed Central

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B.

2018-01-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support. PMID:29629431

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices.

PubMed

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B

2017-06-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

VLSI Implementation of Neuromorphic Learning Networks

DTIC Science & Technology

1993-03-31

AND DATES COVEREDFINAL/O1 AUG 90 TO 31 MAR 93 4. TITLE AND SUBTII1L S. FUNDING NUMBERS VLSI IMPLEMENTATION OF NEUROMORPHIC LEARNING NETWORKS (U) 6...Standard Form 298 (Rev 2-89) rtrfbc byv nN$I A Z’Si - 8 9- A* qip. COVER SHEET VLSI Implementation of Neuromorphic Learning Networks Contract Number... Neuromorphic Learning Networks Sponsored by Defense Advanced Research Projects Agency DARPA Order No. 7013 Monitored by AFOSR Under Contract No. F49620-90-C

Learning Technology Adoption: Navy Barriers And Resistance

DTIC Science & Technology

2018-03-01

ABSTRACT (maximum 200 words) Technological developments offer opportunities to enhance training effectiveness, in support of achieving high-velocity...developments offer opportunities to enhance training effectiveness, in support of achieving high-velocity learning. However, resistance to change...considering what opportunities for enhanced training might be offered by learning-centered technologies. This is evident in the CNO’s statement, “We must

The African Field Epidemiology Network-Networking for effective field epidemiology capacity building and service delivery

PubMed Central

Gitta, Sheba Nakacubo; Mukanga, David; Babirye, Rebecca; Dahlke, Melissa; Tshimanga, Mufuta; Nsubuga, Peter

2011-01-01

Networks are a catalyst for promoting common goals and objectives of their membership. Public Health networks in Africa are crucial, because of the severe resource limitations that nations face in dealing with priority public health problems. For a long time, networks have existed on the continent and globally, but many of these are disease-specific with a narrow scope. The African Field Epidemiology Network (AFENET) is a public health network established in 2005 as a non-profit networking alliance of Field Epidemiology and Laboratory Training Programs (FELTPs) and Field Epidemiology Training Programs (FETPs) in Africa. AFENET is dedicated to helping ministries of health in Africa build strong, effective and sustainable programs and capacity to improve public health systems by partnering with global public health experts. The Network's goal is to strengthen field epidemiology and public health laboratory capacity to contribute effectively to addressing epidemics and other major public health problems in Africa. AFENET currently networks 12 FELTPs and FETPs in sub-Saharan Africa with operations in 20 countries. AFENET has a unique tripartite working relationship with government technocrats from human health and animal sectors, academicians from partner universities, and development partners, presenting the Network with a distinct vantage point. Through the Network, African nations are making strides in strengthening their health systems. Members are able to: leverage resources to support field epidemiology and public health laboratory training and service delivery notably in the area of outbreak investigation and response as well as disease surveillance; by-pass government bureaucracies that often hinder and frustrate development partners; and consolidate efforts of different partners channelled through the FELTPs by networking graduates through alumni associations and calling on them to offer technical support in various public health capacities as the need arises. AFENET presents a bridging platform between governments and the private sector, allowing for continuity of health interventions at the national and regional level while offering free exit and entry for existing and new partners respectively. AFENET has established itself as a versatile networking model that is highly responsive to members’ needs. Based on the successes recorded in AFENET's first 5 years, we envision that the Network's membership will continue to expand as new training programs are established. The lessons learned will be useful in initiating new programs and building sustainability frameworks for FETPs and FELTPs in Africa. AFENET will continue to play a role in coordinating, advocacy, and building capacity for epidemic disease preparedness and response. PMID:22359691

Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

ERIC Educational Resources Information Center

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel; Alberdi, Mikel

2016-01-01

The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

Learning oncogenetic networks by reducing to mixed integer linear programming.

PubMed

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

ERIC Educational Resources Information Center

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

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

ERIC Educational Resources Information Center

Sackey, Shera Carter

2012-01-01

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

Informal and Formal Support Groups Retain Women and Minorities in Physics

NASA Astrophysics Data System (ADS)

Ong, Maria

2005-10-01

Ten U.S. minority female undergraduates who aspire to become physicists were followed over an 8-year period. Participant observation and in-depth interviews recorded the strategies they used to earn bachelor's degrees in physics or physics-related fields, and then go on to graduate school and/or careers in science. One significant strategy these women of color employed was participating in small subcommunities with other women or underrepresented ethnic minorities at the margins of their local physics community. The study found that informal peer groups offered safe spaces to counter negative experiences, to normalize their social realities, and to offer practical guidance for persevering in the field. Formal women- and minority-serving programs in physics provided foundations for community building, stronger curriculum and instruction, networking, and role models. The positive effects of informal and formal support groups on these students' experiences challenge a standard application of Pierre Bourdieu's framework of social and cultural capital. Women of color in the study initially lacked traditional capital of "acceptable" appearance, cultural background and habits, and networks that are more easily acquired by white males and are rewarded by the U.S. physics culture. However, instead of failing or leaving, as Bourdieu's theory would predict, the minority women persisted and achieved in science. The marginal communities contributed to their retention by offering safe spaces in which they could learn and share alternative ways of "accruing capital." Moreover, as these women made strides along their academic and career paths, they also engaged in social justice work in efforts to change the physics culture to be more welcoming of nontraditional members. The outcomes of the study offer empirical confirmation of the critical need for informal and institutionally supported women's and minorities' support groups to promote diversity in science.

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

PubMed Central

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. PMID:25837826

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

PubMed

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.

Language Views on Social Networking Sites for Language Learning: The Case of Busuu

ERIC Educational Resources Information Center

Álvarez Valencia, José Aldemar

2016-01-01

Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

Project Kaleidoscope: Advancing What Works in Undergraduate STEM Education

NASA Astrophysics Data System (ADS)

Elrod, S.

2011-12-01

In 1989, Project Kaleidoscope (PKAL) published its first report, What Works: Building Natural Science Communities, on reforming undergraduate STEM (science, technology, engineering and mathematics) education. Since then, PKAL has grown into a national organization comprised of a diverse group of over 6500 STEM educators who are committed to advancing "what works." The PKAL mission is to be a national leader in catalyzing the efforts of people, institutions, organizations and networks to move from analysis to action in significantly improving undergraduate student learning and achievement in STEM (science, technology, engineering and mathematics). Specifically, PKAL's strategic goals are to: 1) Promote the development and wider use of evidence-based teaching, learning and assessment approaches, 2) Build individual and organizational capacity to lead change in STEM education, and 3) Engage the broader community of external stakeholders - professional and disciplinary societies, business and industry groups, accreditation organizations, educational associations, governmental agencies, philanthropic organizations - in achieving our mission. PKAL achieves these goals by serving as the nexus of an interconnected and multidisciplinary web of people, ideas, strategies, evidence and resources focused on systemic change in undergraduate STEM education. PKAL also provides resources on critical issues, such as teaching using pedagogies of engagement, and engages interested faculty, campuses and professional societies in national projects and programs focused on cutting edge issues in STEM education. One of these projects - Mobilizing Disciplinary Societies for a Sustainable Future - is engaging eleven disciplinary societies, including the National Association of Geoscience Teachers, in defining specific resources, faculty development programs and goals focused on promoting undergraduate STEM courses that: 1) provide more knowledge about real-world issues; 2) connect these real-world issues to the concepts of sustainability; 3) offer students opportunities to analyze and implement choices that can help solve societal problems so they are better able to act on their choices both immediately and as future citizens and professionals. PKAL has also been offering leadership institutes for STEM faculty members to develop their knowledge and skills as change agents who have the capacity to lead educational reform at their institutions. Since 1996, over 200 faculty members from across the STEM disciplines have attended the institutes. An analysis of leadership alumni indicates that nearly 40% have moved on to administrative leadership positions. Alumni of these institutes are now leading regional STEM reform networks in five locations around the U.S. Since 2007, PKAL networks have engaged nearly 650 STEM faculty and campus leaders from over 100 diverse institutions in professional development workshops focused on STEM reform teaching and learning to effect a wider reach of STEM education transformation on campuses where it matters most. Network expertise and resources are disseminated on PKAL's website and national meetings. These programs illustrate PKAL's efforts to build community and disseminate resources that have a national impact on advancing undergraduate STEM teaching, learning and success for all students.

