Western Oregon University: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2009

2009-01-01

The Academy for Educational Development (AED) sent a research team to Western Oregon University (WOU) on November 17-18, 2008, to conduct interviews with individuals who play important roles in the university's teacher preparation program. These interviews, along with additional materials provided by WOU and identified by the AED research team,…

Jackson State University: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2009

2009-01-01

The Academy for Educational Development (AED) sent a research team to Jackson State University (JSU) on October 13-14, 2008 to conduct interviews with individuals who play important roles in the university's teacher preparation program (see Appendix A). These interviews, along with additional documentation provided by JSU and identified by the AED…

Arizona State University: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2009

2009-01-01

The Academy for Educational Development (AED) sent a research team to Arizona State University (ASU) on October 20-21, 2008 to conduct interviews with individuals who play important roles in the university's teacher preparation program. These interviews, along with additional documentation provided by ASU and identified by the AED research team,…

University of Dayton: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2012

2012-01-01

The Academy for Educational Development (AED) sent a research team to the University of Dayton (UD) on November 5-7, 2008, to conduct interviews with individuals who played significant roles in the university's teacher preparation program (see Appendix A). These interviews, along with additional materials provided by UD and identified by the AED…

University of North Carolina Greensboro: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2009

2009-01-01

The Academy for Educational Development (AED) sent a research team to the University of North Carolina at Greensboro (UNCG) on October 23-24, 2008, to conduct interviews with individuals who play important roles in the university's teacher preparation program. These interviews, along with additional documentation provided by UNCG and identified by…

Indiana State University: Documentation of the Teachers for a New Era Learning Network. Case Study

ERIC Educational Resources Information Center

Academy for Educational Development, 2009

2009-01-01

The Academy for Educational Development (AED) sent a research team to Indiana State University (ISU) on November 11-12, 2008 to conduct interviews with individuals who play important roles in the university's teacher preparation program. Based upon the nine case studies, the AED research team will prepare a cross-case study that will document and…

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

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

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Characterizing English Poetic Style Using Complex Networks

NASA Astrophysics Data System (ADS)

Roxas-Villanueva, Ranzivelle Marianne; Nambatac, Maelori Krista; Tapang, Giovanni

Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667-1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.

Machine learning topological states

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Cost Benefit Analysis of Establishing a Network-based Training System in the Turkish Coast Guard

DTIC Science & Technology

2009-12-01

T2_NA.html Rousseau , D.M. (1997). Organizational behavior in the new organizational era. Annual Review of Psychology . Rowley, D . J., Lujan, D . L...William D . Hatch i REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection...based training. Virginia: American Society for Training & Development. Ausubel, D . (1963). The psychology of meaningful verbal learning. New York

Restructuring for the 90s and Beyond: Solution Based Learning in the Era of Smart Homes, Wired Communities, Fast Systems, Global Networks, and Fast Forward Learners in a Borderless World.

ERIC Educational Resources Information Center

Groff, Warren H.

An ultimate purpose of education is human resource development to provide society with a critical mass of intellectual capital and competent workforces. To accomplish this end, leaders implement planning processes to guide policy-making, develop institutions, and allocate resources. Although new information technologies are becoming commonplace in…

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, I. P.

2016-04-01

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

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.

Sustaining a Global Social Network: a quasi-experimental study.

PubMed

Benton, D C; Ferguson, S L

2017-03-01

To examine the longer term impact on the social network of participating nurses in the Global Nursing Leadership Institute (GNLI2013) through using differing frequencies of follow-up to assess impact on maintenance of network cohesion. Social network analysis is increasingly been used by nurse researchers, however, studies tend to use single point-in-time descriptive methods. This study utilizes a repeated measures, block group, control-intervention, quasi-experimental design. Twenty-eight nurse leaders, competitively selected through a double-blind peer review process, were allocated to five action learning-based learning groups. Network architecture, measures of cohesion and node degree frequency were all used to assess programme impact. The programme initiated and sustained connections between nurse leaders drawn from a geographically dispersed heterogeneous group. Modest inputs of two to three e-mails over a 6-month period seem sufficient to maintain connectivity as indicated by measures of network density, diameter and path length. Due to the teaching methodology used, the study sample was relatively small and the follow-up data collection took place after a relatively short time. Replication and further cohort data collection would be advantageous. In an era where many policy solutions are being debated and initiated at the global level, action learning leadership development that utilizes new technology follow-up appears to show significant impact and is worthy of wider application. The approach warrants further inquiry and testing as to its longer term effects on nursing's influence on policy formulation and implementation. © 2016 International Council of Nurses.

Envisioning engineering education and practice in the coming intelligence convergence era — a complex adaptive systems approach

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2013-12-01

Some of the recent attempts for improving and transforming engineering education are reviewed. The attempts aim at providing the entry level engineers with the skills needed to address the challenges of future large-scale complex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. The major characteristics of the coming intelligence convergence era (the post-information age) are identified. These include the prevalence of smart devices and environments, the widespread applications of anticipatory computing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines. Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recent game changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that can significantly impact engineering education in the coming era are identified. Among these are open educational resources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruction / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significant transformative changes in, and mass customization of, learning are envisioned to emerge from the synergistic combination of the game changers and other technologies. The realization of the aforementioned vision requires the development of a new multidisciplinary framework of emergent engineering for relating innovation, complexity and cybernetics, within the future learning environments. The framework can be used to treat engineering education as a complex adaptive system, with dynamically interacting and communicating components (instructors, individual, small, and large groups of learners). The emergent behavior resulting from the interactions can produce progressively better, and continuously improving, learning environment. As a first step towards the realization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collaboration between the various stakeholders of engineering education, and to accelerate the development of a skilled engineering workforce. The major components of the ecosystems include integrated knowledge discovery and exploitation facilities, blended learning and research spaces, novel ultra-intelligent software agents, multimodal and autonomous interfaces, and networked cognitive and tele-presence robots.

An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

NASA Astrophysics Data System (ADS)

Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

2018-05-01

As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

The Era of Lifelong Learning: Implications for Secondary Schools

ERIC Educational Resources Information Center

Bryce, Jennifer; Frigo, Tracey; McKenzie, Phillip; Withers, Graeme

2000-01-01

This paper is concerned with the role that schools can play in engaging young people in their learning, and helping them to develop skills and attitudes that will give them an orientation towards learning for life. To meet the needs of an era of lifelong learning, schools need to view themselves as a stage in the ongoing learning process, where…

Analysis on the University’s Network Security Level System in the Big Data Era

NASA Astrophysics Data System (ADS)

Li, Tianli

2017-12-01

The rapid development of science and technology, the continuous expansion of the scope of computer network applications, has gradually improved the social productive forces, has had a positive impact on the increase production efficiency and industrial scale of China's different industries. Combined with the actual application of computer network in the era of large data, we can see the existence of influencing factors such as network virus, hacker and other attack modes, threatening network security and posing a potential threat to the safe use of computer network in colleges and universities. In view of this unfavorable development situation, universities need to pay attention to the analysis of the situation of large data age, combined with the requirements of network security use, to build a reliable network space security system from the equipment, systems, data and other different levels. To avoid the security risks exist in the network. Based on this, this paper will analyze the hierarchical security system of cyberspace security in the era of large data.

DREAMING OF ATMOSPHERES

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

Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk

Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as themore » “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.« less

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

Lessons Learned from Developing a Patient Engagement Panel: An OCHIN Report.

PubMed

Arkind, Jill; Likumahuwa-Ackman, Sonja; Warren, Nate; Dickerson, Kay; Robbins, Lynn; Norman, Kathy; DeVoe, Jennifer E

2015-01-01

There is renewed interest in patient engagement in clinical and research settings, creating a need for documenting and publishing lessons learned from efforts to meaningfully engage patients. This article describes early lessons learned from the development of OCHIN's Patient Engagement Panel (PEP). OCHIN supports a national network of more than 300 community health centers (CHCs) and other primary care settings that serve over 1.5 million patients annually across nearly 20 states. The PEP was conceived in 2009 to harness the CHC tradition of patient engagement in this new era of patient-centered outcomes research and to ensure that patients were engaged throughout the life cycle of our research projects, from conception to dissemination. Developed by clinicians and researchers within our practice-based research network, recruitment of patients to serve as PEP members began in early 2012. The PEP currently has a membership of 18 patients from 3 states. Over the past 24 months, the PEP has been involved with 12 projects. We describe developing the PEP and challenges and lessons learned (eg, recruitment, funding model, creating value for patient partners, compensation). These lessons learned are relevant not only for research but also for patient engagement in quality improvement efforts and other clinical initiatives. © Copyright 2015 by the American Board of Family Medicine.

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Learning in a Different Era: "Do Our Education Systems Do Enough to Enable Learners to Flourish as Independent, Autonomous and Well-Balanced Individuals?"

ERIC Educational Resources Information Center

Cheng, Kai-ming

2015-01-01

There is a prime necessity to make a distinction between "education" and "learning." Learning is a human instinct. Education is not. Education is about learning processes designed by adults for the young. In the past two centuries in the industrial era, education has developed into society-wide "school" systems. Young…

New Habits of Heart and Mind: "Third Wave" Transformational Leaders. Creating Solution-Based Learning Focused on Children and Families and Math, Science, and Technology in the Era of Smart Homes, Wired Communities, Fast Systems, Global Networks, and Fast Forward Learners in a Borderless World.

ERIC Educational Resources Information Center

Groff, Warren H.

One purpose of education is that of human-resource development--to provide society with the critical mass of intellectual capital and competent work forces. This paper presents an analysis of the emerging global context and school restructuring in industrialized nations. It also describes an evaluation conducted by the Education Committee of the…

Designs of Learning and the Formation and Transformation of Knowledge in an Era of Globalization

ERIC Educational Resources Information Center

Selander, Staffan

2008-01-01

In this article, the formation and transformation of knowledge and the role of designs for learning will be elaborated and discussed in relation to the introduction of national curricula and school textbooks during the beginning of the industrialized era vs. the introduction of individual curricula and new digital learning resources in the…

A New Generation of Networks and Computing Models for High Energy Physics in the LHC Era

NASA Astrophysics Data System (ADS)

Newman, H.

2011-12-01

Wide area networks of increasing end-to-end capacity and capability are vital for every phase of high energy physicists' work. Our bandwidth usage, and the typical capacity of the major national backbones and intercontinental links used by our field have progressed by a factor of several hundred times over the past decade. With the opening of the LHC era in 2009-10 and the prospects for discoveries in the upcoming LHC run, the outlook is for a continuation or an acceleration of these trends using next generation networks over the next few years. Responding to the need to rapidly distribute and access datasets of tens to hundreds of terabytes drawn from multi-petabyte data stores, high energy physicists working with network engineers and computer scientists are learning to use long range networks effectively on an increasing scale, and aggregate flows reaching the 100 Gbps range have been observed. The progress of the LHC, and the unprecedented ability of the experiments to produce results rapidly using worldwide distributed data processing and analysis has sparked major, emerging changes in the LHC Computing Models, which are moving from the classic hierarchical model designed a decade ago to more agile peer-to-peer-like models that make more effective use of the resources at Tier2 and Tier3 sites located throughout the world. A new requirements working group has gauged the needs of Tier2 centers, and charged the LHCOPN group that runs the network interconnecting the LHC Tierls with designing a new architecture interconnecting the Tier2s. As seen from the perspective of ICFA's Standing Committee on Inter-regional Connectivity (SCIC), the Digital Divide that separates physicists in several regions of the developing world from those in the developed world remains acute, although many countries have made major advances through the rapid installation of modern network infrastructures. A case in point is Africa, where a new round of undersea cables promises to transform the continent.

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

PubMed

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

2017-10-01

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

Negotiating the Use of Formative Assessment for Learning in an Era of Accountability Testing

ERIC Educational Resources Information Center

Yin, Xinying

2013-01-01

The purpose of this collaborative action research was to understand how science educators can negotiate the tension between integrating formative assessment (FA) for students' learning and meeting the need for standardized summative assessment (testing) from a critical perspective. Using formative assessment in the era of accountability testing…

Small Aerostationary Telecommunications Orbiter Concept for Mars in the 2020s

NASA Technical Reports Server (NTRS)

Lock, Robert E.; Edwards, Charles D., Jr.; Nicholas, Austin; Woolley, Ryan; Bell, David J.

2016-01-01

Current Mars science orbiters carry UHF proximity payloads to provide limited access and data services to landers and rovers on Mars surface. In the era of human spaceflight to Mars, very high rate and reliable relay services will be needed to serve a large number of supporting vehicles, habitats, and orbiters, as well as astronaut EVAs. These will likely be provided by a robust network of orbiting assets in very high orbits, such as areostationary orbits. In the decade leading to that era, telecommunications orbits can be operated at areostationary orbit that can support a significant population of robotic precursor missions and build the network capabilities needed for the human spaceflight era. Telecommunications orbiters of modest size and cost, delivered by Solar Electric Propulsion to areostationary orbit, can provide continuous access at very high data rates to users on the surface and in Mars orbit.In the era of human spaceflight to Mars very high rate andreliable relay services will be needed to serve a largenumber of supporting vehicles, habitats, and orbiters, aswell as astronaut EVAs. These could be provided by arobust network of orbiting assets in very high orbits. In thedecade leading to that era, telecommunications orbiterscould be operated at areostationary orbit that could support asignificant population of robotic precursor missions andbuild the network capabilities needed for the humanspaceflight era. These orbiters could demonstrate thecapabilities and services needed for the future but withoutthe high bandwidth and high reliability requirements neededfor human spaceflight.Telecommunications orbiters of modest size and cost,delivered by Solar Electric Propulsion to areostationaryorbit, could provide continuous access at very high datarates to users on the surface and in Mars orbit. Twoexamples highlighting the wide variety of orbiter deliveryand configuration options were shown that could providehigh-performance service to users.

Empowerment of Teachers in Implementing Thematic Learning Method

ERIC Educational Resources Information Center

Istiningsih

2017-01-01

The way of looking at something in the present era is different from the past era. In the past era, something is looked at partially. The effect of this situation is harm for the life. At present era and the future, look at something should comprehensively and integral. The profile of human beings who are able to think comprehensively and integral…

Knowledge Based Artificial Augmentation Intelligence Technology: Next Step in Academic Instructional Tools for Distance Learning

ERIC Educational Resources Information Center

Crowe, Dale; LaPierre, Martin; Kebritchi, Mansureh

2017-01-01

With augmented intelligence/knowledge based system (KBS) it is now possible to develop distance learning applications to support both curriculum and administrative tasks. Instructional designers and information technology (IT) professionals are now moving from the programmable systems era that started in the 1950s to the cognitive computing era.…

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

ERIC Educational Resources Information Center

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

2011-01-01

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

Leading for Innovative Practice: Melding Theories of Organizational Change, Adult Learning, and Conditions of Learning

ERIC Educational Resources Information Center

Mirci, Philip S.; Hensley, Phyllis A.

2010-01-01

We live in an era of unique challenges requiring us to face a new reality mired in information overload for the 21st Century. This new reality emphasizes the critical need for educational leaders who can think and act systemically rather than bureaucratically. The bureaucratic model inherited from the Industrial Era still prevails in many…

AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams.

PubMed

Chen, Qiuwen; Luley, Ryan; Wu, Qing; Bishop, Morgan; Linderman, Richard W; Qiu, Qinru

2018-05-01

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

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.

Advancing Variable Star Astronomy: The Centennial History of the American Association of Variable Star Observers

NASA Astrophysics Data System (ADS)

Williams, Thomas R.; Saladyga, Michael

2011-05-01

Preface; Part I. Pioneers in Variable Star Astronomy Prior to 1909: 1. The emergence of variable star astronomy - a need for observations; 2. A need for observers; Part II. The Founding of the AAVSO - The William Tyler Olcott Era: 3. The amateur's amateur; 4. Amateurs in the service of science; Part III. The Leon Campbell Era: 5. Leon Campbell to the rescue; 6. Formalizing relationships; 7. The Pickering Memorial Endowment; 8. Fading of the Old Guard; 9. Growing pains and distractions; Part IV. The Service Bureau - The Margaret Mayall Era: 10. Learning about independence; 11. Eviction from Harvard College Observatory; 12. Actions and reactions; 13. In search of a home; 14. Survival on Brattle Street; 15. AAVSO achievements; 16. Breathing room on Concord Avenue; Part V. Analysis and Science: The Janet Mattei Era: 17. The growth of a director; 18. Learning the ropes the hard way; 19. Managing with renewed confidence; 20. Expanding the scientific charter; Part VI. Accelerating Observational Science - The Arne Henden Era: 21. Bridging the gap; 22. Accelerating the science - the Henden era begins; Epilogue; Appendices; Index.

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.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network.

PubMed

Deng, Lei; Wu, Hongjie; Liu, Chuyao; Zhan, Weihua; Zhang, Jingpu

2018-06-01

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage. Copyright © 2018 Elsevier Ltd. All rights reserved.

Machine-Learning Techniques Applied to Antibacterial Drug Discovery

PubMed Central

Durrant, Jacob D.; Amaro, Rommie E.

2014-01-01

The emergence of drug-resistant bacteria threatens to catapult humanity back to the pre-antibiotic era. Even now, multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the resulting vacuum. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics in an academic setting, leading to improved hit rates and faster transitions to pre-clinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. PMID:25521642

Vocational Education and Training for Life Long Learning in the Information Era. IVETA [International Vocational Education and Training Association] Conference Proceedings (Hong Kong, China, August 6-9, 2000).

ERIC Educational Resources Information Center

Vocational Training Council (Hong Kong).

This document contains 123 papers from an international conference on vocational education and training (VET) for lifelong learning in the information era. The papers focus on the following themes: (1) societal and ethical issues; (2) human resource development and personnel training; (3) international issues; (4) information technology in VET;…

Aerosol Concentration, Size, Hygroscopicity and MEE, Globally: What Do We Need to Know and How Can We Know It?

NASA Technical Reports Server (NTRS)

Kahn, Ralph

2017-01-01

Organizers of the Symposium Clouds, their Properties, and their Climate Feedbacks - What Have We Learned in the Satellite Era, held at Columbia University, NASAGISS June 6-8, 2017 plan to post the presented talks to an online website. http:www.gewex.orgeventclouds-their-properties-and-their-climate-feedbacks-what-have-we-learned-in-the-satellite-era?instance_id293534

Pneumonia's second wind? A case study of the global health network for childhood pneumonia.

PubMed

Berlan, David

2016-04-01

Advocacy, policy, research and intervention efforts against childhood pneumonia have lagged behind other health issues, including malaria, measles and tuberculosis. Accelerating progress on the issue began in 2008, following decades of efforts by individuals and organizations to address the leading cause of childhood mortality and establish a global health network. This article traces the history of this network's formation and evolution to identify lessons for other global health issues. Through document review and interviews with current, former and potential network members, this case study identifies five distinct eras of activity against childhood pneumonia: a period of isolation (post WWII to 1984), the duration of WHO's Acute Respiratory Infections (ARI) Programme (1984-1995), Integrated Management of Childhood illness's (IMCI) early years (1995-2003), a brief period of network re-emergence (2003-2008) and recent accelerating progress (2008 on). Analysis of these eras reveals the critical importance of building a shared identity in order to form an effective network and take advantage of emerging opportunities. During the ARI era, an initial network formed around a relatively narrow shared identity focused on community-level care. The shift to IMCI led to the partial dissolution of this network, stalled progress on addressing pneumonia in communities and missed opportunities. Frustrated with lack of progress on the issue, actors began forming a network and shared identity that included a broad spectrum of those whose interests overlap with pneumonia. As the network coalesced and expanded, its members coordinated and collaborated on conducting and sharing research on severity and tractability, crafting comprehensive strategies and conducting advocacy. These network activities exerted indirect influence leading to increased attention, funding, policies and some implementation. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2015; all rights reserved.

Social media, surgeons, and the Internet: an era or an error?

PubMed

Azu, Michelle C; Lilley, Elizabeth J; Kolli, Aparna H

2012-05-01

According to the National Research Corporation, 1 in 5 Americans use social media sites to obtain healthcare information. Patients can easily access information on medical conditions and medical professionals; however physicians may not be aware of the nature and impact of this information. All physicians must learn to use the Internet to their advantage and be acutely aware of the disadvantages. Surgeons are in a unique position because, unlike in the primary care setting, less time is spent developing a long-term relationship with the patient. In this literature review, we discuss the impact of the Internet, social networking websites, and physician rating websites and make recommendations for surgeons about managing digital identity and maintaining professionalism.

The Analysis of RDF Semantic Data Storage Optimization in Large Data Era

NASA Astrophysics Data System (ADS)

He, Dandan; Wang, Lijuan; Wang, Can

2018-03-01

With the continuous development of information technology and network technology in China, the Internet has also ushered in the era of large data. In order to obtain the effective acquisition of information in the era of large data, it is necessary to optimize the existing RDF semantic data storage and realize the effective query of various data. This paper discusses the storage optimization of RDF semantic data under large data.

Pneumonia’s second wind? A case study of the global health network for childhood pneumonia

PubMed Central

Berlan, David

2016-01-01

Advocacy, policy, research and intervention efforts against childhood pneumonia have lagged behind other health issues, including malaria, measles and tuberculosis. Accelerating progress on the issue began in 2008, following decades of efforts by individuals and organizations to address the leading cause of childhood mortality and establish a global health network. This article traces the history of this network’s formation and evolution to identify lessons for other global health issues. Through document review and interviews with current, former and potential network members, this case study identifies five distinct eras of activity against childhood pneumonia: a period of isolation (post WWII to 1984), the duration of WHO’s Acute Respiratory Infections (ARI) Programme (1984–1995), Integrated Management of Childhood illness’s (IMCI) early years (1995–2003), a brief period of network re-emergence (2003–2008) and recent accelerating progress (2008 on). Analysis of these eras reveals the critical importance of building a shared identity in order to form an effective network and take advantage of emerging opportunities. During the ARI era, an initial network formed around a relatively narrow shared identity focused on community-level care. The shift to IMCI led to the partial dissolution of this network, stalled progress on addressing pneumonia in communities and missed opportunities. Frustrated with lack of progress on the issue, actors began forming a network and shared identity that included a broad spectrum of those whose interests overlap with pneumonia. As the network coalesced and expanded, its members coordinated and collaborated on conducting and sharing research on severity and tractability, crafting comprehensive strategies and conducting advocacy. These network activities exerted indirect influence leading to increased attention, funding, policies and some implementation. PMID:26438780

Ubiquitous Learning Environments in Higher Education: A Scoping Literature Review

ERIC Educational Resources Information Center

Virtanen, Mari Aulikki; Haavisto, Elina; Liikanen, Eeva; Kääriäinen, Maria

2018-01-01

Ubiquitous learning and the use of ubiquitous learning environments heralds a new era in higher education. Ubiquitous learning environments enhance context-aware and seamless learning experiences available from any location at any time. They support smooth interaction between authentic and digital learning resources and provide personalized…

In Abundance: Networked Participatory Practices as Scholarship

ERIC Educational Resources Information Center

Stewart, Bonnie E.

2015-01-01

In an era of knowledge abundance, scholars have the capacity to distribute and share ideas and artifacts via digital networks, yet networked scholarship often remains unrecognized within institutional spheres of influence. Using ethnographic methods including participant observation, interviews, and document analysis, this study investigates…

Cancer Prevention in the Precision Medicine Era | Division of Cancer Prevention

Cancer.gov

Speaker | Timothy R. Rebbeck, PhD will present "Cancer Prevention in the Precision Medicine Era" on March 20, 2018, from 11:00 am - 12:00 pm at the NCI Shady Grove Campus. Learn more about this lecture.

New Perspectives on Teaching and Working with Languages in the Digital Era

ERIC Educational Resources Information Center

Pareja-Lora, Antonio, Ed.; Calle-Martínez, Cristina, Ed.; Rodríguez-Arancón, Pilar, Ed.

2016-01-01

This volume offers a comprehensive, up-to-date, empirical and methodological view over the new scenarios and environments for language teaching and learning recently emerged (e.g. blended learning, e-learning, ubiquitous learning, social learning, autonomous learning or lifelong learning), and also over some of the new approaches to language…

Virtual University: A Peer to Peer Open Education Network

ERIC Educational Resources Information Center

Razavi, Amir R.; Strommen-Bakhtiar, Abbas; Krause, Paul

2011-01-01

The world is currently going through a transitional period, moving from the Service era to the Information era. Rapid societal and technological innovations are changing the way we live, communicate, and work. As the rate of the technological/societal change increases, pressure on educational institutions also increases. This pressure is…

Professional Boundaries in the Era of the Internet

ERIC Educational Resources Information Center

Gabbard, Glen O.; Kassaw, Kristin A.; Perez-Garcia, Gonzalo

2011-01-01

Objective: The era of the Internet presents new dilemmas in educating psychiatrists about professional boundaries. The objective of this overview is to clarify those dilemmas and offer recommendations for dealing with them. Method: The characteristics of social networking sites, blogs, and search engines are reviewed with a specific focus on their…

Twelve tips for using social media as a medical educator.

PubMed

Kind, Terry; Patel, Pradip D; Lie, Désirée; Chretien, Katherine C

2014-04-01

We now live, learn, teach and practice medicine in the digital era. Social networking sites are used by at least half of all adults. Engagement with social media can be personal, professional, or both, for health-related and educational purposes. Use is often public. Lapses in professionalism can have devastating consequences, but when used well social media can enhance the lives of and learning by health professionals and trainees, ultimately for public good. Both risks and opportunities abound for individuals who participate, and health professionals need tips to enhance use and avoid pitfalls in their use of social media and to uphold their professional values. This article draws upon current evidence, policies, and the authors' experiences to present best practice tips for health professions educators, trainees, and students to build a framework for navigating the digital world in a way that maintains and promotes professionalism. These practical tips help the newcomer to social media get started by identifying goals, establishing comfort, and connecting. Furthermore, users can ultimately successfully contribute, engage, learn, and teach, and model professional behaviors while navigating social media.

Twenty-First Century Learning: Communities, Interaction and Ubiquitous Computing

ERIC Educational Resources Information Center

Leh, Amy S.C.; Kouba, Barbara; Davis, Dirk

2005-01-01

Advanced technology makes 21st century learning, communities and interactions unique and leads people to an era of ubiquitous computing. The purpose of this article is to contribute to the discussion of learning in the 21st century. The paper will review literature on learning community, community learning, interaction, 21st century learning and…

Experience, Reflect, Critique: The End of the "Learning Cycles" Era

ERIC Educational Resources Information Center

Seaman, Jayson

2008-01-01

According to prevailing models, experiential learning is by definition a stepwise process beginning with direct experience, followed by reflection, followed by learning. It has been argued, however, that stepwise models inadequately explain the holistic learning processes that are central to learning from experience, and that they lack scientific…

Web Mining: Machine Learning for Web Applications.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chau, Michael

2004-01-01

Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

What we learned from the Dust Bowl: lessons in science, policy, and adaptation.

PubMed

McLeman, Robert A; Dupre, Juliette; Berrang Ford, Lea; Ford, James; Gajewski, Konrad; Marchildon, Gregory

2014-01-01

This article provides a review and synthesis of scholarly knowledge of Depression-era droughts on the North American Great Plains, a time and place known colloquially as the Dust Bowl era or the Dirty Thirties. Recent events, including the 2008 financial crisis, severe droughts in the US corn belt, and the release of a popular documentary film, have spawned a resurgence in public interest in the Dust Bowl. Events of the Dust Bowl era have also proven in recent years to be of considerable interest to scholars researching phenomena related to global environmental change, including atmospheric circulation, drought modeling, land management, institutional behavior, adaptation processes, and human migration. In this review, we draw out common themes in terms of not only what natural and social scientists have learned about the Dust Bowl era itself, but also how insights gained from the study of that period are helping to enhance our understanding of climate-human relations more generally.

CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change

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

Anderson-Teixeira, Kristina J.; Davies, Stuart J.; Bennett, Amy C.