Brain and Social Networks: Fundamental Building Blocks of Human Experience.

PubMed

Falk, Emily B; Bassett, Danielle S

2017-09-01

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

PubMed

Valt, Christian; Klein, Christoph; Boehm, Stephan G

2015-08-01

Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

How to Trigger Emergence and Self-Organisation in Learning Networks

NASA Astrophysics Data System (ADS)

Brouns, Francis; Fetter, Sibren; van Rosmalen, Peter

The previous chapters of this section discussed why the social structure of Learning Networks is important and present guidelines on how to maintain and allow the emergence of communities in Learning Networks. Chapter 2 explains how Learning Networks rely on social interaction and active participations of the participants. Chapter 3 then continues by presenting guidelines and policies that should be incorporated into Learning Network Services in order to maintain existing communities by creating conditions that promote social interaction and knowledge sharing. Chapter 4 discusses the necessary conditions required for knowledge sharing to occur and to trigger communities to self-organise and emerge. As pointed out in Chap. 4, ad-hoc transient communities facilitate the emergence of social interaction in Learning Networks, self-organising them into communities, taking into account personal characteristics, community characteristics and general guidelines. As explained in Chap. 4 community members would benefit from a service that brings suitable people together for a specific purpose, because it will allow the participant to focus on the knowledge sharing process by reducing the effort or costs. In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

The Interactions of Relationships, Interest, and Self-Efficacy in Undergraduate Physics

NASA Astrophysics Data System (ADS)

Dou, Remy

This collected papers dissertation explores students' academic interactions in an active learning, introductory physics settings as they relate to the development of physics self-efficacy and interest. The motivation for this work extends from the national call to increase participation of students in the pursuit of science, technology, engineering, and mathematics (STEM) careers. Self-efficacy and interest are factors that play prominent roles in popular, evidence-based, career theories, including the Social cognitive career theory (SCCT) and the identity framework. Understanding how these constructs develop in light of the most pervasive characteristic of the active learning introductory physics classroom (i.e., peer-to-peer interactions) has implications on how students learn in a variety of introductory STEM classrooms and settings structured after constructivist and sociocultural learning theories. I collected data related to students' in-class interactions using the tools of social network analysis (SNA). Social network analysis has recently been shown to be an effective and useful way to examine the structure of student relationships that develop in and out of STEM classrooms. This set of studies furthers the implementation of SNA as a tool to examine self-efficacy and interest formation in the active learning physics classroom. Here I represent a variety of statistical applications of SNA, including bootstrapped linear regression (Chapter 2), structural equation modeling (Chapter 3), and hierarchical linear modeling for longitudinal analyses (Chapter 4). Self-efficacy data were collected using the Sources of Self-Efficacy for Science Courses - Physics survey (SOSESC-P), and interest data were collected using the physics identity survey. Data for these studies came from the Modeling Instruction sections of Introductory Physics with Calculus offered at Florida International University in the fall of 2014 and 2015. Analyses support the idea that students' perceptions of one another impact the development of their social network centrality, which in turn affects their self-efficacy building experiences and their overall self-efficacy. It was shown that unlike career theories that emphasize causal relationships between the development of self-efficacy and the subsequent growth of student interest, in this context student interest takes precedence before the development of student self-efficacy. This outcome also has various implications for career theories.

A Collaborative Diagonal Learning Network: The role of formal and informal professional development in elementary science reform

NASA Astrophysics Data System (ADS)

Cooke-Nieves, Natasha Anika

Science education research has consistently shown that elementary teachers have a low self-efficacy and background knowledge to teach science. When they teach science, there is a lack of field experiences and inquiry-based instruction at the elementary level due to limited resources, both material and pedagogical. This study focused on an analysis of a professional development (PD) model designed by the author known as the Collaborative Diagonal Learning Network (CDLN). The purpose of this study was to examine elementary school teacher participants pedagogical content knowledge related to their experiences in a CDLN model. The CDLN model taught formal and informal instruction using a science coach and an informal educational institution. Another purpose for this research included a theoretical analysis of the CDLN model to see if its design enabled teachers to expand their resource knowledge of available science education materials. The four-month-long study used qualitative data obtained during an in-service professional development program facilitated by a science coach and educators from a large natural history museum. Using case study as the research design, four elementary school teachers were asked to evaluate the effectiveness of their science coach and museum educator workshop sessions. During the duration of this study, semi-structured individual/group interviews and open-ended pre/post PD questionnaires were used. Other data sources included researcher field notes from lesson observations, museum field trips, audio-recorded workshop sessions, email correspondence, and teacher-created artifacts. The data were analyzed using a constructivist grounded theory approach. Themes that emerged included increased self-efficacy; increased pedagogical content knowledge; increased knowledge of museum education resources and access; creation of a professional learning community; and increased knowledge of science notebooking. Implications for formal and informal professional development in elementary science reform are offered. It is suggested that researchers investigate collaborative coaching through the lenses of organizational learning network theory, and develop professional learning communities with formal and informal educators; and that professional developers in city school systems and informal science institutions work in concert to produce more effective elementary teachers who not only love science but love teaching it.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Using deep learning to quantify the beauty of outdoor places.

PubMed

Seresinhe, Chanuki Illushka; Preis, Tobias; Moat, Helen Susannah

2017-07-01

Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not , combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as 'Coast', 'Mountain' and 'Canal Natural', man-made structures such as 'Tower', 'Castle' and 'Viaduct' lead to places being considered more scenic. Importantly, while scenes containing 'Trees' tend to rate highly, places containing more bland natural green features such as 'Grass' and 'Athletic Fields' are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments.

Enabling Broadband as Commodity within Access Networks: A QoS Recipe

NASA Astrophysics Data System (ADS)

Areizaga, Enrique; Foglar, Andreas; Elizondo, Antonio J.; Geilhardt, Frank

This paper describes the QoS features that will transform the access networks landscape in order to bring “Broadband” as a commodity while setting up the pillars of the “Future Media Internet”. Quality of Experience is obviously key for emerging and future services. Broadcasting services will first need to equal the QoE of their counterparts in the Open-air market (for IP-TV examples would be artifact-free, no picture freezing, fast zapping times) and offer new features often using interactivity (Time-shifted TV, access to more content, 3DTV with feeling of presence). The huge variety of communications alternatives will lead to different requirements per customer, whose needs will also be dependent on parameters like where the connection is made, the time of the day/day of the week/period of the year or even his/her mood. Today’s networks, designed for providing just Broadband connectivity, will not be enough to satisfy customer’s needs and will necessarily support the introduction of new and innovative services. The Networks of the future should learn from the way the users are communicating, what services they are using, where, when, and how, and adapt accordingly.

Vector Quantization Algorithm Based on Associative Memories

NASA Astrophysics Data System (ADS)

Guzmán, Enrique; Pogrebnyak, Oleksiy; Yáñez, Cornelio; Manrique, Pablo

This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.

Neural Network Control of a Magnetically Suspended Rotor System

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.

1998-01-01

Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

PubMed

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

2016-10-01

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

Developing an inter-organizational community-based health network: an Australian investigation.

PubMed

Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

2015-12-01

Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Food Web Designer: a flexible tool to visualize interaction networks.

PubMed

Sint, Daniela; Traugott, Michael

Species are embedded in complex networks of ecological interactions and assessing these networks provides a powerful approach to understand what the consequences of these interactions are for ecosystem functioning and services. This is mandatory to develop and evaluate strategies for the management and control of pests. Graphical representations of networks can help recognize patterns that might be overlooked otherwise. However, there is a lack of software which allows visualizing these complex interaction networks. Food Web Designer is a stand-alone, highly flexible and user friendly software tool to quantitatively visualize trophic and other types of bipartite and tripartite interaction networks. It is offered free of charge for use on Microsoft Windows platforms. Food Web Designer is easy to use without the need to learn a specific syntax due to its graphical user interface. Up to three (trophic) levels can be connected using links cascading from or pointing towards the taxa within each level to illustrate top-down and bottom-up connections. Link width/strength and abundance of taxa can be quantified, allowing generating fully quantitative networks. Network datasets can be imported, saved for later adjustment and the interaction webs can be exported as pictures for graphical display in different file formats. We show how Food Web Designer can be used to draw predator-prey and host-parasitoid food webs, demonstrating that this software is a simple and straightforward tool to graphically display interaction networks for assessing pest control or any other type of interaction in both managed and natural ecosystems from an ecological network perspective.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Distance Learning in a Multimedia Networks Project: Main Results.