2014-09-25

Global change is impacting forests worldwide, threatening biodiversity and ecosystem services, including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamic research sites useful for characterizing forest responses to global change. The broad suite of measurements made at the CTFS-ForestGEO sites make it possible to investigate the complex ways in which global change is impacting forest dynamics. ongoing research across the network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forestmore » diversity and dynamics in a era of global change« less

The New ERA of Journal Ranking: The Consequences of Australia's Fraught Encounter with "Quality"

ERIC Educational Resources Information Center

Cooper, Simon; Poletti, Anna

2011-01-01

Ranking scholarly journals forms a major feature of the Excellence in Research for Australia (ERA) initiative. We argue this process is not only a flawed system of measurement, but more significantly erodes the very contexts that produce "quality" research. We argue that collegiality, networks of international research, the…

Enabling Remote Activity: Using mobile technology for remote participation in geoscience fieldwork

NASA Astrophysics Data System (ADS)

Davies, Sarah; Collins, Trevor; Gaved, Mark; Bartlett, Jessica; Valentine, Chris; McCann, Lewis

2010-05-01

Field-based activities are regarded as essential to the development of a range of professional and personal skills within the geosciences. Students enjoy field activities, preferring these to learning with simulations (Spicer and Stratford 2001), and these improve deeper learning and understanding (Kern and Carpenter, 1984; Elkins and Elkins, 2007). However, some students find it difficult to access these field-based learning opportunities. Field sites may be remote and often require travel across uneven, challenging or potentially dangerous terrain. Mobility-impaired students are particularly limited in their opportunities to participate in field-based learning activities and, as higher education institutions have a responsibility to provide inclusive opportunities for students (UK Disability Discrimination Act 1995, UK Special Education Needs and Disability Rights Act 2001), the need for inclusive fieldwork learning is being increasingly recognised. The Enabling Remote Activity (ERA) project has been investigating how mobile communications technologies might allow field learning experiences to be brought to students who would otherwise find it difficult to participate, and also to enhance activities for all participants. It uses a rapidly deployable, battery-powered wireless network to transmit video, audio, and high resolution still images to connect participants at an accessible location with participants in the field. Crucially, the system uses a transient wireless network, allowing multiple locations to be explored during a field visit, and for plans to be changed dynamically if required. Central to the concept is the requirement for independent investigative learning: students are enabled to participate actively in the learning experience and to direct the investigations, as opposed to being simply remote viewers of the experience. Two ways of using the ERA system have been investigated: remote access and collaborative groupwork. In 2006 and 2008 remote access was used to enable mobility-impaired students to take part in and complete a field course. This involved connecting the student in an accessible vehicle located close to the field site, via a wireless network, to a geologist in the field. The geologist worked alongside the general body of students and the field tutor as each geological site was investigated. Two-way communications allowed the student to guide the geologist to provide video panoramas of the area, to select areas of interest for further study and to obtain high resolution images of specific points. The students were able to work through the field activities alongside the rest of the student group. A collaborative groupwork trial (2007) was used to connect two groups of students; one in an accessible laboratory, the other at a field site. Traditionally, students collect data in the field and analyze it on return to the laboratory; this system proposes a more rapid collection and analysis procedure, with information being transmitted between sites with field and laboratory participants having their own distinct, significant roles within the learning activity. This project recently received an award at the 2008 Handheld Learning Conference and a HEFCE sponsored Open University Teaching Award. In contrast to the use of ‘virtual fieldwork' that aims to provide simulations or a resource for a student to use, the focus of this project is on how technology can be used to support actual fieldwork activities. This approach has been trialled now over three field seasons, with students using the system to remotely participate in fieldwork activities. Interviews with tutors and students have shown that this was perceived as valuable and allowed participants to achieve the learning objectives of the course alongside their peers. The challenges of remote fieldwork concern the co-ordination of students' activities, the integration of remote and field activities and practical issues of lightweight, easy-to-use, robust technologies and the provision of a reliable communications network. References Elkins, J.T. & Elkins, N.M.L. (2007) Teaching geology in the field: significant geoscience concept gains in entirely field-based introductory geology courses. Journal of Geoscience Education, 55 (2), 126-132. Kern, E. and Carpenter, J. (2004). Enhancement of student values, interests and attitudes in Earth Science through a field-oriented approach. Journal of Geological Education, 32 (5), 299-305. Spicer, J. I. and Stratford, J. (2001) Student perceptions of a virtual field trip to replace a real field trip. Journal of Computer Assisted Learning, 17(4), 345-354.

Healthy Families America state systems development: an emerging practice to ensure program growth and sustainability.

PubMed

Friedman, Lori; Schreiber, Lisa

2007-01-01

In an era of fiscal constraints and increased accountability for social service programs, having a centralized and efficient infrastructure is critical. A well-functioning infrastructure helps a state reduce duplication of services, creates economies of scale, coordinates resources, supports high-quality site development and promotes the self-sufficiency and growth of community-based programs. Throughout the Healthy Families America home visitation network, both program growth and contraction have been managed by in-state collaborations, referred to as "state systems." This article explores the research base that supports the rationale for implementing state systems, describes the evolution of state systems for Healthy Families America, and discusses the benefits, challenges and lessons learned of utilizing a systems approach.

Learning How to Learn: Implications for Non Traditional Adult Students

ERIC Educational Resources Information Center

Tovar, Lynn A.

2008-01-01

In this article, learning how to learn for non traditional adult students is discussed with a focus on police officers and firefighters. Learning how to learn is particularly relevant for all returning non-traditional adults; however in the era of terrorism it is critical for the public safety officers returning to college after years of absence…

Modeling Students' Readiness to Adopt Mobile Learning in Higher Education: An Empirical Study

ERIC Educational Resources Information Center

Al-Adwan, Ahmad Samed; Al-Madadha, Amr; Zvirzdinaite, Zahra

2018-01-01

Mobile devices are increasingly coming to penetrate people's daily lives. Mobile learning (m-learning) is viewed as key to the coming era of electronic learning (e-learning). In the meantime, the use of mobile devices for learning has made a significant contribution to delivering education among higher education students worldwide. However, while…

Inquiry-Based Learning in China: Lesson Learned for School Science Practices

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2014-01-01

Inquiry-based learning is widely considered for science education in this era. This study aims to explore inquiry-based learning in teacher preparation program and the findings will help us to understanding what inquiry-based classroom is and how inquiry-based learning are. Data were collected by qualitative methods; classroom observation,…

Image-based deep learning for classification of noise transients in gravitational wave detectors

NASA Astrophysics Data System (ADS)

Razzano, Massimiliano; Cuoco, Elena

2018-05-01

The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.

Dedicated Operating Room Teams and Clinical Outcomes in an Enhanced Recovery after Surgery Pathway for Colorectal Surgery.

PubMed

Grant, Michael C; Hanna, Andrew; Benson, Andrew; Hobson, Deborah; Wu, Christopher L; Yuan, Christina T; Rosen, Michael; Wick, Elizabeth C

2018-03-01

Our aim was to determine whether the establishment of a dedicated operating room team leads to improved process measure compliance and clinical outcomes in an Enhanced Recovery after Surgery (ERAS) program. Enhanced Recovery after Surgery programs involve the application of bundled best practices to improve the value of perioperative care. Successful implementation and sustainment of ERAS programs has been linked to compliance with protocol elements. Development of dedicated teams of anesthesia providers was a component of ERAS implementation. Intraoperative provider team networks (surgeons, anesthesiologists, and certified registered nurse anesthetists) were developed for all cases before and after implementation of colorectal ERAS. Four measures of centrality were analyzed in each network based on case assignments, and these measures were correlated with both rates of process measure compliance and clinical outcomes. Enhanced Recovery after Surgery provider teams led to a decrease in the closeness of anesthesiologists (p = 0.04) and significant increase in the clustering coefficient of certified registered nurse anesthetists (p = 0.005) compared with the pre-ERAS network. There was no significant change in centrality among surgeons (p = NS for all measures). Enhanced Recovery after Surgery designation among anesthesiologists and nurse anesthetists-whereby individual providers received an in-service on protocol elements and received compliance data was strongly associated with high compliance (>0.6 of measures; p < 0.001 for each group). In addition, high compliance was associated with a significant reduction in length of stay (p < 0.01), surgical site infection (p < 0.002), and morbidity (p < 0.009). Dedicated operating room teams led to increased centrality among anesthesia providers, which in turn not only increased compliance, but also improved several clinical outcomes. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Storytelling: An Ancient Human Technology and Critical-Creative Pedagogy for Transformative Learning

ERIC Educational Resources Information Center

Kalogeras, Stavroula

2013-01-01

In the era of e-learning, student-centered approaches and constructivists learning environments are critical success factors. The inherent interactivity of the Internet and the emotional engagement of story can lead to transformative learning experiences in media rich environments. This paper focuses on Web-Based Transmedia Storytelling…

Global Learning in a New Era

ERIC Educational Resources Information Center

Ramaley, Judith

2016-01-01

Our nation's colleges and universities frequently adapt their approach to education in response to the reality of social, economic and environmental challenges. Today the reality is that we are increasingly interconnected on a global scale. This new era of globalization impacts every facet of society, and it offers both an exciting blend of…

[Smart, Social, and Mobile: the future of Nephrology in the Era of Digital Health].

PubMed

Iannuzzella, Francesco; Murtas, Corrado; Bertolini, Riccardo; Corradini, Mattia; Pasquali, Sonia

2016-01-01

Healthcare is in the middle of a digital revolution. Physicians are adopting mobile apps that make them more effective and patients are taking to ones that give them more control over their healthcare. Mobile technology is changing Medicine. A new movement for free open access medical education (FOAMed) is growing through Social Media. E-learning is increasing access to new and exciting learning opportunities, deeply changing the traditional concept of continuous medical education. What will be the future of Nephrology in the era of Digital Health?

Networking CD-ROMs: The Decision Maker's Guide to Local Area Network Solutions.

ERIC Educational Resources Information Center

Elshami, Ahmed M.

In an era when patrons want access to CD-ROM resources but few libraries can afford to buy multiple copies, CD-ROM local area networks (LANs) are emerging as a cost-effective way to provide shared access. To help librarians make informed decisions, this manual offers information on: (1) the basics of LANs, a "local area network primer";…

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era.

PubMed

Zador, Zsolt; Huang, Wendy; Sperrin, Matthew; Lawton, Michael T

2018-06-01

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

Strengthening Integrated Learning: Towards a New Era for Pluriliteracies and Intercultural Learning

ERIC Educational Resources Information Center

Coyle, Do

2015-01-01

The expansion of Content and Language Integrated Learning (CLIL) on a global scale has brought to the fore challenges of how alternative, more holistic approaches to learning might transform classrooms into language-rich transcultural environments. Integrated approaches can offer learners opportunities to engage in meaning-making and language…

A Study on Information Technology Integrated Guided Iscovery Instruction towards Students' Learning Achievement and Learning Retention

ERIC Educational Resources Information Center

Shieh, Chich-Jen; Yu, Lean

2016-01-01

In the information explosion era with constant changes of information, educators have promoted various effective learning strategies for students adapting to the complex modern society. The impact and influence of traditional teaching method have information technology integrated modern instruction and science concept learning play an important…

Investigating Learner Affective Performance in Web-Based Learning by Using Entrepreneurship as a Metaphor

ERIC Educational Resources Information Center

Liu, Ming-Chou; Chi, Ming-Hsiao

2012-01-01

In the era of the Internet, factors which influence effective learning in a Web-based learning environment are well worth exploring. In addition to knowledge acquisition and skills training, affect is also an important factor, since successful learning requires excellent affective performance. Thus this study focuses on learners' affective…

ERA-MIN: The European network (ERA-NET) on non-energy raw materials

NASA Astrophysics Data System (ADS)

vidal, o.; christmann, p.; Bol, d.; Goffé, b.; Groth, m.; Kohler, e.; Persson Nelson, k.; Schumacher, k.

2012-04-01

Non-energy raw materials are vital for the EU's economy, and for the development of environmentally friendly technologies. The EU is the world's largest consumers of non-energy minerals, but it remains dependent on the importation of many metals, as its domestic production is limited to about 3% of world production. We will present the project ERA-MIN, which is an ERA-NET on the Industrial Handling of Raw Materials for European industries, financially supported by the European Commission. The main objectives of ERA-MIN are: 1) Mapping and Networking: interconnecting the members of the currently fragmented European mineral resources research area, to the aim of fostering convergence of public research programs, industry, research institutes, academia and the European Commission, 2) Coordinating: establishing a permanent mechanism for planning and coordination of the European non-energy mineral raw materials research community (ENERC). 3) Roadmapping: defining the most important scientific and technological challenges that should be supported by the EU and its state members, 4) Programming: designing a Joint European Research Programme model and implementating it into a call for proposals open to academic and industrial research. The topics of interest in ERA-MIN are the primary continental and marine resources, the secondary resources and their related technologies, substitution and material efficiency, along with transversal topics such as environmental impact, public policy support, mineral intelligence, and public education and teaching. Public scientific research is very central in the scope of the ERA-MIN activity, whose consortium is indeed lead by a public organisation of fundamental research. Thus, universities and public research organisations are warmly invited to play an active role in defining the scientific questions and challenges that shall determine the European Raw Materials Roadmap and should be addressed by joint programming at the European scale. The various levels of possible involvement in ERA-MIN for the interested stakeholders will be presented.

Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

DOT National Transportation Integrated Search

2010-05-01

Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

The Testing of English as a Second/Foreign Language in the Criterion-Referenced Era.

ERIC Educational Resources Information Center

Davidson, Fred

In the assessment of second/foreign language proficiency, we are entering the era of criterion-referenced assessment as language learning is being recognized as an integrative, multifaceted construct. Norm-referenced measurement (NRM) is compared with criterion-referenced measurement (CRM). CRM is characterized by attention to skill, whereas NRM…

Trends in Chicago's Schools across Three Eras of Reform

ERIC Educational Resources Information Center

Luppescu, Stuart; Allensworth, Elaine M.; Moore, Paul; de la Torre, Marisa; Murphy, James

2011-01-01

"Trends in Chicago's Schools Across Three Eras of Reform" finds that Chicago Public Schools has experienced tremendous growth in graduation rates over the past 20 years, but learning gains have been modest. The report tracks elementary and high school test scores and graduation rates in Chicago since 1988, when U.S. Secretary of…

Analysis of Computer Network Information Based on "Big Data"

NASA Astrophysics Data System (ADS)

Li, Tianli

2017-11-01

With the development of the current era, computer network and large data gradually become part of the people's life, people use the computer to provide convenience for their own life, but at the same time there are many network information problems has to pay attention. This paper analyzes the information security of computer network based on "big data" analysis, and puts forward some solutions.

Growing up at the intersection of the genomic era and the information age.

PubMed

Driessnack, Martha

2009-06-01

Children actively seek to make sense of their worlds based on the information they receive and their experience. For children growing up at the intersection of genomic era and information age, the array of information and experience continues to expand. This article highlights the importance of exploring these early contexts for learning, including the children's exposure to books and mass media, and the impact of early learning on later health literacy and behaviors. This article presents a case study discussing the inheritance of cystic fibrosis using the Harry Potter book series.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Literacy Learning in a Digitally Rich Humanities Classroom: Embracing Multiple, Collaborative, and Simultaneous Texts

ERIC Educational Resources Information Center

Buckley-Marudas, Mary Frances

2016-01-01

Understanding what happens when teachers embrace digital media for literacy learning is critical to realizing the potential of learning in the digital era. This article examines some of the ways that a high school teacher and his students leverage digital technologies for literacy learning in their humanities classrooms. The author introduces the…

Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge

ERIC Educational Resources Information Center

Macfadyen, Leah P.; Dawson, Shane; Pardo, Abelardo; Gaševic, Dragan

2014-01-01

In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the…

How the Young Generation Uses Digital Textbooks via Mobile Learning Terminals: Measurement of Elementary School Students in China

ERIC Educational Resources Information Center

Sun, Zhong; Jiang, Yuzhen

2015-01-01

Digital textbooks that offer multimedia features, interactive controls, e-annotation and learning process tracking are gaining increasing attention in today's mobile learning era, particularly with the rapid development of mobile learning terminals such as Apple's iPad series and Android-based models. Accordingly, this study explores how…

Learning-Centered Leadership and Teacher Learning in China: Does Trust Matter?

ERIC Educational Resources Information Center

Liu, Shengnan; Hallinger, Philip; Feng, Daming

2016-01-01

Purpose: In this era of global education reform, teacher professional learning (TPL) has emerged as a key factor in efforts to create sustainable school improvement. The same holds in Mainland China where ambitious curriculum reforms have been undertaken since 2000. The purpose of this paper is to examine the role of learning-centered leadership…

Preparing Students to Learn without Us

ERIC Educational Resources Information Center

Richardson, Will

2012-01-01

In this era of access, personalizing learning means allowing students to choose their own paths through the curriculum. However, the ability to learn what we want, when we want it, and with whomever we want creates a huge push against a system of education steeped in time-and-place learning. Fundamental changes need to happen in schools to provide…

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

ERIC Educational Resources Information Center

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

2017-01-01

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

Learning Literature in an Era of Change: Innovations in Teaching.

ERIC Educational Resources Information Center

Hickey, Dona, J. Ed.; Reiss, Donna, Ed.

This essay collection presents a range of teaching strategies developed by teachers of literature who have heard the call from students, employers, and academic administrators for more relevant learning experiences in an ever-changing world. Integrating critical theory and classroom experiences, the essays demonstrate how to foster learning,…

Using Stephen Crane's "Maggie" To Teach the Progressive Era.

ERIC Educational Resources Information Center

Gerwin, David; Manolios, Vassilios; Popodopoulos, Lia

1999-01-01

Outlines a lesson plan designed for an eleventh-grade U.S. history class in which the students learn about the Progressive Era by reading Stephen Crane's "Maggie: A Girl of the Streets." Explains that students analyze point of view, role play a talk show, write an essay, and complete a long-term research project. (CMK)

Teaching Foreign Policy in the Post-Cold War Era. ERIC Digest.

ERIC Educational Resources Information Center

Graseck, Susan

This ERIC Digest discusses issues relating to teaching about U.S. foreign policy in the changing international environment following the end of the Cold War era and the disintegration of the Soviet Union. The document treats: (1) the need and rationale for teaching and learning about current foreign policy issues; (2) main themes in foreign policy…

An Examination of Organizational Information Protection in the Era of Social Media: A Study of Social Network Security and Privacy Protection

ERIC Educational Resources Information Center

Maar, Michael C.

2013-01-01

This study investigates information protection for professional users of online social networks. It addresses management's desire to motivate their employees to adopt protective measures while accessing online social networks and to help their employees improve their proficiency in information security and ability to detect deceptive…

Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

PubMed

Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

2018-04-13

Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

Learning on-Location: Evaluating the Instructional Design for Just-in-Time Learning in Interdisciplinary Short-Term Study Abroad

ERIC Educational Resources Information Center

Coryell, J. E.

2013-01-01

In the current era of global society, adults need to cultivate cognitive and affective capabilities for interacting in a wide variety of work and living situations. Studying abroad can provide unique learning opportunities toward this end. Good intentions in offering study abroad experiences do not, however, always produce the kind of learning,…

Research on Biodiversity and Climate Change at a Distance: Collaboration Networks between Europe and Latin America and the Caribbean

PubMed Central

Dangles, Olivier; Loirat, Jean; Freour, Claire; Serre, Sandrine; Vacher, Jean; Le Roux, Xavier

2016-01-01

Biodiversity loss and climate change are both globally significant issues that must be addressed through collaboration across countries and disciplines. With the December 2015 COP21 climate conference in Paris and the recent creation of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), it has become critical to evaluate the capacity for global research networks to develop at the interface between biodiversity and climate change. In the context of the European Union (EU) strategy to stand as a world leader in tackling global challenges, the European Commission has promoted ties between the EU and Latin America and the Caribbean (LAC) in science, technology and innovation. However, it is not clear how these significant interactions impact scientific cooperation at the interface of biodiversity and climate change. We looked at research collaborations between two major regions—the European Research Area (ERA) and LAC—that addressed both biodiversity and climate change. We analysed the temporal evolution of these collaborations, whether they were led by ERA or LAC teams, and which research domains they covered. We surveyed publications listed on the Web of Science that were authored by researchers from both the ERA and LAC and that were published between 2003 and 2013. We also run similar analyses on other topics and other continents to provide baseline comparisons. Our results revealed a steady increase in scientific co-authorships between ERA and LAC countries as a result of the increasingly complex web of relationships that has been weaved among scientists from the two regions. The ERA-LAC co-authorship increase for biodiversity and climate change was higher than those reported for other topics and for collaboration with other continents. We also found strong differences in international collaboration patterns within the LAC: co-publications were fewest from researchers in low- and lower-middle-income countries and most prevalent from researchers in emerging countries like Mexico and Brazil. Overall, interdisciplinary publications represented 25.8% of all publications at the interface of biodiversity and climate change in the ERA-LAC network. Further scientific collaborations should be promoted 1) to prevent less developed countries from being isolated from the global cooperation network, 2) to ensure that scientists from these countries are trained to lead visible and recognized biodiversity and climate change research, and 3) to develop common study models that better integrate multiple scientific disciplines and better support decision-making. PMID:27304924

Research on Biodiversity and Climate Change at a Distance: Collaboration Networks between Europe and Latin America and the Caribbean.

PubMed

Dangles, Olivier; Loirat, Jean; Freour, Claire; Serre, Sandrine; Vacher, Jean; Le Roux, Xavier

2016-01-01

Biodiversity loss and climate change are both globally significant issues that must be addressed through collaboration across countries and disciplines. With the December 2015 COP21 climate conference in Paris and the recent creation of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), it has become critical to evaluate the capacity for global research networks to develop at the interface between biodiversity and climate change. In the context of the European Union (EU) strategy to stand as a world leader in tackling global challenges, the European Commission has promoted ties between the EU and Latin America and the Caribbean (LAC) in science, technology and innovation. However, it is not clear how these significant interactions impact scientific cooperation at the interface of biodiversity and climate change. We looked at research collaborations between two major regions-the European Research Area (ERA) and LAC-that addressed both biodiversity and climate change. We analysed the temporal evolution of these collaborations, whether they were led by ERA or LAC teams, and which research domains they covered. We surveyed publications listed on the Web of Science that were authored by researchers from both the ERA and LAC and that were published between 2003 and 2013. We also run similar analyses on other topics and other continents to provide baseline comparisons. Our results revealed a steady increase in scientific co-authorships between ERA and LAC countries as a result of the increasingly complex web of relationships that has been weaved among scientists from the two regions. The ERA-LAC co-authorship increase for biodiversity and climate change was higher than those reported for other topics and for collaboration with other continents. We also found strong differences in international collaboration patterns within the LAC: co-publications were fewest from researchers in low- and lower-middle-income countries and most prevalent from researchers in emerging countries like Mexico and Brazil. Overall, interdisciplinary publications represented 25.8% of all publications at the interface of biodiversity and climate change in the ERA-LAC network. Further scientific collaborations should be promoted 1) to prevent less developed countries from being isolated from the global cooperation network, 2) to ensure that scientists from these countries are trained to lead visible and recognized biodiversity and climate change research, and 3) to develop common study models that better integrate multiple scientific disciplines and better support decision-making.

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

PubMed

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

Coping with the Challenges of Open Online Education in Chinese Societies in the Mobile Era: NTHU OCW as a Case Study

ERIC Educational Resources Information Center

Young, Shelley Shwu-Ching; Hung, Hui-Chun

2014-01-01

In an era witnessing the rapid development of information technology, mobile devices have brought revolutionary changes to learning. A single conventional media platform is not enough for the various mobile devices. Technology-enriched educational environments supported by different devices are important research issues nowadays. To capture the…

Beginning Teachers' Experiential Learning in the Era of Common Core: A Case Study

ERIC Educational Resources Information Center

Dakwa, Loy

2016-01-01

This qualitative, single-case study described the professional learning experiences of a group of beginning teachers who participated in a California teacher induction program. The study contributes to an understanding of factors that form the foundation of professional learning as perceived by the participants. Furthermore, the study adds to…

Factors Impacting Students' Online Learning Experience in a Learner-Centred Course

ERIC Educational Resources Information Center

Wu, Y.

2016-01-01

Technologies bring a new era of content presentation for online teaching and learning. With more instructors adopting new tools to design online teaching materials, students are often put into learning contexts with certain new design components. Assessing learner experience and outcome in these contexts is challenging because of the complexity…

Designing a Resource Evolution Support System for Open Knowledge Communities

ERIC Educational Resources Information Center

Yang, Xianmin; Yu, Shengquan

2015-01-01

The continuous generation and evolution of digital learning resources is important for promoting open learning and meeting the personalized needs of learners. In the Web 2.0 era, open and collaborative authoring is becoming a popular method by which to create vast personalized learning resources in open knowledge communities (OKCs). However, the…

Teachers' Learning Communities: Catalyst for Change or a New Infrastructure for the Status Quo?

ERIC Educational Resources Information Center

Wood, Diane

2007-01-01

Background/Context: In an era of high stakes accountability, public school districts struggle to improve teaching and learning for all students. As a result, effective professional development approaches for teachers are a high priority. Recently, teachers' learning communities (LCs) have been recommended because successful LCs foster teacher…

Equipping Network Warfare: Industrial-Era Bureaucracies for Information-Era Weapons

DTIC Science & Technology

2009-04-01

Scientist , Air Force Research Lab Information Directorate; Dr. John Parker, Chief Technical Officer, GlimmerGlass Corportation; Mr. J. Michael Kretzer...operations.2 Additionally, the Air Force has established a functional management office within the Air Staff, has created a formal schoolhouse and...found that overall research and development costs exceeded their budget by 40% in Fiscal Year 2005 (up from 27% in 2000), while total acquisition costs

Naloxone for heroin, prescription opioid, and illicitly made fentanyl overdoses: Challenges and innovations responding to a dynamic epidemic.

PubMed

Fairbairn, Nadia; Coffin, Phillip O; Walley, Alexander Y

2017-08-01

Community-based overdose prevention programs first emerged in the 1990's and are now the leading public health intervention for overdose. Key elements of these programs are overdose education and naloxone distribution to people who use opioids and their social networks. We review the evolution of naloxone programming through the heroin overdose era of the 1990's, the prescription opioid era of the 2000's, and the current overdose crisis stemming from the synthetic opioid era of illicitly manufactured fentanyl and its analogues in the 2010's. We present current challenges arising in this new era of synthetic opioids, including variable potency of illicit drugs due to erratic adulteration of the drug supply with synthetic opioids, potentially changing efficacy of standard naloxone formulations for overdose rescue, potentially shorter overdose response time, and reports of fentanyl exposure among people who use drugs but are opioid naïve. Future directions for adapting naloxone programming to the dynamic opioid epidemic are proposed, including scale-up to new venues and social networks, new standards for post-overdose care, expansion of supervised drug consumption services, and integration of novel technologies to detect overdose and deliver naloxone. Copyright © 2017 Elsevier B.V. All rights reserved.

Lessons learned from modern military surgery.

PubMed

Beekley, Alec C; Starnes, Benjamin W; Sebesta, James A

2007-02-01

The era of global terrorism and asymmetric warfare heralded by the September 11, 2001 attacks on the United States have blurred the traditional lines between civilian and military trauma. The lessons learned by physicians in the theaters of war, particularly regarding the response to mass casualties, blast and fragmentation injuries, and resuscitation of casualties in austere environments, likely resonate strongly with civilian trauma surgeons in the current era. The evolution of a streamlined trauma system in the theaters of operations, the introduction of an in-theater institution review board process, and dedicated personnel to collect combat casualty data have resulted in improved data capture and realtime, on-the-scene research.

Library Resource-Sharing in the Network-Centric World.

ERIC Educational Resources Information Center

McGee, Rob

This paper discusses changes in services, technology, and organization as libraries prepare to enter the "network-centric library world." Part 1 addresses the transition from the analog era to the digital age, and the convergence of libraries and education, including opportunities for library leadership in Internet access, digital…

Challenges and opportunities for early-career Teaching-Focussed academics in the biosciences.

PubMed

Hubbard, Katharine; Gretton, Sarah; Jones, Katherine; Tallents, Lucy

2015-01-01

Twenty-seven percent of academics in UK Higher Education (HE) are in Teaching-Focussed positions, making major contributions to undergraduate programmes in an era of high student expectations when it comes to teaching quality. However, institutional support for Teaching-Focussed academics is often limited, both in terms of peer networking and opportunities for career development. As four early-career stage Teaching-Focussed academics working in a variety of institutions, we explore what motivated our choices to make teaching our primary academic activity, and the challenges that we have faced in doing so. In addition to highlighting the need for universities to fully recognise the achievements of teaching staff, we discuss the role that the various biosciences learned societies have in supporting Teaching-Focussed academics. We identify that there is a need for the learned societies to come together and pool their expertise in this area. The fragmented nature of the Teaching-Focussed academic community means that clear sources of national support are needed in order to best enable the next generation of bioscience educators to reach their full potential.