ERIC Educational Resources Information Center

Ruokamo, Heli; Pohjolainen, Seppo

2000-01-01

Discusses a goal-oriented project, focused on open learning environments using computer networks, called Distance Learning in Multimedia Networks that was part of the Finnish Multimedia Program. Describes the combined efforts of Finnish telecommunications companies, content providers, publishing houses, hardware companies, and educational…

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Knowledgeable Lemurs Become More Central in Social Networks.

PubMed

Kulahci, Ipek G; Ghazanfar, Asif A; Rubenstein, Daniel I

2018-04-23

Strong relationships exist between social connections and information transmission [1-9], where individuals' network position plays a key role in whether or not they acquire novel information [2, 3, 5, 6]. The relationships between social connections and information acquisition may be bidirectional if learning novel information, in addition to being influenced by it, influences network position. Individuals who acquire information quickly and use it frequently may receive more affiliative behaviors [10, 11] and may thus have a central network position. However, the potential influence of learning on network centrality has not been theoretically or empirically addressed. To bridge this epistemic gap, we investigated whether ring-tailed lemurs' (Lemur catta) centrality in affiliation networks changed after they learned how to solve a novel foraging task. Lemurs who had frequently initiated interactions and approached conspecifics before the learning experiment were more likely to observe and learn the task solution. Comparing social networks before and after the learning experiment revealed that the frequently observed lemurs received more affiliative behaviors than they did before-they became more central after the experiment. This change persisted even after the task was removed and was not caused by the observed lemurs initiating more affiliative behaviors. Consequently, quantifying received and initiated interactions separately provides unique insights into the relationships between learning and centrality. While the factors that influence network position are not fully understood, our results suggest that individual differences in learning and becoming successful can play a major role in social centrality, especially when learning from others is advantageous. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Dynamic Three-Dimensional Network Visualization Program for Integration into CyberCIEGE and Other Network Visualization Scenarios

DTIC Science & Technology

2007-06-01

information flow involved in network attacks. This kind of information can be invaluable in learning how to best setup and defend computer networks...administrators, and those interested in learning about securing networks a way to conceptualize this complex system of computing. NTAV3D will provide a three...teaching with visual and other components can make learning more effective” (Baxley et al, 2006). A hyperbox (Alpern and Carter, 1991) is

Benefits of Cooperative Learning in Weblog Networks

ERIC Educational Resources Information Center

Wang, Jenny; Fang, Yuehchiu

2005-01-01

The purpose of this study was to explore the benefits of cooperative learning in weblog networks, focusing particularly on learning outcomes in college writing curriculum integrated with computer-mediated learning tool-weblog. The first section addressed the advantages of using weblogs in cooperative learning structure on teaching and learning.…

Investigating the Educational Value of Social Learning Networks: A Quantitative Analysis

ERIC Educational Resources Information Center

Dafoulas, Georgios; Shokri, Azam

2016-01-01

Purpose: The emergence of Education 2.0 enabled technology-enhanced learning, necessitating new pedagogical approaches, while e-learning has evolved into an instrumental pedagogy of collaboration through affordances of social media. Social learning networks and ubiquitous learning enabled individual and group learning through social engagement and…

Blended Learning: The Student Viewpoint.

PubMed

Shantakumari, N; Sajith, P

2015-01-01

Blended learning (BL) is defined as "a way of meeting the challenges of tailoring learning and development to the needs of individuals by integrating the innovative and technological advances offered by online learning with the interaction and participation offered in the best of traditional learning." The Gulf Medical University (GMU), Ajman, UAE, offers a number of courses which incorporate BL with contact classes and online component on an E-learning platform. Insufficient learning satisfaction has been stated as an obstacle to its implementation and efficacy. To determine the students' perceptions toward BL which in turn will determine their satisfaction and the efficacy of the courses offered. This was a cross-sectional study conducted at the GMU, Ajman between January and December 2013. Perceptions of BL process, content, and ease of use were collected from 75 students enrolled in the certificate courses offered by the university using a questionnaire. Student perceptions were assessed using Mann-Whitney U-test and Kruskal-Wallis test on the basis of gender, age, and course enrollment. The median scores of all the questions in the three domains were above three suggesting positive perceptions on BL. The distribution of perceptions was similar between gender and age. However, significant differences were observed in the course enrollment (P = 0.02). Students hold a positive perception of the BL courses being offered in this university. The difference in perceptions among students of different courses suggest that the BL format offered needs modification according to course content to improve its perception.

Psychology for the Classroom: E-Learning

ERIC Educational Resources Information Center

Woollard, John

2011-01-01

"Psychology for the Classroom: E-Learning" is a lively and accessible introduction to the field of technology-supported teaching and learning and the educational psychology associated with those developments. Offering a substantial and useful analysis of e-learning, this practical book includes current research, offers a grounding in both theory…

How Learning Mathematics Can Be Made More Enjoyable

ERIC Educational Resources Information Center

D'Apice, Ciro; Manzo, Rosanna

2004-01-01

New information technologies can act as a Trojan horse offering activities that will require major changes in the teaching-learning process. Computer aided learning applications are able to offer advanced students the opportunity to improve their skills and to maintain their motivation. In the spirit of "learning by doing", they are…

Astronautics Degrees for Space Industry

NASA Astrophysics Data System (ADS)

Gruntman, M.; Brodsky, R.; Erwin, D.; Kunc, J.

The Astronautics Program (http://astronautics.usc.edu) of the University of Southern California (USC) offers a full set of undergraduate and graduate degree programs in Aerospace Engineering with emphasis in Astronautics. The Bachelor of Science degree program in Astronautics combines basic science and engineering classes with specialized astronautics classes. The Master of Science degree program in Astronautics offers classes in various areas of space technology. The Certificate in Astronautics targets practicing engineers and scientists who enter space-related fields and/or who want to obtain training in specific space-related areas. Many specialized graduate classes are taught by adjunct faculty working at the leading space companies. The Master of Science degree and Certificate are available through the USC Distance Education Network (DEN). Today, the Internet allows us to reach students anywhere in the world through webcasting. The majority of our graduate students, as well as those pursuing the Certificate, work full time as engineers in the space industry and government research and development centers. The new world of distance learning presents new challenges and opens new opportunities. We show how the transformation of distance learning and particularly the introduction of webcasting transform organization of the program and class delivery. We will describe in detail the academic focus of the program, student reach, and structure of program components. Program development is illustrated by the student enrollment dynamics and related industrial trends; the lessons learned emphasize the importance of feedback from the students and from the space industry.

Neuroeconomics of attention-deficit/hyperactivity disorder: differential influences of medial, dorsal, and ventral prefrontal brain networks on suboptimal decision making?

PubMed

Sonuga-Barke, Edmund J S; Fairchild, Graeme

2012-07-15

Psychiatric neuroeconomics offers an alternative approach to understanding mental disorders by studying the way disorder-related neurobiological alterations constrain economic agency, as revealed through decisions about choices between future goods. In this article, we apply this perspective to understand suboptimal decision making in attention-deficit/hyperactivity disorder (ADHD) by integrating recent advances in the neuroscience of decision making and studies of the pathophysiology of ADHD. We identify three brain networks as candidates for further study and develop specific hypotheses about how these could be implicated in ADHD. First, we postulate that altered patterns of connectivity within a network linking medial prefrontal cortex and posterior cingulate cortex (i.e., the default mode network) disrupts ordering of utilities, prospection about desired future states, setting of future goals, and implementation of aims. Second, we hypothesize that deficits in dorsal frontostriatal networks, including the dorsolateral prefrontal cortex and dorsal striatum, produce executive dysfunction-mediated impairments in the ability to compare outcome options and make choices. Third, we propose that dopaminergic dysregulation in a ventral frontostriatal network encompassing the orbitofrontal cortex, ventral striatum, and amygdala disrupts processing of cues of future utility, evaluation of experienced outcomes (feedback), and learning of associations between cues and outcomes. Finally, we extend this perspective to consider three contemporary themes in ADHD research. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

High Speed Computing, LANs, and WAMs

NASA Technical Reports Server (NTRS)

Bergman, Larry A.; Monacos, Steve

1994-01-01

Optical fiber networks may one day offer potential capacities exceeding 10 terabits/sec. This paper describes present gigabit network techniques for distributed computing as illustrated by the CASA gigabit testbed, and then explores future all-optic network architectures that offer increased capacity, more optimized level of service for a given application, high fault tolerance, and dynamic reconfigurability.