Challenges and opportunities for early-career Teaching-Focussed academics in the biosciences

PubMed Central

Hubbard, Katharine; Gretton, Sarah; Jones, Katherine; Tallents, Lucy

2015-01-01

Twenty-seven percent of academics in UK Higher Education (HE) are in Teaching-Focussed positions, making major contributions to undergraduate programmes in an era of high student expectations when it comes to teaching quality. However, institutional support for Teaching-Focussed academics is often limited, both in terms of peer networking and opportunities for career development. As four early-career stage Teaching-Focussed academics working in a variety of institutions, we explore what motivated our choices to make teaching our primary academic activity, and the challenges that we have faced in doing so. In addition to highlighting the need for universities to fully recognise the achievements of teaching staff, we discuss the role that the various biosciences learned societies have in supporting Teaching-Focussed academics. We identify that there is a need for the learned societies to come together and pool their expertise in this area. The fragmented nature of the Teaching-Focussed academic community means that clear sources of national support are needed in order to best enable the next generation of bioscience educators to reach their full potential. PMID:25977754

Creativity in the Era of Social Networking: A Case Study at Tertiary Education in the Greek Context

ERIC Educational Resources Information Center

Theodotou, Evgenia; Papastathopoulos, Avraam

2015-01-01

This paper investigates the utilization of a social network tool in order to promote creativity in higher education. Buddypress was selected as a social network tool and de Bono's "6 thinking hats" as a creativity strategy. The participants were 17 undergraduate students from a case study in a University in Greece in the field of social…

Information-Technology Based Physics Education

NASA Astrophysics Data System (ADS)

Kim, J. S.; Lee, K. H.

2001-04-01

Developing countries emphasize expansion of the educated population but demand for quality improvement follows later. Current science education reform is driven in part by post cold war restructuring of the global economy and associated focus on the education of a more scientifically literate society, due to the industrial change from labor-intensive to high-technology type, and the societal change inherent in the present information era. Industry needs employees of broad and flexible background with inter disciplinary training, engineers with better physics training, and well trained physicists. Education researches have proved that active-learning based methods are superior to the traditional methods and the information technology (IT) has lot to offer in this. Use of IT for improving physics education is briefly discussed with prospects for collaboration in the Asia-Pacific region via Asian Physics Education Network (ASPEN), UNESCO University Foundation Course in Physics (UUFCP), etc.

Changing outcomes after heart transplantation in patients with amyloid cardiomyopathy.

PubMed

Davis, Margot K; Lee, Peter H U; Witteles, Ronald M

2015-05-01

Amyloid cardiomyopathy (ACM) is associated with a poor prognosis. Previous reports have suggested unfavorable post-heart transplant (HT) survival in this population compared with other HT recipients. Data from the United Network for Organ Sharing (UNOS) registry were used to study outcomes among ACM patients undergoing HT in the modern era (Era 2, 2008 to 2013) as compared with the historical era (Era 1, 1987 to 2007). One hundred eighty-eight ACM patients underwent primary single-organ HT. Ninety-seven patients (51.6%) were transplanted in Era 1 and 91 (48.4%) in Era 2. ACM patients undergoing HT in Era 2 were older (p < 0.0001), had higher body mass index (p = 0.008) and longer ischemic times (p = 0.02), and were more likely to be African-American (p < 0.0001), UNOS Status 1A (p < 0.0001), male (p = 0.01) and highly sensitized (p < 0.0001) compared with those in Era 1. Compared with patients with other etiologies of restrictive cardiomyopathy (RCM; n = 339 in Era 1, n = 164 in Era 2), adjusted hazard ratios (HRs) for post-HT mortality of ACM were 2.08 (p < 0.0001) in Era 1 and 1.22 (p = not statistically significant) in Era 2. Adjusted HRs for mortality of ACM vs all other diagnoses (n = 36,334 in Era 1, n = 9,225 in Era 2) were 1.84 (p < 0.0001) in Era 1 and 1.38 (p = NS) in Era 2. Although post-HT survival did not change with time among non-ACM RCM patients, post-HT mortality was lower in Era 2 compared with Era 1 among ACM patients (HR 0.49, p = 0.03). Although historically associated with inferior survival, post-HT outcomes in ACM patients in the modern era are now approaching those of non-ACM patients. Changes in patients' demographics suggest that this may be related to improved patient selection, including an increased proportion of patients with transthyretin ACM. HT should be considered for appropriate candidates with ACM. Copyright © 2015 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Teaching and Learning in the Digital Era: A Case Study of Video-Conference Lectures from Japan to Australia

ERIC Educational Resources Information Center

Yasumoto, Seiko

2014-01-01

"Blended learning" has been attracting academic interest catalysed by the advance of mixed-media technology and has significance for the global educational community and evolutionary development of pedagogical approaches to optimise student learning. This paper examines one aspect of blended teaching of Japanese language and culture in…

The Virtual History Museum: Learning U.S. History in Diverse Eighth Grade Classrooms

ERIC Educational Resources Information Center

Okolo, Cynthia M.; Englert, Carol Sue; Bouck, Emily C.; Heutsche, Anne; Wang, Hequn

2011-01-01

History is an important but often overlooked content area for all students in this current era of accountability. Yet instruction in history can help students become problem solvers and learn to make interpretations from multiple perspectives. This article reports the results of a pilot study examining history learning across three groups of…

District Administrator Perspectives on Student Learning in an Era of Standards and Accountability: A Collective Frame Analysis

ERIC Educational Resources Information Center

Anderson, Stephen E.; Macri, Joelle Rodway

2009-01-01

Our analysis explores the agenda for student learning communicated in interviews with school district officials from four Ontario districts. Using research methods drawn from collective action framing theory, we identified six core frames and one broader frame in the discourse on student learning: (a) measureable academic achievement, (b)…

The "Tse Tsa Watle" Speaker Series: An Example of Ensemble Leadership and Generative Adult Learning

ERIC Educational Resources Information Center

McKendry, Virginia

2017-01-01

This chapter examines an Indigenous speaker series formed to foster intercultural partnerships at a Canadian university. Using ensemble leadership and generative learning theories to make sense of the project, the author argues that ensemble leadership is key to designing the generative learning adult learners need in an era of ambiguity.

Test Preparation in the Accountability Era: Toward a Learning-Oriented Approach

ERIC Educational Resources Information Center

Gebril, Atta

2018-01-01

The article introduces a learning-oriented approach to test preparation that could help in easing the tension between learning and assessment. The first part of the article discusses the different concepts that are usually used in the context of test preparation. The second section reports on the literature pertaining to the effects of test…

Action Learning: Developing Critical Competencies for Knowledge Era Workers

ERIC Educational Resources Information Center

Robinson, Greg

2005-01-01

For most of the twentieth century, the goal in education was the generation and dissemination of information. With the rise of technology and unlimited access to information, it is the ability to apply knowledge and learn from experience that is the new priority for employee development. Action learning, with its emphasis on action and reflection,…

Affective Teaching for Effective Learning: A Deleuzian Pedagogy for the (Corporate Era and) Chinese Context

ERIC Educational Resources Information Center

Fleming, David H.

2014-01-01

In this article I explore the pedagogical value of Gilles Deleuze and Félix Guattari's philosophical concepts for helping make an "event" of thought, with a view towards fostering deep learning in Chinese students' learning theory and criticism in a second language. Paying attention to the qualitative role of bodies, humour and…

Foucault, Confucius and the In-Service Learning of Experienced Teachers in an Era of Managerialism

ERIC Educational Resources Information Center

Huang, Hua

2018-01-01

By drawing on Foucault's theory of subjectification, this study presents a case study of two experienced teachers' in-service learning in the managerialist climate of Macau. The results indicate that the prevailing policies and administrative strategies on in-service learning served as the apparatus of managerialism working on teachers and…

A complex regulatory network controls aerobic ethanol oxidation in Pseudomonas aeruginosa: indication of four levels of sensor kinases and response regulators.

PubMed

Mern, Demissew S; Ha, Seung-Wook; Khodaverdi, Viola; Gliese, Nicole; Görisch, Helmut

2010-05-01

In addition to the known response regulator ErbR (former AgmR) and the two-component regulatory system EraSR (former ExaDE), three additional regulatory proteins have been identified as being involved in controlling transcription of the aerobic ethanol oxidation system in Pseudomonas aeruginosa. Two putative sensor kinases, ErcS and ErcS', and a response regulator, ErdR, were found, all of which show significant similarity to the two-component flhSR system that controls methanol and formaldehyde metabolism in Paracoccus denitrificans. All three identified response regulators, EraR (formerly ExaE), ErbR (formerly AgmR) and ErdR, are members of the luxR family. The three sensor kinases EraS (formerly ExaD), ErcS and ErcS' do not contain a membrane domain. Apparently, they are localized in the cytoplasm and recognize cytoplasmic signals. Inactivation of gene ercS caused an extended lag phase on ethanol. Inactivation of both genes, ercS and ercS', resulted in no growth at all on ethanol, as did inactivation of erdR. Of the three sensor kinases and three response regulators identified thus far, only the EraSR (formerly ExaDE) system forms a corresponding kinase/regulator pair. Using reporter gene constructs of all identified regulatory genes in different mutants allowed the hierarchy of a hypothetical complex regulatory network to be established. Probably, two additional sensor kinases and two additional response regulators, which are hidden among the numerous regulatory genes annotated in the genome of P. aeruginosa, remain to be identified.

CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change

Treesearch

Kristina J. Anderson-Teixeira; Stuart J. Davies; Amy C. Bennett; Erika B. Gonzalez-Akre; Helene C. Muller-Landau; S. Joseph Wright; Kamariah Abu Salim; Angélica M. Almeyda Zambrano; Alfonso Alonso; Jennifer L. Baltzer; Yves Basset; Norman A. Bourg; Eben N. Broadbent; Warren Y. Brockelman; Sarayudh Bunyavejchewin; David F. R. P. Burslem; Nathalie Butt; Min Cao; Dairon Cardenas; George B. Chuyong; Keith Clay; Susan Cordell; Handanakere S. Dattaraja; Xiaobao Deng; Matteo Detto; Xiaojun Du; Alvaro Duque; David L. Erikson; Corneille E.N. Ewango; Gunter A. Fischer; Christine Fletcher; Robin B. Foster; Christian P. Giardina; Gregory S. Gilbert; Nimal Gunatilleke; Savitri Gunatilleke; Zhanqing Hao; William W. Hargrove; Terese B. Hart; Billy C.H. Hau; Fangliang He; Forrest M. Hoffman; Robert W. Howe; Stephen P. Hubbell; Faith M. Inman-Narahari; Patrick A. Jansen; Mingxi Jiang; Daniel J. Johnson; Mamoru Kanzaki; Abdul Rahman Kassim; David Kenfack; Staline Kibet; Margaret F. Kinnaird; Lisa Korte; Kamil Kral; Jitendra Kumar; Andrew J. Larson; Yide Li; Xiankun Li; Shirong Liu; Shawn K.Y. Lum; James A. Lutz; Keping Ma; Damian M. Maddalena; Jean-Remy Makana; Yadvinder Malhi; Toby Marthews; Rafizah Mat Serudin; Sean M. McMahon; William J. McShea; Hervé R. Memiaghe; Xiangcheng Mi; Takashi Mizuno; Michael Morecroft; Jonathan A. Myers; Vojtech Novotny; Alexandre A. de Oliveira; Perry S. Ong; David A. Orwig; Rebecca Ostertag; Jan den Ouden; Geoffrey G. Parker; Richard P. Phillips; Lawren Sack; Moses N. Sainge; Weiguo Sang; Kriangsak Sri-ngernyuang; Raman Sukumar; I-Fang Sun; Witchaphart Sungpalee; Hebbalalu Sathyanarayana Suresh; Sylvester Tan; Sean C. Thomas; Duncan W. Thomas; Jill Thompson; Benjamin L. Turner; Maria Uriarte; Renato Valencia; Marta I. Vallejo; Alberto Vicentini; Tomáš Vrška; Xihua Wang; Xugao Wang; George Weiblen; Amy Wolf; Han Xu; Sandra Yap; Jess Zimmerman

2014-01-01

Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses...

Considerations on communications network protocols in deep space

NASA Technical Reports Server (NTRS)

Clare, L. P.; Agre, J. R.; Yan, T.

2001-01-01

Communications supporting deep space missions impose numerous unique constraints that impact the architectural choices made for cost-effectiveness. We are entering the era where networks that exist in deep space are needed to support planetary exploration. Cost-effective performance will require a balanced integration of applicable widely used standard protocols with new and innovative designs.

Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

PubMed

Wang, Edwin; Zou, Jinfeng; Zaman, Naif; Beitel, Lenore K; Trifiro, Mark; Paliouras, Miltiadis

2013-08-01

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

The Vietnam War: History, Learning, and Leadership.

ERIC Educational Resources Information Center

Edwards, Tricia

2002-01-01

Focuses on the curriculum entitled "Echoes from the Wall: History, Learning and Leadership through the Lens of the Vietnam War Era." Discusses the purpose of the materials. States that the curriculum incorporates primary resources into the classroom while making history more immediate to students. (CMK)

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking

PubMed Central

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2017-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992–1998), global regime formation through the FCTC negotiations (1999–2005), and philanthropic funding through the Bloomberg Initiative (2006–2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999–2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network. PMID:28596813

Machine Learning in the Big Data Era: Are We There Yet?

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

Sukumar, Sreenivas Rangan

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstandingmore » challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.« less

Trends in Wait-list Mortality in Children Listed for Heart Transplantation in the United States

PubMed Central

Singh, Tajinder P.; Almond, Christopher S.; Piercey, Gary; Gauvreau, Kimberlee

2014-01-01

We sought to evaluate trends in overall and race-specific pediatric heart transplant (HT) wait-list mortality in the United States (US) during the last 20 years. We identified all children <18 years old listed for primary HT in the US during 1989–2009 (N=8096, 62% white, 19% black, 13% Hispanic, 6% other) using the Organ Procurement and Transplant Network database. Wait-list mortality was assessed in 4 successive eras (1989–1994, 1995–1999, 2000–2004, and 2005–2009). Overall wait-list mortality declined in successive eras (26%, 23%, 18% and 13%, respectively). The decline across eras remained significant in adjusted analysis (hazard ratio [HR] 0.70 in successive eras, 95% confidence interval [CI] 0.67, 0.74) and was 67% lower for children listed during 2005–2009 vs. those listed during 1989–1994 (HR 0.33, CI 0.28, 0.39). In models stratified by race, wait-list mortality decreased in all racial groups in successive eras. In models stratified by era, minority children were not at higher risk of wait-list mortality in the most recent era. We conclude that the risk of wait-list mortality among US children listed for HT has decreased by two-thirds during the last 20 years. Racial gaps in wait-list mortality present variably in the past are not present in the current era. PMID:21883920

Learning in networks: individual teacher learning versus organizational learning in a regional health-promoting schools network.

PubMed

Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

2013-12-01

To change a school into a health-promoting organization, organizational learning is required. The evaluation of an Austrian regional health-promoting schools network provides qualitative data on the views of the different stakeholders on learning in this network (steering group, network coordinator and representatives of the network schools; n = 26). Through thematic analysis and deep-structure analyses, the following three forms of learning in the network were identified: (A) individual learning through input offered by the network coordination, (B) individual learning between the network schools, i.e. through exchange between the representatives of different schools and (C) learning within the participating schools, i.e. organizational learning. Learning between (B) or within the participating schools (C) seems to be rare in the network; concepts of individual teacher learning are prevalent. Difficulties detected relating to the transfer of information from the network to the member schools included barriers to organizational learning such as the lack of collaboration, coordination and communication in the network schools, which might be effects of the school system in which the observed network is located. To ensure connectivity of the information offered by the network, more emphasis should be put on linking health promotion to school development and the core processes of schools.

A New Era for Jefferson Lab

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

McKeown, R. D.; Montgomery, H. E.; Pennington, M. R.

On a cool Saturday morning in late April a seemingly endless stream of cars turned off Jefferson Avenue in Newport News, Virginia, bringing 12,000 people ages 1 to 91 to the Open House to learn more about “the new era in science” at the Thomas Jefferson National Accelerator Facility. Here, the visitors were dazzled by the complex equipment, the enthusiastic staff, and the advanced technology at the Laboratory.

How To Dance through Time. Volume II: Dances of the Ragtime Era, 1910-1920. [Videotape].

ERIC Educational Resources Information Center

Teten, Carol

This 59-minute VHS videotape is the second in a series of "How To Dance Through Time" videos. It provides 44 step combinations and how-to instructions to help viewers learn to dance the most popular dances of the early 20th century (the ragtime era), including: the wild animal dances (fox trot, horse trot, kangaroo hop, duck waddle, squirrel,…

A New Era for Jefferson Lab

DOE PAGES

McKeown, R. D.; Montgomery, H. E.; Pennington, M. R.

2016-09-16

On a cool Saturday morning in late April a seemingly endless stream of cars turned off Jefferson Avenue in Newport News, Virginia, bringing 12,000 people ages 1 to 91 to the Open House to learn more about “the new era in science” at the Thomas Jefferson National Accelerator Facility. Here, the visitors were dazzled by the complex equipment, the enthusiastic staff, and the advanced technology at the Laboratory.

GCF Mark IV development

NASA Technical Reports Server (NTRS)

Mortensen, L. O.

1982-01-01

The Mark IV ground communication facility (GCF) as it is implemented to support the network consolidation program is reviewed. Changes in the GCF are made in the area of increased capacity. Common carrier circuits are the medium for data transfer. The message multiplexing in the Mark IV era differs from the Mark III era, in that all multiplexing is done in a GCF computer under GCF software control, which is similar to the multiplexing currently done in the high speed data subsystem.

First Year Implementation of a Project-Based Learning Approach: The Need for Addressing Teachers' Orientations in the Era of Reform

ERIC Educational Resources Information Center

Rogers, Meredith A. Park; Cross, Dionne I.; Gresalfi, Melissa Sommerfeld; Trauth-Nare, Amy E.; Buck, Gayle A.

2011-01-01

The purpose of this study was to examine the extent to which three teachers' professional experience and existing orientations toward teaching and learning mathematics and science influenced their implementation of a project-based curriculum (i.e. project-based learning (PBL)). Data sources included interviews, videotapes of classroom activity,…

Teaching and Learning with ICT Tools: Issues and Challenges from Teachers' Perceptions

ERIC Educational Resources Information Center

Ghavifekr, Simin; Kunjappan, Thanusha; Ramasamy, Logeswary; Anthony, Annreetha

2016-01-01

In this digital era, ICT use in the classroom is important for giving students opportunities to learn and apply the required 21st century skills. Hence studying the issues and challenges related to ICT use in teaching and learning can assist teachers in overcoming the obstacles and become successful technology users. Therefore, the main purpose of…

Digital Technology Use by the Students and English Teachers and Self-Directed Language Learning

ERIC Educational Resources Information Center

Sert, Nehir; Boynuegri, Ebru

2017-01-01

The digital era is a new challenge for teachers. While children get acquainted with the digital technology before the age of six, teachers, who have encountered with the digital world at a later time in their lives, struggle with it. Self-directed learning, which is crucial for lifelong learning, can be enhanced by the use technology particularly…

A Mind for Adventure

ERIC Educational Resources Information Center

Strother, Mark A.

2007-01-01

Formal schooling began centuries before scientists would discover how the brains of children actually learn. Not surprisingly, traditional teaching was often boring and brain antagonistic. But great teachers in every era intuitively recognized what has now been validated by neuroscience: powerful learning is an adventure of the mind. Students,…

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, Ingo

2016-10-01

Radiative transfer retrievals have become the standard in modelling of exoplanetary transmission and emission spectra. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain.To address these issues, we have developed the Tau-REx (tau-retrieval of exoplanets) retrieval and the RobERt spectral recognition algorithms. Tau-REx is a bayesian atmospheric retrieval framework using Nested Sampling and cluster computing to fully map these large correlated parameter spaces. Nonetheless, data volumes can become prohibitively large and we must often select a subset of potential molecular/atomic absorbers in an atmosphere.In the era of open-source, automated and self-sufficient retrieval algorithms, such manual input should be avoided. User dependent input could, in worst case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is build to address these issues. RobERt is a deep belief neural (DBN) networks trained to accurately recognise molecular signatures for a wide range of planets, atmospheric thermal profiles and compositions. Using these deep neural networks, we work towards retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.In this talk I will discuss how neural networks and Bayesian Nested Sampling can be used to solve highly degenerate spectral retrieval problems and what 'dreaming' neural networks can tell us about atmospheric characteristics.

An Assessment of the Effectiveness of Computer-based Training for Newly Commissioned Surface Warfare Division Officers

DTIC Science & Technology

2009-08-24

expect from CBT. - x - For the most part, students prefer face-to-face learning to mediated instruction, with “ blended solutions” (a combination...in the era of correspondence courses.” 5. Blended Learning The concept of blended learning has existed at least as long as two classic meta...Zimmerman, 2001), blended learning is seen by a majority of critics as superior to CBT. Mackay and Stockport (2006) point out that e- learning

For Performance through Learning, Knowledge Management Is Critical Practice

ERIC Educational Resources Information Center

Gorelick, Carol; Tantawy-Monsou, Brigitte

2005-01-01

Purpose: This paper proposes that knowledge management is a system that integrates people, process and technology for sustainable results by increasing performance through learning. Definitions of knowledge, knowledge management and performance serve as a foundation. Design/methodology/approach: The model for the knowledge era proposed in this…

E-Learning and Lifelong Learning

ERIC Educational Resources Information Center

Mouzakitis, George S.; Tuncay, Nazime

2011-01-01

It is supported that the object of education is to provide results. Hence, it is of crucial importance to economic development globally. In our era, globalization is a highly disputable event with strong persuasive arguments and equally solid disagreements. The impact of globalization in our everyday activities has been increased. In parallel,…

A New Approach to Accountability: Creating Effective Learning Environments for Programs

ERIC Educational Resources Information Center

Surr, Wendy

2012-01-01

This article describes a new paradigm for accountability that envisions afterschool programs as learning organizations continually engaged in improving quality. Nearly 20 years into the era of results-based accountability, a new generation of afterschool accountability systems is emerging. Rather than aiming to test whether programs have produced…

A New Era for Educational Assessment

ERIC Educational Resources Information Center

Conley, David

2015-01-01

In this article, David Conley focuses on how to assess meaningful learning in ways that promote student achievement while simultaneously meeting system accountability needs. The article draws upon research that supports the notion that a major shift in educational assessment is needed in order to encourage and evaluate the kind of learning that…

Using Online Education Technologies to Support Studio Instruction

ERIC Educational Resources Information Center

Bender, Diane M.; Vredevoogd, Jon D.

2006-01-01

Technology is transforming the education and practice of architecture and design. The newest form of education is blended learning, which combines personal interaction from live class sessions with online education for greater learning flexibility (Abrams & Haefner, 2002). Reluctant to join the digital era are educators teaching studio courses…

Pedagogical Dramas and Transformational Play: Narratively Rich Games for Learning

ERIC Educational Resources Information Center

Barab, Sasha A.; Dodge, Tyler; Ingram-Goble, Adam; Pettyjohn, Patrick; Peppler, Kylie; Volk, Charlene; Solomou, Maria

2010-01-01

Although every era is met with the introduction of powerful technologies for entertainment and learning, videogames represent a new contribution binding the two and bearing the potential to create sustained engagement in a curricular drama where the player's knowledgeable actions shape an unfolding fiction within a designed world. Although…

Effective approach to spectroscopy and spectral analysis techniques using Matlab

NASA Astrophysics Data System (ADS)

Li, Xiang; Lv, Yong

2017-08-01

With the development of electronic information, computer and network, modern education technology has entered new era, which would give a great impact on teaching process. Spectroscopy and spectral analysis is an elective course for Optoelectronic Information Science and engineering. The teaching objective of this course is to master the basic concepts and principles of spectroscopy, spectral analysis and testing of basic technical means. Then, let the students learn the principle and technology of the spectrum to study the structure and state of the material and the developing process of the technology. MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. A proprietary programming language developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, Based on the teaching practice, this paper summarizes the new situation of applying Matlab to the teaching of spectroscopy. This would be suitable for most of the current school multimedia assisted teaching

Research on information security in big data era

NASA Astrophysics Data System (ADS)

Zhou, Linqi; Gu, Weihong; Huang, Cheng; Huang, Aijun; Bai, Yongbin

2018-05-01

Big data is becoming another hotspot in the field of information technology after the cloud computing and the Internet of Things. However, the existing information security methods can no longer meet the information security requirements in the era of big data. This paper analyzes the challenges and a cause of data security brought by big data, discusses the development trend of network attacks under the background of big data, and puts forward my own opinions on the development of security defense in technology, strategy and product.

'E-learning' modalities in the current era of Medical Education in Pakistan.

PubMed

Jawaid, Masood; Aly, Syed Moyn

2014-09-01

There are a number of e-Learning modalities, some or all of which may be used throughout a medical, dental, nursing or any other health related undergraduate curriculum. The purpose of this paper is to briefly describe what e-learning is along with some of the modalities, their common advantages and limitations. This publication ends with practical implications of these modalities for Pakistan.

A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

PubMed

Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

2015-04-01

Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

Improving National Results in Liver Transplantation Using Grafts From Donation After Cardiac Death Donors.

PubMed

Croome, Kristopher P; Lee, David D; Keaveny, Andrew P; Taner, C Burcin

2016-12-01

Published reports describing the national experience with liver grafts from donation after cardiac death (DCD) donors have resulted in reservations with their widespread utilization. The present study aimed to investigate if temporal improvements in outcomes have been observed on a national level and to determine if donor and recipient selection have been modified in a fashion consistent with published data on DCD use in liver transplantation (LT). Patients undergoing DCD LT between 2003 and 2014 were obtained from the United Network of Organ Sharing Standard Transplant Analysis and Research file and divided into 3 equal eras based on the date of DCD LT: era 1 (2003-2006), era 2 (2007-2010), and era 3 (2011-2014). Improvement in graft survival was seen between era 1 and era 2 (P = 0.001) and between era 2 and era 3 (P < 0.001). Concurrently, an increase in the proportion of patients with hepatocellular carcinoma and a decrease in critically ill patients, retransplant recipients, donor age, warm ischemia time greater than 30 minutes and cold ischemic time also occurred over the same period. On multivariate analysis, significant predictors of graft survival included: recipient age, biologic MELD score, recipient on ventilator, recipient hepatitis C virus + serology, donor age and cold ischemic time. In addition, even after adjustment for all of the aforementioned variables, both era 2 (hazard ratio, 0.81; confidence interval, 0.69-0.94; P = 0.007), and era 3 (hazard ratio, 0.61; confidence interval, 0.5-0.73; P < 0.001) had a protective effect compared to era 1. The national outcomes for DCD LT have improved over the last 12 years. This change was associated with modifications in both recipient and donor selection. Furthermore, an era effect was observed, even after adjustment for all recipient and donor variables on multivariate analysis.

Evolution of cosmic string networks

NASA Technical Reports Server (NTRS)

Albrecht, Andreas; Turok, Neil

1989-01-01

A discussion of the evolution and observable consequences of a network of cosmic strings is given. A simple model for the evolution of the string network is presented, and related to the statistical mechanics of string networks. The model predicts the long string density throughout the history of the universe from a single parameter, which researchers calculate in radiation era simulations. The statistical mechanics arguments indicate a particular thermal form for the spectrum of loops chopped off the network. Detailed numerical simulations of string networks in expanding backgrounds are performed to test the model. Consequences for large scale structure, the microwave and gravity wave backgrounds, nucleosynthesis and gravitational lensing are calculated.