A pharmacy course on leadership and leading change.

PubMed

Sorensen, Todd D; Traynor, Andrew P; Janke, Kristin K

2009-04-07

To develop and implement a course that develops pharmacy students' leadership skills and encourages them to become leaders within the profession. A leadership course series was offered to pharmacy students on 2 campuses. The series incorporated didactic, experiential, and self-directed learning activities, and focused on developing core leadership skills, self-awareness, and awareness of the process for leading change. Students reported increased knowledge and confidence in their ability to initiate and lead efforts for change. The learning activities students' valued most were the StrengthsFinder assessment (67% of students rated "very useful") and a Leadership Networking Partners (LNP) program (83% of students rated "very useful"). Teaching leadership skills poses a significant challenge in curriculum development and requires multifaceted course design elements that resonate with students and engage the practice community. Addressing these requirements results in a high level of student engagement and a desire to continue the development of leadership skills.

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

PubMed Central

Merk, Daniel; Friedrich, Lukas; Grisoni, Francesca

2018-01-01

Abstract Generative artificial intelligence offers a fresh view on molecular design. We present the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine‐tuned on recognizing retinoid X and peroxisome proliferator‐activated receptor agonists. We synthesized five top‐ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low‐micromolar receptor modulatory activity in cell‐based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry. PMID:29319225

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information.

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

Aimone, James Bradley; Bernard, Michael Lewis; Vineyard, Craig Michael

2014-10-01

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach bymore » which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.« less

Lessons Learned from the Evolution of an Academic Community Partnership: Creating "Patient Voices".

PubMed

Chambers, Meghan K; Ireland, Anna; D'Aniello, Rona; Lipnicki, Stephanie; Glick, Myron; Tumiel-Berhalter, Laurene

2015-01-01

Long-term partners received federal funding to develop the Patient Voices Network, a partnership of safety-net family practices and their patients to develop health improvement strategies. The scope and structure of the newly funded grant presented unexpected challenges that threatened the future of the partnership.Purpose of Article: To present a case study of the evolution of an existing partnership and offer lessons learned along with recommendations for future partnerships. Federal funding formalized the partnership in a way that required looking at it through a new lens. Leadership, programmatic, personnel, and financial challenges emerged. Short-term and long-term strategies were applied to address evolving needs. This case study demonstrates how federal funding raises the bar for academic-community partnerships and how challenges can be worked through, particularly if the partnership embraces the key principles of community-based participatory research (CBPR). Recommendations have been applied successfully to future initiatives.

The application of network teaching in applied optics teaching

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Advanced Optical Burst Switched Network Concepts

NASA Astrophysics Data System (ADS)

Nejabati, Reza; Aracil, Javier; Castoldi, Piero; de Leenheer, Marc; Simeonidou, Dimitra; Valcarenghi, Luca; Zervas, Georgios; Wu, Jian

In recent years, as the bandwidth and the speed of networks have increased significantly, a new generation of network-based applications using the concept of distributed computing and collaborative services is emerging (e.g., Grid computing applications). The use of the available fiber and DWDM infrastructure for these applications is a logical choice offering huge amounts of cheap bandwidth and ensuring global reach of computing resources [230]. Currently, there is a great deal of interest in deploying optical circuit (wavelength) switched network infrastructure for distributed computing applications that require long-lived wavelength paths and address the specific needs of a small number of well-known users. Typical users are particle physicists who, due to their international collaborations and experiments, generate enormous amounts of data (Petabytes per year). These users require a network infrastructures that can support processing and analysis of large datasets through globally distributed computing resources [230]. However, providing wavelength granularity bandwidth services is not an efficient and scalable solution for applications and services that address a wider base of user communities with different traffic profiles and connectivity requirements. Examples of such applications may be: scientific collaboration in smaller scale (e.g., bioinformatics, environmental research), distributed virtual laboratories (e.g., remote instrumentation), e-health, national security and defense, personalized learning environments and digital libraries, evolving broadband user services (i.e., high resolution home video editing, real-time rendering, high definition interactive TV). As a specific example, in e-health services and in particular mammography applications due to the size and quantity of images produced by remote mammography, stringent network requirements are necessary. Initial calculations have shown that for 100 patients to be screened remotely, the network would have to securely transport 1.2 GB of data every 30 s [230]. According to the above explanation it is clear that these types of applications need a new network infrastructure and transport technology that makes large amounts of bandwidth at subwavelength granularity, storage, computation, and visualization resources potentially available to a wide user base for specified time durations. As these types of collaborative and network-based applications evolve addressing a wide range and large number of users, it is infeasible to build dedicated networks for each application type or category. Consequently, there should be an adaptive network infrastructure able to support all application types, each with their own access, network, and resource usage patterns. This infrastructure should offer flexible and intelligent network elements and control mechanism able to deploy new applications quickly and efficiently.

Developing 21st century skills through the use of student personal learning networks

NASA Astrophysics Data System (ADS)

Miller, Robert D.

This research was conducted to study the development of 21st century communication, collaboration, and digital literacy skills of students at the high school level through the use of online social network tools. The importance of this study was based on evidence high school and college students are not graduating with the requisite skills of communication, collaboration, and digital literacy skills yet employers see these skills important to the success of their employees. The challenge addressed through this study was how high schools can integrate social network tools into traditional learning environments to foster the development of these 21st century skills. A qualitative research study was completed through the use of case study. One high school class in a suburban high performing town in Connecticut was selected as the research site and the sample population of eleven student participants engaged in two sets of interviews and learned through the use social network tools for one semester of the school year. The primary social network tools used were Facebook, Diigo, Google Sites, Google Docs, and Twitter. The data collected and analyzed partially supported the transfer of the theory of connectivism at the high school level. The students actively engaged in collaborative learning and research. Key results indicated a heightened engagement in learning, the development of collaborative learning and research skills, and a greater understanding of how to use social network tools for effective public communication. The use of social network tools with high school students was a positive experience that led to an increased awareness of the students as to the benefits social network tools have as a learning tool. The data supported the continued use of social network tools to develop 21st century communication, collaboration, and digital literacy skills. Future research in this area may explore emerging social network tools as well as the long term impact these tools have on the development of lifelong learning skills and quantitative data linked to student learning.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

ERIC Educational Resources Information Center

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

Material Matters for Learning in Virtual Networks: A Case Study of a Professional Learning Programme Hosted in a Google+ Online Community

ERIC Educational Resources Information Center

Ackland, Aileen; Swinney, Ann

2015-01-01

In this paper, we draw on Actor-Network Theories (ANT) to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data…

Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

ERIC Educational Resources Information Center

Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

2015-01-01

The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

PubMed

Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Sharma, Alok; Paliwal, Kuldip; Sattar, Abdul; Zhou, Yaoqi; Yang, Yuedong

2014-10-30

Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. Copyright © 2014 Wiley Periodicals, Inc.

The Brain Network Underpinning Novel Melody Creation.