Social Networking Sites' Influence on Travelers' Authentic Experience a Case Study of Couch Surfing

ERIC Educational Resources Information Center

Liu, Xiao

2013-01-01

This study explored travelers' experiences in the era of network hospitality 2.0 using CouchSurfing.org as a case study. The following research questions guided this study: 1) what experience does CouchSurfing create for travelers before, during and after their travel? 2) how does couch surfers' experience relate to authenticity in context of…

Design and Implementation of an Architectural Framework for Web Portals in a Ubiquitous Pervasive Environment

PubMed Central

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework. PMID:22346693

Design and implementation of an architectural framework for web portals in a ubiquitous pervasive environment.

PubMed

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal's gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world's largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework.

[Future trends in nursing education in Taiwan in the light of globalization].

PubMed

Lee, Sheuan; Lu, Ying-Chi; Yen, Wen-Jiuan; Lin, Shu-Chin

2004-08-01

The twenty-first century is the era of the knowledge-based economy. Its information networks developing rapidly, Taiwan has already entered an age of liberalization, diversity and globalization. Competition and change will be the norm. As globalization continues it will pose substantial problems for nursing education. Nursing is a service-oriented activity which has to develop constantly to meet the changing demands of the public as people start to live longer, society becomes more multi-cultural, the nature of diseases and other health problems changes and public policy, such as that on National Health Insurance, is modified. This article outlines the problems currently facing nursing education (i.e., the complexity of the educational system, shortcomings in the learning environment, curriculum design, the quality of faculty, evaluation methods, and the quality of students' English and Mathematics) to predict likely difficulties (i.e. student recruitment, the running of schools and the quality of clinical nurses) and trends in nursing education. (i.e. changes in the way schools are run in line with the impact of globalization, new teaching methods; faculty training and development, lifelong learning, and the internationalization of education.) The article should be of interest to nursing educators.

A CEFR-Based Computerized Adaptive Testing System for Chinese Proficiency

ERIC Educational Resources Information Center

Wang, Hsuan-Po; Kuo, Bor-Chen; Tsai, Ya-Hsun; Liao, Chen-Huei

2012-01-01

In the era of globalization, the trend towards learning Chinese as a foreign language (CFL) has become increasingly popular worldwide. The increasing demand in learning CFL has raised the profile of the Chinese proficiency test (CPT). This study will analyze in depth the inadequacy of current CPT's utilizing the common European framework of…

"World-Travelling": A Framework for Re-Thinking Teaching and Learning in Internationalised Higher Education

ERIC Educational Resources Information Center

Anderson, Vivienne

2014-01-01

In an era of unprecedented student mobility, increasingly diverse student populations in many national contexts, and globally interconnected environmental and social concerns, there is an urgent need to find new ways of thinking about teaching and learning. Static assumptions about so-called "Western" versus "non-Western"…

Prospects for Integrating Service Learning into Short-Term International Study

ERIC Educational Resources Information Center

Daly, Donna M.; Baker, Suzanne; Williams, Stephen J.

2014-01-01

In an era of significant social, political, and economic globalization, it is crucial for health and human services educators to adopt a more hands on international view vis-à-vis student education. This article presents information that will assist educators in extending domestic service learning concepts and activities into the undergraduate…

Assessment Approaches in Massive Open Online Courses: Possibilities, Challenges and Future Directions

ERIC Educational Resources Information Center

Xiong, Yao; Suen, Hoi K.

2018-01-01

The development of massive open online courses (MOOCs) has launched an era of large-scale interactive participation in education. While massive open enrolment and the advances of learning technology are creating exciting potentials for lifelong learning in formal and informal ways, the implementation of efficient and effective assessment is still…

Smartphones Promote Autonomous Learning in ESL Classrooms

ERIC Educational Resources Information Center

Ramamuruthy, Viji; Rao, Srinivasa

2015-01-01

The rapid development of high-technology has caused new inventions of gadgets for all walks of life regardless age. In this rapidly advancing technology era many individuals possess hi-tech gadgets such as laptops, tablets, iPad, android phones and smart phones. Adult learners in higher learning institution especially are fond of using smart…

Quantitative Genetics in the Era of Molecular Genetics: Learning Abilities and Disabilities as an Example

ERIC Educational Resources Information Center

Haworth, Claire M. A.; Plomin, Robert

2010-01-01

Objective: To consider recent findings from quantitative genetic research in the context of molecular genetic research, especially genome-wide association studies. We focus on findings that go beyond merely estimating heritability. We use learning abilities and disabilities as examples. Method: Recent twin research in the area of learning…

Identification of Gifted Students with Learning Disabilities in a Response-to-Intervention Era

ERIC Educational Resources Information Center

Crepeau-Hobson, Franci; Bianco, Margarita

2011-01-01

The identification of children who are twice-exceptional--those who are gifted and have concomitant learning disabilities (LDs)--has historically posed a number of challenges for school psychologists and other school personnel. With the reauthorization of the Individuals With Disabilities Education Act and the shift to the use of a…

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

ERIC Educational Resources Information Center

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

2011-01-01

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

Choice and Compulsion: The End of an Era.

ERIC Educational Resources Information Center

McGhan, Barry

1998-01-01

Parents want to send their children to schools that are free not to teach everyone. Since schools are susceptible to societal disorders, pressures to provide school choices offering "safe havens" for learning will persist. Schools can do little to protect their learning environment from uncooperative students. A forced-exit process for unruly…

Passport to Mobility: Learning Differently, Learning Abroad.

ERIC Educational Resources Information Center

Commission of the European Communities, Brussels (Belgium). Directorate-General for Education and Culture.

Personal mobility, which is becoming increasingly necessary in the era of the Internet and the globalization of trade, is a crucial part of the European Union's (EU) goal of becoming a knowledge society. While millions of young people, students, teachers, and trainers have participated in educational, training, and linguistic exchanges in the past…

Making Sense of Student Performance Data: Data Use Logics and Mathematics Teachers' Learning Opportunities

ERIC Educational Resources Information Center

Horn, Ilana Seidel; Kane, Britnie Delinger; Wilson, Jonee

2015-01-01

In the accountability era, educators are pressed to use evidence-based practice. In this comparative case study, we examine the learning opportunities afforded by teachers' data use conversations. Using situated discourse analysis, we compare two middle school mathematics teacher workgroups interpreting data from the same district assessment.…

Learning Design Implementation for Distance e-Learning: Blending Rapid e-Learning Techniques with Activity-Based Pedagogies to Design and Implement a Socio-Constructivist Environment

ERIC Educational Resources Information Center

Santally, Mohammad Issack; Rajabalee, Yousra; Cooshna-Naik, Dorothy

2012-01-01

This paper discusses how modern technologies are changing the teacher-student-content relationships from the conception to the delivery of so-called "distance" education courses. The concept of Distance Education has greatly evolved in the digital era of 21st Century. With the widespread use and access to the Internet, exponential growth…

The post-genomic era of biological network alignment.

PubMed

Faisal, Fazle E; Meng, Lei; Crawford, Joseph; Milenković, Tijana

2015-12-01

Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches' biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.

Research of ad hoc network based on SINCGARS network

NASA Astrophysics Data System (ADS)

Nie, Hao; Cai, Xiaoxia; Chen, Hong; Chen, Jian; Weng, Pengfei

2016-03-01

In today's world, science and technology make a spurt of progress, so society has entered the era of information technology, network. Only the comprehensive use of electronic warfare and network warfare means can we maximize their access to information and maintain the information superiority. Combined with the specific combat mission and operational requirements, the research design and construction in accordance with the actual military which are Suitable for the future of information technology needs of the tactical Adhoc network, tactical internet, will greatly improve the operational efficiency of the command of the army. Through the study of the network of the U.S. military SINCGARS network, it can explore the routing protocol and mobile model, to provide a reference for the research of our army network.

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…

All-memristive neuromorphic computing with level-tuned neurons

NASA Astrophysics Data System (ADS)

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-01

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

All-memristive neuromorphic computing with level-tuned neurons.

PubMed

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-02

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

FLUXCOM - Overview and First Synthesis

NASA Astrophysics Data System (ADS)

Jung, M.; Ichii, K.; Tramontana, G.; Camps-Valls, G.; Schwalm, C. R.; Papale, D.; Reichstein, M.; Gans, F.; Weber, U.

2015-12-01

We present a community effort aiming at generating an ensemble of global gridded flux products by upscaling FLUXNET data using an array of different machine learning methods including regression/model tree ensembles, neural networks, and kernel machines. We produced products for gross primary production, terrestrial ecosystem respiration, net ecosystem exchange, latent heat, sensible heat, and net radiation for two experimental protocols: 1) at a high spatial and 8-daily temporal resolution (5 arc-minute) using only remote sensing based inputs for the MODIS era; 2) 30 year records of daily, 0.5 degree spatial resolution by incorporating meteorological driver data. Within each set-up, all machine learning methods were trained with the same input data for carbon and energy fluxes respectively. Sets of input driver variables were derived using an extensive formal variable selection exercise. The performance of the extrapolation capacities of the approaches is assessed with a fully internally consistent cross-validation. We perform cross-consistency checks of the gridded flux products with independent data streams from atmospheric inversions (NEE), sun-induced fluorescence (GPP), catchment water balances (LE, H), satellite products (Rn), and process-models. We analyze the uncertainties of the gridded flux products and for example provide a breakdown of the uncertainty of mean annual GPP originating from different machine learning methods, different climate input data sets, and different flux partitioning methods. The FLUXCOM archive will provide an unprecedented source of information for water, energy, and carbon cycle studies.

Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data

NASA Astrophysics Data System (ADS)

George, Daniel; Huerta, E. A.

2018-03-01

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.

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.

New Era Expansion Act

THOMAS, 111th Congress

Rep. Braley, Bruce L. [D-IA-1

2010-02-03

House - 02/23/2010 Referred to the Subcommittee on Higher Education, Lifelong Learning, and Competitiveness. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Disease dynamics in a dynamic social network

NASA Astrophysics Data System (ADS)

Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka

2010-07-01

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.

Feasibility Analysis of Developing Cross-border Network Education in China

NASA Astrophysics Data System (ADS)

Lan, Jun

In the era of economic globalization, strengthen of international cooperation on network education is a general trend. Although China has not made commitments about the market access and national treatment of cross-border supply in Schedule of Specific Commitments on Services, the basic conditions of network education development in China have been met. The Chinese government should formulate strategies for the development of cross-border network education and take relevant measures to implement them. In the near future, the carrying out of cross-border network education in China will become an irreversible trend, and will possess broad prospect with the advance of globalization of Chinese education.

Statistical physics of balance theory

PubMed Central

Belaza, Andres M.; Hoefman, Kevin; Bramson, Aaron; van den Heuvel, Milan; Schoors, Koen

2017-01-01

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads’ incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the “agents” involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data. PMID:28846726

Statistical physics of balance theory.

PubMed

Belaza, Andres M; Hoefman, Kevin; Ryckebusch, Jan; Bramson, Aaron; van den Heuvel, Milan; Schoors, Koen

2017-01-01

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads' incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the "agents" involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data.

Teaching and Learning in the Era of the Common Core: An Introduction to the Project and the Nine Research Papers in the "Students at the Center" Series

ERIC Educational Resources Information Center

Jobs for the Future, 2012

2012-01-01

Despite the wide interest in and need for student-centered approaches to learning, educators have scant access to a comprehensive accounting of the key components of it. To build the knowledge base for the emerging field of student-centered learning, Jobs for the Future, a national nonprofit based in Boston, commissioned papers from nine teams of…

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 Organizational Learning Obstacles in Hong Kong Secondary Schools from Teachers' Perspective

ERIC Educational Resources Information Center

Choi, Oi ling

2010-01-01

A series of educational reforms implemented in recent years had a considerable impact on secondary schools. From principals to teachers; from the school sector to the educational system, all parties had to effect fundamental changes. Facing this new educational era, many scholars suggested schools should have organizational learning so as to have…

Making Pedagogy Tangible: Developing Skills and Knowledge Using a Team Teaching and Blended Learning Approach

ERIC Educational Resources Information Center

Crawford, Renée; Jenkins, Louise E.

2018-01-01

In an era of accountability government and industry bodies are mandating that teacher education programs provide evidence of their impact. This paper provides an example of evidence-based practice, exploring how a team teaching and blended learning approach influenced the development of pre-service teachers (PSTs) competency skills and knowledge.…

Accountability as a Design for Teacher Learning: Sensemaking about Mathematics and Equity in the NCLB Era

ERIC Educational Resources Information Center

Horn, Ilana Seidel

2018-01-01

Using a learning design perspective on No Child Left Behind (NCLB), I examine how accountability policy shaped urban educators' instructional sensemaking. Focusing on the role of policy-rooted classifications, I examine conversations from a middle school mathematics teacher team as a "best case" because they worked diligently to comply…

One-Track Minds

ERIC Educational Resources Information Center

Sander, Wesley F.

2005-01-01

This article talks about how a teacher from Rail Road Flat Elementary School, Randall Youngblood, handles his class of 4th, 5th, and 6th graders through discipline. Discipline and the kind of teach-to-the-test learning that has become endemic in the era of No Child Left Behind has kept his students' energy channeled. Such rote learning often gets…

Study on Innovation of Teacher Training Model in Basic Education from the Perspective of "Blended Learning"

ERIC Educational Resources Information Center

Bu, Huabai; Bu, Shizhen

2012-01-01

Gradual integration of synergetic technology, P2P technology and online learning community furnishes a new research field for innovation of teacher training model in a knowledge economy era. This article proposes the innovative model of "whole of three lines" in teacher training in basic education from the perspective of "blended…

From Policy to Guidelines: Metamorphosis of Lifelong Learning in India

ERIC Educational Resources Information Center

Mandal, Sayantan

2013-01-01

In this era of globalisation, the present perception of lifelong learning (LLL) in the Indian policy domain has been going through major changes in an attempt to make it nationally realistic yet globally viable. In this process, all facets of the concept of LLL are constantly metamorphosing, and this in many ways outperforms the older perception…

Japanese English Education and Learning: A History of Adapting Foreign Cultures

ERIC Educational Resources Information Center

Shimizu, Minoru

2010-01-01

This essay is a history that relates the Japanese tradition of accepting and adapting aspects of foreign culture, especially as it applies to the learning of foreign languages. In particular, the essay describes the history of English education in Japan by investigating its developments after the Meiji era. The author addresses the issues from the…

Distance Education in an Era of eLearning: Challenges and Opportunities for a Campus-Focused Institution

ERIC Educational Resources Information Center

Forsyth, Hannah; Pizzica, Jenny; Laxton, Ruth; Mahony, Mary Jane

2010-01-01

The growth of eLearning technologies has blurred the boundaries of educational modes to a point where distance education programs can be offered without drawing particular notice on campus. The experience of distance education staff working in campus-focused universities and their perceptions of their chances of successfully planning and teaching…

Helping Preservice Teachers Learn to Teach for Understanding in This Era of High-Stakes Early Education Reform

ERIC Educational Resources Information Center

Brown, Christopher Pierce

2009-01-01

As early childhood education becomes more regulated through a range of education reforms and mandates, early childhood teacher educators are seeking ways to prepare their preservice teachers to address these policy constraints through appropriate teaching practices that foster learning with understanding. Using the National Research Council's…

The Learning Sciences in a New Era of U.S. Nationalism

ERIC Educational Resources Information Center

Cognition and Instruction, 2017

2017-01-01

What responsibilities do researchers of learning have in the wake of Trump's election and the proliferation of far-right, populist nationalism across the globe? In this essay, we seek to prompt and engage a dialogue about the political role and responsibilities of our field at this historical moment. First, we situate the social hierarchies that…

On Two Metaphors for Pedagogy and Creativity in the Digital Era: Liquid and Solid Learning

ERIC Educational Resources Information Center

Das, Simon

2012-01-01

As part of a belief in higher education (HE) aiding "creative capital", McWilliam and Dawson argue for a shift in pedagogic attention towards "Small C" creativity, which emphasises group endeavour over individual. Their radical 'liquid-learning" prescription, based on swarm intelligence, gives rise to the pedagogy of metagroups and modes of…

Learning to Make Sense: What Works in Entrepreneurial Education?