PubMed

Adhikari, Bhim M; Norgaard, Martin; Quinn, Kristen M; Ampudia, Jenine; Squirek, Justin; Dhamala, Mukesh

2016-12-01

Musical improvisation offers an excellent experimental paradigm for the study of real-time human creativity. It involves moment-to-moment decision-making, monitoring of one's performance, and utilizing external feedback to spontaneously create new melodies or variations on a melody. Recent neuroimaging studies have begun to study the brain activity during musical improvisation, aiming to unlock the mystery of human creativity. What brain resources come together and how these are utilized during musical improvisation are not well understood. To help answer these questions, we recorded electroencephalography (EEG) signals from 19 experienced musicians while they played or imagined short isochronous learned melodies and improvised on those learned melodies. These four conditions (Play-Prelearned, Play-Improvised, Imagine-Prelearned, Imagine-Improvised) were randomly interspersed in a total of 300 trials per participant. From the sensor-level EEG, we found that there were power differences in the alpha (8-12 Hz) and beta (13-30 Hz) bands in separate clusters of frontal, parietal, temporal, and occipital electrodes. Using EEG source localization and dipole modeling methods for task-related signals, we identified the locations and network activities of five sources: the left superior frontal gyrus (L SFG), supplementary motor area (SMA), left inferior parietal lobule (L IPL), right dorsolateral prefrontal cortex, and right superior temporal gyrus. During improvisation, the network activity between L SFG, SMA, and L IPL was significantly less than during the prelearned conditions. Our results support the general idea that attenuated cognitive control facilitates the production of creative output.

Edmodo social learning network for elementary school mathematics learning

NASA Astrophysics Data System (ADS)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

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.

Learning Without Boundaries: A NASA - National Guard Bureau Distance Learning Partnership

NASA Technical Reports Server (NTRS)

Anderson, Susan H.; Chilelli, Christopher J.; Picard, Stephan

2003-01-01

With a variety of high-quality live interactive educational programs originating at the Johnson Space Center in Houston, Texas and other space and research centers, the US space agency NASA (National Aeronautics and Space Administration) has a proud track record of connecting with students throughout the world and stimulating their creativity and collaborative skills by teaching them underlying scientific and technological underpinnings of space exploration. However, NASA desires to expand its outreach capability for this type of interactive instruction. In early 2002, NASA and the National Guard Bureau -- using the Guard's nationwide system of state-ofthe-art classrooms and high bandwidth network -- began a collaboration to extend the reach of NASA content and educational programs to more of America's young people. Already, hundreds of elementary, middle, and high school students have visited Guard e-Learning facilities and participated in interactive NASA learning events. Topics have included experimental flight, satellite imagery-interpretation, and Mars exploration. Through this partnership, NASA and the National Guard are enabling local school systems throughout the United States (and, increasingly, the world) to use the excitement of space flight to encourage their students to become passionate about the possibility of one day serving as scientists, mathematicians, technologists, and engineers. At the 54th International Astronautical Conference MAJ Stephan Picard, the guiding visionary behind the Guard's partnership with NASA, and Chris Chilelli, an educator and senior instructional designer at NASA, will share with attendees background on NASA's educational products and the National Guard's distributed learning network; will discuss the unique opportunity this partnership already has provided students and teachers throughout the United States; will offer insights into the formation by government entities of e-Learning partnerships with one another; and will suggest a possible future for the NASA - National Guard Bureau partnership, one potentially to include live multi-party interaction of hundreds of students in several countries with astronauts, scientists, engineers and designers. To inspire the next generation of explorers as only NASA can!

Summary of Research on Online and Blended Learning Programs That Offer Differentiated Learning Options. REL 2017-228

ERIC Educational Resources Information Center

Brodersen, R. Marc; Melluzzo, Daniel

2017-01-01

This report summarizes the methodology, measures, and findings of research on the influence on student achievement outcomes of K-12 online and blended face-to-face and online learning programs that offer differentiated learning options. The report also describes the characteristics of the learning programs. Most of the examined programs used…

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.

Towards a Social Networks Model for Online Learning & Performance

ERIC Educational Resources Information Center

Chung, Kon Shing Kenneth; Paredes, Walter Christian

2015-01-01

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

Social Networks, Communication Styles, and Learning Performance in a CSCL Community

ERIC Educational Resources Information Center

Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

2007-01-01

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

Networked Learning for Agricultural Extension: A Framework for Analysis and Two Cases

ERIC Educational Resources Information Center

Kelly, Nick; Bennett, John McLean; Starasts, Ann

2017-01-01

Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)- mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

Neural networks for self-learning control systems

NASA Technical Reports Server (NTRS)

Nguyen, Derrick H.; Widrow, Bernard

1990-01-01

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Lessons learned in building a global information network on chemicals (GINC).

PubMed

Kaminuma, Tsuguchika

2005-09-01

The Global Information Network on Chemicals (GINC) was a project to construct a worldwide information network linking international, national, and other organizations working for the safe management of chemicals. Proposed in 1993, the project started the next year and lasted almost 10 years. It was begun as a joint project of World Health Organization (WHO), International Labor Organization (ILO), and United Nations Environment Program (UNEP), and later endorsed by the Intergovernmental Forum on Chemical Safety (IFCS). Asia, particularly East Asia and the Pacific islands, was chosen as the feasibility study region. The author's group then at the National Institute of Health Sciences (NIHS) of Japan led this initiative and hosted numerous meetings. At these meetings, tutorial sessions for communicating chemical safety expertise and emerging new information technologies relevant to the safe management of chemicals were offered. Our experience with this project, particularly the Web-based system and the tutorial sessions, may be of use to others involved with Web-based instruction and the training of chemical safety specialists from both developed and developing countries.

Development of an e-learning package on Service-Learning for university teachers: experience from Hong Kong.

PubMed

Shek, Daniel T L; Chan, Stephen C F

2013-01-01

To help university teachers to understand Service-Learning and develop Service-Learning subjects, a 3-h+ e-learning package was developed at The Hong Kong Polytechnic University (PolyU). There are seven units in this e-learning package: introduction session (Unit 1), what is Service-Learning? (Unit 2), impact and benefits of Service-Learning (Unit 3), myths and positive attitudes toward Service-Learning (Unit 4), developing a Service-Learning subject at PolyU (Unit 5), self-reflection about Service-Learning (Unit 6), and concluding session (Unit 7). To understand the views of the users on the e-learning package, the package was offered before formal launching. For the first offering, three focus group sessions were held. Results showed that the users were satisfied with the structural arrangement of the e-learning package and agreed that the e-learning package was useful for them to understand more about Service-Learning. For the second offering, colleagues were generally satisfied with the e-learning package and demonstrated gain in knowledge on Service-Learning. Suggestions for improvement were noted.

Lifelong Learning in German Learning Cities/Regions

ERIC Educational Resources Information Center

Reghenzani-Kearns, Denise; Kearns, Peter

2012-01-01

This paper traces the policies and lessons learned from two consecutive German national programs aimed at developing learning cities/regions. Known as Learning Regions Promotion of Networks, this first program transitioned into the current program, Learning on Place. A case study chosen is from the Tolzer region where a network has self-sustained…

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

PubMed

Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

2017-02-01

Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

Exploring Practice-Research Networks for Critical Professional Learning

ERIC Educational Resources Information Center

Appleby, Yvon; Hillier, Yvonne

2012-01-01

This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

Graduate Employability: The Perspective of Social Network Learning

ERIC Educational Resources Information Center

Chen, Yong

2017-01-01

This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

Online Treatment and Virtual Therapists in Child and Adolescent Psychiatry.

PubMed

Schueller, Stephen M; Stiles-Shields, Colleen; Yarosh, Lana

2017-01-01

Online and virtual therapies are a well-studied and efficacious treatment option for various mental and behavioral health conditions among children and adolescents. However, many interventions have not considered the unique affordances offered by technologies that might align with the capacities and interests of youth users. In this article, the authors discuss learnings from child-computer interaction that can inform future generations of interventions and guide developers, practitioners, and researchers how to best use new technologies for youth populations. The article concludes with innovative examples illustrating future potentials of online and virtual therapies such as gaming and social networking. Copyright © 2016 Elsevier Inc. All rights reserved.

Astronomy 3.0 Style

NASA Astrophysics Data System (ADS)

Accomazzi, A.

2010-10-01

Over the next decade, we will witness the development of a new infrastructure in support of data-intensive scientific research, which includes Astronomy. This new networked environment will offer both challenges and opportunities to our community and has the potential to transform the way data are described, curated and preserved. Based on the lessons learned during the development and management of the ADS, a case is made for adopting the emerging technologies and practices of the Semantic Web to support the way Astronomy research will be conducted. Examples of how small, incremental steps can, in the aggregate, make a significant difference in the provision and repurposing of astronomical data are provided.

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

PubMed

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

2013-09-01

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

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Co-Operative Learning and Development Networks.

ERIC Educational Resources Information Center

Hodgson, V.; McConnell, D.

1995-01-01

Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Learning through Artful Knowing

ERIC Educational Resources Information Center

van Meer, Pleuntje

2016-01-01

In this paper, van Meer offers an exploration in the context of action learning doctoral research in which she explores leadership consultancy practice. The purpose is to grow insight on learning as a lived experience. Artful knowing offers a way to learn about life and practice, through a deeply personal, reflective and sense-making process. The…

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

NASA Astrophysics Data System (ADS)

Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

1985-07-01

Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

PubMed

Devalla, Sripad Krishna; Chin, Khai Sing; Mari, Jean-Martial; Tun, Tin A; Strouthidis, Nicholas G; Aung, Tin; Thiéry, Alexandre H; Girard, Michaël J A

2018-01-01

To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer + prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 ± 0.03, 0.92 ± 0.03, 0.99 ± 0.00, 0.89 ± 0.03, and 0.94 ± 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P < 0.001). Besides offering a good reliability, digital staining also performed well on OCT images of both glaucoma and healthy individuals. Our deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management.

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

76 FR 12998 - Proposed Information Collection Renewals

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-09

... information are offering to assist, mentor or network for jobs. 3. OMB Control Number: 0420-0533. Old Title... information are offering to assist, mentor or network for jobs. Dated: March 3, 2011. Earl W. Yates, Associate...

Praxis and reflexivity for interprofessional education: towards an inclusive theoretical framework for learning.

PubMed

Hutchings, Maggie; Scammell, Janet; Quinney, Anne

2013-09-01

While there is growing evidence of theoretical perspectives adopted in interprofessional education, learning theories tend to foreground the individual, focusing on psycho-social aspects of individual differences and professional identity to the detriment of considering social-structural factors at work in social practices. Conversely socially situated practice is criticised for being context-specific, making it difficult to draw generalisable conclusions for improving interprofessional education. This article builds on a theoretical framework derived from earlier research, drawing on the dynamics of Dewey's experiential learning theory and Archer's critical realist social theory, to make a case for a meta-theoretical framework enabling social-constructivist and situated learning theories to be interlinked and integrated through praxis and reflexivity. Our current analysis is grounded in an interprofessional curriculum initiative mediated by a virtual community peopled by health and social care users. Student perceptions, captured through quantitative and qualitative data, suggest three major disruptive themes, creating opportunities for congruence and disjuncture and generating a model of zones of interlinked praxis associated with professional differences and identity, pedagogic strategies and technology-mediated approaches. This model contributes to a framework for understanding the complexity of interprofessional learning and offers bridges between individual and structural factors for engaging with the enablements and constraints at work in communities of practice and networks for interprofessional education.

A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

NASA Astrophysics Data System (ADS)

Ravela, S.

2017-12-01

In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.

Examining Perceptions of Networking among African American Women in Student Affairs

ERIC Educational Resources Information Center

Burke, Monica G.; Carter, Joelle D.

2015-01-01

Since career networks offer access to upper level positions, being a part of one can offer opportunities and access to information, knowledge and referrals for career mobility. However, the duality of race and gender can create a distinctive challenge to gaining access to career networks, which are crucial components to rise to the upper level of…

Stress and eustress in nursing students.

PubMed

Gibbons, Chris; Dempster, Martin; Moutray, Marianne

2008-02-01

This paper is a report of a study to identify experiences that led to both distress and eustress and to make recommendations to help students cope with course demands. Much of the research on stress in nursing students is quantitative in focus and all draws on their experience of distress, with little attempt to understand experiences of eustress. A series of focus groups were carried out with a volunteer sample of final year nursing students (n = 16) in the United Kingdom in 2007. The data were thematically analysed. The themes identified were clinical experience, support, learning and teaching experience and course structure. There were experiences within each that were perceived as sources of distress and eustress. Many of the sources of distress concur with earlier findings but they are more likely to be experienced and commented on because the demands of present-day programmes and the profile of many nursing students mean that more effort is invested in meeting educational demands. The experiential learning and patient-care opportunity that placements provided was an important source of eustress. Students who coped well drew on effective support networks and adopted a positive, optimistic perspective towards programme issues. Effective educators did not offer more time than those perceived as less effective but seemed more effective at tuning into students' concerns, showing more empathy and offering clearer guidance.

Blended Learning: The Student Viewpoint

PubMed Central

Shantakumari, N; Sajith, P

2015-01-01

Background: Blended learning (BL) is defined as “a way of meeting the challenges of tailoring learning and development to the needs of individuals by integrating the innovative and technological advances offered by online learning with the interaction and participation offered in the best of traditional learning.” The Gulf Medical University (GMU), Ajman, UAE, offers a number of courses which incorporate BL with contact classes and online component on an E-learning platform. Insufficient learning satisfaction has been stated as an obstacle to its implementation and efficacy. Aim: To determine the students’ perceptions toward BL which in turn will determine their satisfaction and the efficacy of the courses offered. Subjects and Methods: This was a cross-sectional study conducted at the GMU, Ajman between January and December 2013. Perceptions of BL process, content, and ease of use were collected from 75 students enrolled in the certificate courses offered by the university using a questionnaire. Student perceptions were assessed using Mann–Whitney U-test and Kruskal–Wallis test on the basis of gender, age, and course enrollment. Results: The median scores of all the questions in the three domains were above three suggesting positive perceptions on BL. The distribution of perceptions was similar between gender and age. However, significant differences were observed in the course enrollment (P = 0.02). Conclusion Students hold a positive perception of the BL courses being offered in this university. The difference in perceptions among students of different courses suggest that the BL format offered needs modification according to course content to improve its perception. PMID:26500788

A Statewide Service Learning Network Ignites Teachers and Students.

ERIC Educational Resources Information Center

Monsour, Florence

Service learning, curriculum-linked community service, has proved remarkably effective in igniting students' desire to learn. In 1997, the Wisconsin Partnership in Service Learning was initiated as a cross-disciplinary, cross-institutional endeavor. Supported by a grant from Learn and Serve America, the partnership created a network throughout…

Scaffolding in Connectivist Mobile Learning Environment

ERIC Educational Resources Information Center

Ozan, Ozlem

2013-01-01

Social networks and mobile technologies are transforming learning ecology. In this changing learning environment, we find a variety of new learner needs. The aim of this study is to investigate how to provide scaffolding to the learners in connectivist mobile learning environment: (1) to learn in a networked environment; (2) to manage their…

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

"Getting Practical" and the National Network of Science Learning Centres

ERIC Educational Resources Information Center

Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

2011-01-01

The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

The Practices of Student Network as Cooperative Learning in Ethiopia

ERIC Educational Resources Information Center

Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

2015-01-01

Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…

The STIN in the Tale: A Socio-Technical Interaction Perspective on Networked Learning

ERIC Educational Resources Information Center

Walker, Steve; Creanor, Linda

2009-01-01

In this paper, we go beyond what have been described as "mechanistic" accounts of e-learning to explore the complexity of relationships between people and technology as encountered in cases of networked learning. We introduce from the social informatics literature the concept of sociotechnical interaction networks which focus on the…

Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

ERIC Educational Resources Information Center

Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

2017-01-01

Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

ERIC Educational Resources Information Center

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

Implementation of a Framework for Collaborative Social Networks in E-Learning

ERIC Educational Resources Information Center

Maglajlic, Seid

2016-01-01

This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

The Role of Action Research in the Development of Learning Networks for Entrepreneurs

ERIC Educational Resources Information Center

Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

2012-01-01

Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

ERIC Educational Resources Information Center

Sarkar, Nurul I.

2013-01-01

Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Starting a Conversation: Engaging Scientists with the Public through Interactive Activities

NASA Astrophysics Data System (ADS)

Bartel, B. A.; Charlevoix, D. J.

2016-12-01

While demonstrations are a handy tool, research shows conversations are an even more effective way to have a lasting impact. UNAVCO joined the Portal to the Public Network (PoPNet) in March 2016 to offer PoPNet-style hands-on communication training to the geodetic scientific community. Portal to the Public is a system of professional development tools designed to better connect scientists and the public in positive dialogue, in informal education settings such as museums. The framework focuses not on one-way messaging, but on conversations. PoPNet is the network of museums and other institutions implementing PoP training. UNAVCO developed and launched a pilot program in summer 2016 with a cohort of graduate students. In the PoPNet model, museums bring local scientists to the museum to learn about and practice science communication to the general public. UNAVCO does not have a museum but rather is an international community of geoscientists who can receive training on informal communications and then practice their skills at a museum local to them. The skills learned are useful in communicating science not only to the public, but also to policy makers, funders, and anyone outside the immediate scientific discipline. Developing communications skills also encourages scientists to think more holistically about their research and its impacts.In the 2016 pilot program, UNAVCO offered a seven-week workshop to UNAVCO Student Internship Program (USIP) interns. The students spent two hours per week working on communication skills through hands-on activities and discussions, and developed tabletop activities based on their research. These tabletop activities are hands-on demonstrations of scientific concepts such as tsunami early warning and volcano deformation designed for interaction rather than one-way show-and-tell. The program culminated in an evening with the tabletop activities at the Boulder County Farmers Market. All six participating interns indicated on the exit survey that UNAVCO should "Yes-absolutely!" offer the program again. The program will continue with interns in summer 2017 and PoP training in a compressed form at the 2017 EarthScope National Meeting.

Toolkits and Libraries for Deep Learning.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Deep learning for computational chemistry.

PubMed

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Deep learning for computational chemistry

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

Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore » this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.« less

The Convergence of Intelligences

NASA Astrophysics Data System (ADS)

Diederich, Joachim

Minsky (1985) argued an extraterrestrial intelligence may be similar to ours despite very different origins. ``Problem- solving'' offers evolutionary advantages and individuals who are part of a technical civilisation should have this capacity. On earth, the principles of problem-solving are the same for humans, some primates and machines based on Artificial Intelligence (AI) techniques. Intelligent systems use ``goals'' and ``sub-goals'' for problem-solving, with memories and representations of ``objects'' and ``sub-objects'' as well as knowledge of relations such as ``cause'' or ``difference.'' Some of these objects are generic and cannot easily be divided into parts. We must, therefore, assume that these objects and relations are universal, and a general property of intelligence. Minsky's arguments from 1985 are extended here. The last decade has seen the development of a general learning theory (``computational learning theory'' (CLT) or ``statistical learning theory'') which equally applies to humans, animals and machines. It is argued that basic learning laws will also apply to an evolved alien intelligence, and this includes limitations of what can be learned efficiently. An example from CLT is that the general learning problem for neural networks is intractable, i.e. it cannot be solved efficiently for all instances (it is ``NP-complete''). It is the objective of this paper to show that evolved intelligences will be constrained by general learning laws and will use task-decomposition for problem-solving. Since learning and problem-solving are core features of intelligence, it can be said that intelligences converge despite very different origins.

Identifying Gatekeepers in Online Learning Networks

ERIC Educational Resources Information Center

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

NASA Astrophysics Data System (ADS)

Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

2018-05-01

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

[E-Learning--an important contribution to general medical training and continuing education?].

PubMed

Ruf, D; Berner, M M; Kriston, L; Härter, M

2008-09-01

There is increasing activity in the development of e-learning modules for general medical training and continuing education. One of the central advantages of e-learning is flexibility regarding time and place of its use. The quality of the available e-learning opportunities varies quite considerably. For users it is often not easy to assess the quality of e-learning modules or to find offers of high quality. This could be a reason for the fact that despite the huge number of e-learning modules still only few students and physicians are using them. This is although e-learning has proven to be as effective as and even more efficient than learning in the classroom or with paper-based materials. This article summarizes the different models of e-learning, how and where to find offers of high quality, advantages of using e-learning, and the effectiveness and efficiency of such offers. In addition problems of e-learning and possibilities to overcome these problems are shown.

Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

NASA Astrophysics Data System (ADS)

Zamora Ramos, Ernesto

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures, multilayer percepterons and convolutional neural networks. Our research with neural networks has encountered a great deal of difficulties regarding hyperparameter estimation for good training convergence rate and accuracy. Most hyperparameters, including architecture, learning rate, regularization, trainable parameters (or weights) initialization, and so on, are chosen via a trial and error process with some educated guesses. However, we developed the first quantitative method to compare weight initialization strategies, a critical hyperparameter choice during training, to estimate among a group of candidate strategies which would make the network converge to the highest classification accuracy faster with high probability. Our method provides a quick, objective measure to compare initialization strategies to select the best possible among them beforehand without having to complete multiple training sessions for each candidate strategy to compare final results.

Experiential learning and andragogy--negotiated learning in nurse education: a critical appraisal.

PubMed

Burnard, P

1989-10-01

Andragogy and experiential learning have frequently been cited as recommended approaches to aspects of nurse education. This paper offers a critical appraisal of the two approaches and offers suggestions as to how a negotiated nursing curriculum may be developed.

The research of "blind" spot in the LVQ network

NASA Astrophysics Data System (ADS)

Guo, Zhanjie; Nan, Shupo; Wang, Xiaoli

2017-04-01

Nowadays competitive neural network has been widely used in the pattern recognition, classification and other aspects, and show the great advantages compared with the traditional clustering methods. But the competitive neural networks still has inadequate in many aspects, and it needs to be further improved. Based on the learning Vector Quantization Network proposed by Learning Kohonen [1], this paper resolve the issue of the large training error, when there are "blind" spots in a network through the introduction of threshold value learning rules and finally programs the realization with Matlab.

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Embodied learning of a generative neural model for biological motion perception and inference

PubMed Central

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V.

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons. PMID:26217215

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

PubMed

Chambon, Stanislas; Galtier, Mathieu N; Arnal, Pierrick J; Wainrib, Gilles; Gramfort, Alexandre

2018-04-01

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.

Applying Collaborative Learning and Quality Improvement to Public Health: Lessons from the Collaborative Improvement and Innovation Network (CoIIN) to Reduce Infant Mortality.

PubMed

Ghandour, Reem M; Flaherty, Katherine; Hirai, Ashley; Lee, Vanessa; Walker, Deborah Klein; Lu, Michael C

2017-06-01

Infant mortality remains a significant public health problem in the U.S. The Collaborative Improvement & Innovation Network (CoIIN) model is an innovative approach, using the science of quality improvement and collaborative learning, which was applied across 13 Southern states in Public Health Regions IV and VI to reduce infant mortality and improve birth outcomes. We provide an in-depth discussion of the history, development, implementation, and adaptation of the model based on the experience of the original CoIIN organizers and participants. In addition to the political genesis and functional components of the initiative, 8 key lessons related to staffing, planning, and implementing future CoIINs are described in detail. This paper reports the findings from a process evaluation of the model. Data on the states' progress toward reducing infant mortality and improving birth outcomes were collected through a survey in the final months of a 24-month implementation period, as well as through ongoing team communications. The peer-to-peer exchange and platform for collaborative learning, as well as the sharing of data across the states, were major strengths and form the foundation for future CoIIN efforts. A lasting legacy of the initiative is the unique application and sharing of provisional "real time" data to inform "real time" decision-making. The CoIIN model of collaborative learning, QI, and innovation offers a promising approach to strengthening partnerships within and across states, bolstering data systems to inform and track progress more rapidly, and ultimately accelerating improvement toward healthier communities, States, and the Nation as a whole.

Faculty development: if you build it, they will come.

PubMed

Steinert, Yvonne; Macdonald, Mary Ellen; Boillat, Miriam; Elizov, Michelle; Meterissian, Sarkis; Razack, Saleem; Ouellet, Marie-Noel; McLeod, Peter J

2010-09-01

The goals of this study were three-fold: to explore the reasons why some clinical teachers regularly attend centralised faculty development activities; to compare their responses with those of colleagues who do not attend, and to learn how we can make faculty development programmes more pertinent to teachers' needs. In 2008-2009, we conducted focus groups with 23 clinical teachers who had participated in faculty development activities on a regular basis in order to ascertain their perceptions of faculty development, reasons for participation, and perceived barriers against involvement. Thematic analysis and research team consensus guided the data interpretation. Reasons for regular participation included the perceptions that: faculty development enables personal and professional growth; learning and self-improvement are valued; workshop topics are viewed as relevant to teachers' needs; the opportunity to network with colleagues is appreciated, and initial positive experiences promote ongoing involvement. Barriers against participation mirrored those cited by non-attendees in an earlier study (e.g. volume of work, lack of time, logistical factors), but did not prevent participation. Suggestions for increasing participation included introducing a 'buddy system' for junior faculty members, an orientation workshop for new staff, and increased role-modelling and mentorship. The conceptualisation of faculty development as a means to achieve specific objectives and the desire for relevant programming that addresses current needs (i.e., expectancies), together with an appreciation of learning, self-improvement and networking with colleagues (i.e., values), were highlighted as reasons for participation by regular attendees. Medical educators should consider these 'lessons learned' in the design and delivery of faculty development offerings. They should also continue to explore the notion of faculty development as a social practice and the application of motivational theories that include expectancy-value constructs to personal and professional development.

Embodied learning of a generative neural model for biological motion perception and inference.

PubMed

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

PubMed

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Learning by Doing: Twenty Successful Active Learning Exercises for Information Systems Courses

ERIC Educational Resources Information Center

Mitchell, Alanah; Petter, Stacie; Harris, Albert L.

2017-01-01

Aim/Purpose: This paper provides a review of previously published work related to active learning in information systems (IS) courses. Background: There are a rising number of strategies in higher education that offer promise in regards to getting students' attention and helping them learn, such as flipped classrooms and offering courses online.…

Why Bother with the Scholarship of Teaching and Learning?

ERIC Educational Resources Information Center

Draeger, John

2013-01-01

This paper argues that the scholarship of teaching and learning (SoTL) matters on at least six interrelated levels. First, SoTL matters because learning matters, and SoTL can help students learn more effectively. Second, it offers professors the tools to more effectively share their disciplinary passions. Third, it offers faculty an avenue for…

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

Supporting nurse mentor development: An exploration of developmental constellations in nursing mentorship practice.

PubMed

MacLaren, Julie-Ann

2018-01-01

Supervised practice as a mentor is currently an integral component of nurse mentor education. However, workplace education literature tends to focus on dyadic mentor-student relationships rather than developmental relationships between colleagues. This paper explores the supportive relationships of nurses undertaking a mentorship qualification, using the novel technique of constellation development to determine the nature of workplace support for this group. Semi-structured interviews were conducted with three recently qualified nurse mentors. All participants developed a mentorship constellation identifying colleagues significant to their own learning in practice. These significant others were also interviewed alongside practice education, and nurse education leads. Constellations were analysed in relation to network size, breadth, strength of relationships, and attributes of individuals. Findings suggest that dyadic forms of supervisory mentorship may not offer the range of skills and attributes that developing mentors require. Redundancy of mentorship attributes within the constellation (overlapping attributes between members) may counteract problems caused when one mentor attempts to fulfil all mentorship roles. Wider nursing teams are well placed to provide the support and supervision required by mentors in training. Where wider and stronger networks were not available to mentorship students, mentorship learning was at risk. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

CRAFFT: An Activity Prediction Model based on Bayesian Networks

PubMed Central

Nazerfard, Ehsan; Cook, Diane J.

2014-01-01

Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments. PMID:25937847

CRAFFT: An Activity Prediction Model based on Bayesian Networks.

PubMed

Nazerfard, Ehsan; Cook, Diane J

2015-04-01

Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

Designing a holistic end-to-end intelligent network analysis and security platform

NASA Astrophysics Data System (ADS)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

Functional connectivity changes in second language vocabulary learning.

PubMed

Ghazi Saidi, Ladan; Perlbarg, Vincent; Marrelec, Guillaume; Pélégrini-Issac, Mélani; Benali, Habib; Ansaldo, Ana-Inés

2013-01-01

Functional connectivity changes in the language network (Price, 2010), and in a control network involved in second language (L2) processing (Abutalebi & Green, 2007) were examined in a group of Persian (L1) speakers learning French (L2) words. Measures of network integration that characterize the global integrative state of a network (Marrelec, Bellec et al., 2008) were gathered, in the shallow and consolidation phases of L2 vocabulary learning. Functional connectivity remained unchanged across learning phases for L1, whereas total, between- and within-network integration levels decreased as proficiency for L2 increased. The results of this study provide the first functional connectivity evidence regarding the dynamic role of the language processing and cognitive control networks in L2 learning (Abutalebi, Cappa, & Perani, 2005; Altarriba & Heredia, 2008; Leonard et al., 2011; Parker-Jones et al., 2011). Thus, increased proficiency results in a higher degree of automaticity and lower cognitive effort (Segalowitz & Hulstijn, 2005). Copyright © 2012 Elsevier Inc. All rights reserved.

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

PubMed Central

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task. PMID:29441027

Convergence analysis of sliding mode trajectories in multi-objective neural networks learning.

PubMed

Costa, Marcelo Azevedo; Braga, Antonio Padua; de Menezes, Benjamin Rodrigues

2012-09-01

The Pareto-optimality concept is used in this paper in order to represent a constrained set of solutions that are able to trade-off the two main objective functions involved in neural networks supervised learning: data-set error and network complexity. The neural network is described as a dynamic system having error and complexity as its state variables and learning is presented as a process of controlling a learning trajectory in the resulting state space. In order to control the trajectories, sliding mode dynamics is imposed to the network. It is shown that arbitrary learning trajectories can be achieved by maintaining the sliding mode gains within their convergence intervals. Formal proofs of convergence conditions are therefore presented. The concept of trajectory learning presented in this paper goes further beyond the selection of a final state in the Pareto set, since it can be reached through different trajectories and states in the trajectory can be assessed individually against an additional objective function. Copyright © 2012 Elsevier Ltd. All rights reserved.

Reaching Rural Schools Using an Interactive Satellite Based Educational Network: Evaluating TI-IN Network's First Year.

ERIC Educational Resources Information Center

Pease, Pamela S.; Tinsley, Patsy J.

The paper details development, implementation, and user research/evaluation of TI-IN Network, Inc., the first private, interactive satellite based educational system in the United States developed for public schools and offering a total systems approach by providing both user technology and a wide range of course offerings. An overview of specific…

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks

PubMed Central

Rittner, Jessica Levin; Johnson, Karin E.; Gerteis, Jessie; Miller, Therese

2017-01-01

Objective: Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Data sources/study setting: Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. Study design: The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. Data collection: The authors reviewed detailed notes from the interviews to distill salient themes. Principal findings: Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. Conclusion: The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. PMID:28277202

Exploring the Peer Interaction Effects on Learning Achievement in a Social Learning Platform Based on Social Network Analysis

ERIC Educational Resources Information Center

Lin, Yu-Tzu; Chen, Ming-Puu; Chang, Chia-Hu; Chang, Pu-Chen

2017-01-01

The benefits of social learning have been recognized by existing research. To explore knowledge distribution in social learning and its effects on learning achievement, we developed a social learning platform and explored students' behaviors of peer interactions by the proposed algorithms based on social network analysis. An empirical study was…

Practices and Strategies of Self-Initiated Language Learning in an Online Social Network Discussion Forum: A Descriptive Case Study

ERIC Educational Resources Information Center

Hsieh, Hsiu-Wei

2012-01-01

The proliferation of information and communication technologies and the prevalence of online social networks have facilitated the opportunities for informal learning of foreign languages. However, little educational research has been conducted on how individuals utilize those social networks to take part in self-initiated language learning without…

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,…

The Fire Learning Network: A promising conservation strategy for forestry

Treesearch

Bruce E. Goldstein; William H. Butler; R. Bruce Hull

2010-01-01

Conservation Learning Networks (CLN) are an emerging conservation strategy for addressing complex resource management challenges that face the forestry profession. The US Fire Learning Network (FLN) is a successful example of a CLN that operates on a national scale. Developed in 2001 as a partnership between The Nature Conservancy, the US Forest Service, and land-...

Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

ERIC Educational Resources Information Center

Engelthaler, Tomas; Hills, Thomas T.

2017-01-01

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…