ERIC Educational Resources Information Center

Higgins, David; Elliott, Chris

2011-01-01

Purpose: The paper aims to explore the changing influences and relevance of passive and experiential methods of learning within what can be described as a new era of entrepreneurial education. What still largely remains unaddressed in the literature is how are entrepreneur's best educated and developed in a manner which can have a direct impact on…

Data Analysis Tools and Methods for Improving the Interaction Design in E-Learning

ERIC Educational Resources Information Center

Popescu, Paul Stefan

2015-01-01

In this digital era, learning from data gathered from different software systems may have a great impact on the quality of the interaction experience. There are two main directions that come to enhance this emerging research domain, Intelligent Data Analysis (IDA) and Human Computer Interaction (HCI). HCI specific research methodologies can be…

The Techy Teacher/Escaping the Lesson-Planning Doldrums

ERIC Educational Resources Information Center

Tucker, Catlin

2016-01-01

With a teacher's busy schedule, it is easy to get stuck in the lesson planning doldrums. However, as this is an era when teachers can easily learn from and share with educators all over the world, and are interacting increasingly with connected and tech-savvy students, their interest in all things digital can be leveraged for learning by teachers…

Practising the Public? Collaborative Teacher Inquiry in an Era of Standardization and Accountability

ERIC Educational Resources Information Center

Hardy, Ian

2018-01-01

This paper analyses the nature of collaborative teacher learning as a form of 'public sphere', under current policy conditions. The research draws upon Habermas' notions of communicative action and public spheres, and literature on the nature of teachers' learning in the context of standardized curriculum and assessment reform, to analyse how…

Trapped in a Local History: Why Did Extramural Fail to Engage in the Era of Engagement?

ERIC Educational Resources Information Center

Duke, Chris

2008-01-01

Extramural liberal adult education (LAE), as conceived in the particular UK tradition, was doomed by its high-minded origins and its privileged status, and contributed little to the new concepts of "éducation permanente," lifelong learning, the knowledge society, the learning society and region, or to the new understandings of university…

On the Nature of and Teachers' Goals for Students' Mathematical Argumentation in High School Classrooms

ERIC Educational Resources Information Center

Howell, Tracey H.

2013-01-01

In an era of new standards and emerging accountability systems, an understanding of the supports needed to aid teachers and students in making necessary transitions in mathematics teaching and learning is critical. Given the established research base demonstrating the importance of justification and reasoning in students' mathematics learning and…

Student's Plagiarisms in Higher Learning Institutions in the Era of Improved Internet Access: Case Study of Developing Countries

ERIC Educational Resources Information Center

Anney, Vicent Naano; Mosha, Mary Atanas

2015-01-01

This study investigated students' plagiarism practices in Tanzania higher learning institutions by involving two universities-one public and one private university as a case study. The universities involved have honour code and policies for plagiarism detection however they do not employ software for checking students' plagiarism. The study…

Computers in the Lives of Our Children: The Legacy of Television Research.

ERIC Educational Resources Information Center

Chen, Milton

This comparative examination considers a new era of research on children's learning. How children learn from microcomputers is studied, in light of research on children and television; and such issues are illuminated as how television and computers differ in their historical and economic contexts, how such differences affect their ability to serve…

Reorienting Self-Directed Learning for the Creative Digital Era

ERIC Educational Resources Information Center

Karakas, Fahri; Manisaligil, Alperen

2012-01-01

Purpose: The purpose of this paper is to identify the new role that human resource developers play in the globally connected workplace. Towards that end, this paper explores the changing landscape of self-directed learning (SDL) within the digital ecosystem based on the concept of World 2.0. Design/methodology/approach: This paper reviews and…

Mobile Learning Model and Process Optimization in the Era of Fragmentation

ERIC Educational Resources Information Center

Zhang, Shi-Jun; Yu, Gui-Hua

2017-01-01

In the context of mobile Internet, college students' leisure time has fragmentation characteristics to improve the value of time, it is of great practical significance to make full use of fragmentation time to study effectively. This research focuses on mobile learning model and its effect, firstly, qualitative research is used to construct the…

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,…

Primary care clinician expectations regarding aging.

PubMed

Davis, Melinda M; Bond, Lynne A; Howard, Alan; Sarkisian, Catherine A

2011-12-01

Expectations regarding aging (ERA) in community-dwelling older adults are associated with personal health behaviors and health resource usage. Clinicians' age expectations likely influence patients' expectations and care delivery patterns; yet, limited research has explored clinicians' age expectations. The Expectations Regarding Aging Survey (ERA-12) was used to assess (a) age expectations in a sample of primary care clinicians practicing in the United States and (b) clinician characteristics associated with ERA-12 scores.  This study was a cross-sectional survey of primary care clinicians affiliated with 5 practice-based research networks, October 2008 to June 2009. A total of 374 of the 1,510 distributed surveys were returned (24.8% response rate); 357 analyzed. Mean respondent age was 48.6 years (SD = 11.6; range 23-87 years); 88.0% physicians, 96.0% family medicine, 94.9% White, and 61.9% male. Female clinicians reported higher ERA-12 scores; clinicians' age expectations decreased with greater years in practice. Among the clinicians, higher ERA-12 scores were associated with higher clinician ratings of the importance of and personal skill in administering preventive counseling and the importance of delivering preventive services. Agreement with individual ERA-12 items varied widely. Unrealistically high or low ERA could negatively influence the quality of care provided to patients and patients' own age expectations. Research should examine the etiology of clinicians' age expectations and their association with older adult diagnoses and treatment. Medical education must incorporate strategies to promote clinician attitudes that facilitate successful patient aging.

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…

76 FR 57026 - Air Force Scientific Advisory Board Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-15

... and Technology plan emphasizing next generation energy, autonomy, sustainment, cyber, and ISR... secure cyber ops; acquisition challenges amid new era of defense policy and lessons learned from...

Accelerating Astronomy & Astrophysics in the New Era of Parallel Computing: GPUs, Phi and Cloud Computing

NASA Astrophysics Data System (ADS)

Ford, Eric B.; Dindar, Saleh; Peters, Jorg

2015-08-01

The realism of astrophysical simulations and statistical analyses of astronomical data are set by the available computational resources. Thus, astronomers and astrophysicists are constantly pushing the limits of computational capabilities. For decades, astronomers benefited from massive improvements in computational power that were driven primarily by increasing clock speeds and required relatively little attention to details of the computational hardware. For nearly a decade, increases in computational capabilities have come primarily from increasing the degree of parallelism, rather than increasing clock speeds. Further increases in computational capabilities will likely be led by many-core architectures such as Graphical Processing Units (GPUs) and Intel Xeon Phi. Successfully harnessing these new architectures, requires significantly more understanding of the hardware architecture, cache hierarchy, compiler capabilities and network network characteristics.I will provide an astronomer's overview of the opportunities and challenges provided by modern many-core architectures and elastic cloud computing. The primary goal is to help an astronomical audience understand what types of problems are likely to yield more than order of magnitude speed-ups and which problems are unlikely to parallelize sufficiently efficiently to be worth the development time and/or costs.I will draw on my experience leading a team in developing the Swarm-NG library for parallel integration of large ensembles of small n-body systems on GPUs, as well as several smaller software projects. I will share lessons learned from collaborating with computer scientists, including both technical and soft skills. Finally, I will discuss the challenges of training the next generation of astronomers to be proficient in this new era of high-performance computing, drawing on experience teaching a graduate class on High-Performance Scientific Computing for Astrophysics and organizing a 2014 advanced summer school on Bayesian Computing for Astronomical Data Analysis with support of the Penn State Center for Astrostatistics and Institute for CyberScience.

Consolidation of trauma programs in the era of large health care delivery networks.

PubMed

Trooskin, S Z; Faucher, M B; Santora, T A; Talucci, R C

1999-03-01

To review the development of an integrated trauma program at two separate campuses brought about by the merger of two medical-affiliated hospitals, each with an integrated program and a common trauma administrator, medical director, and educational coordinator. Each campus has an associate trauma medical director for on-site administrative management, a nurse coordinator, and a registrar. The integration resulted in a reduction of 1.5 full-time equivalents and "cost" savings by consolidated use of the helicopter, outreach, prevention, research, and educational programs. Regular "integration meetings," ad hoc committees, and video-linked conferences were used to institute common quality improvement programs, morbidity and mortality discussions, policies, and clinical management protocols. Reaccreditation by an outside agency, elimination of duplicated services, and maintenance of pre-merger clinical volume results. This integrated trauma program may serve as a model in this era of individual hospitals merging into large health care delivery networks.

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.

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.

A Holistic Approach to Networked Information Systems Design and Analysis

DTIC Science & Technology

2016-04-15

attain quite substantial savings. 11. Optimal algorithms for energy harvesting in wireless networks. We use a Markov- decision-process (MDP) based...approach to obtain optimal policies for transmissions . The key advantage of our approach is that it holistically considers information and energy in a...Coding technique to minimize delays and the number of transmissions in Wireless Systems. As we approach an era of ubiquitous computing with information

Blending Formal and Informal Learning Networks for Online Learning

ERIC Educational Resources Information Center

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Advanced Distribution Management System

NASA Astrophysics Data System (ADS)

Avazov, Artur R.; Sobinova, Liubov A.

2016-02-01

This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

In Search of Quality Student Teachers in a Digital Era: Reframing the Practices of Soft Skills in Teacher Education

ERIC Educational Resources Information Center

Hadiyanto; Mukmimnin, Amirul; Failasofah; Arif, Nely; Fajaryani, Nunung; Habibi, Akhmad

2017-01-01

The purpose of this current study was to examine and document the practices of soft skills (communication, IT, numeracy, learning how to learn, problem solving, working with others, and subject-specific competencies) among English as foreign language (EFL) student teachers at one public university teacher education program in Jambi, Indonesia. The…

ICT Integration in Education: Incorporation for Teaching & Learning Improvement

ERIC Educational Resources Information Center

Ghavifekr, Simin; Razak, Ahmad Zabidi Abd; Ghani, Muhammad Faizal A.; Ran, Ng Yan; Meixi, Yao; Tengyue, Zhang

2014-01-01

Over the last two decades, the rapid growth of ICT has become one of the most important topics discussed by the scholars in education. This is due to the capability of ICT in providing a dynamic and proactive teaching and learning environment. In line with the current digital era, teachers are required to integrate ICT in their daily teaching and…

Recognition of Prior Learning (RPL) and Skill Deficit: The Role of Open Distance Learning (ODL)

ERIC Educational Resources Information Center

Srivastava, Mamta; Jena, S. S.

2015-01-01

Skills acquisition is vital for any economic growth, particularly in an era of economic and technological changes. The need for skill development is a vital challenge, foremost for a developing nation, such as India. Therefore, vocational education and training (VET) is a direct means of providing workers with skills more relevant to their…

A Study on the Effects of Multiple Goal Orientation on Learning Motivation and Learning Behaviors

ERIC Educational Resources Information Center

Li, Jie-Yi; Shieh, Chich-Jen

2016-01-01

In such an era when the value is constantly restructured and information is rapidly changed, education reform should cater for new challenges. The role and function of teachers is encountering a new change. Coping with current information generation, people with high self-efficacy of selecting and mastering large amount of information and higher…

Implementing an Innovative Educational Program in an Era of Accountability: An Interview Study of the Expeditionary Learning Program

ERIC Educational Resources Information Center

DeLima, Laura E.

2017-01-01

This study examined the facilitators and barriers to the implementation of an innovative, whole-school reform model, Expeditionary Learning, within the context of the high-stakes accountability policy environment. Twenty-four teachers and four principals were interviewed across four schools, two of which were high poverty and two of which were low…

"In and Against" Lifelong Learning: Flexibility and the Corrosion of Character

ERIC Educational Resources Information Center

Crowther, Jim

2004-01-01

This paper argues against the dominant discourse of lifelong learning. It is primarily a mode of social control that acts as a new disciplinary technology to make people more compliant and adaptable for work in the era of flexible capitalism. Whilst the main reference point is trends in the UK, the argument has a wider resonance. Lifelong learning…

A Case Study of Instructor Scaffolding Using Web 2.0 Tools to Teach Social Informatics

ERIC Educational Resources Information Center

McLoughlin, Catherine E.; Alam, Sultana Lubna

2014-01-01

In the 21st century, also known as the digital era, higher education needs to face the changing technological contexts and to adopt pedagogies and tools for more engaging forms of learning. Despite much publicized enthusiasm about new media and its role in transforming learning in ways aligned with advances and contemporary socio-cultural…

Effects of Matching Multiple Memory Strategies with Computer-Assisted Instruction on Students' Statistics Learning Achievement

ERIC Educational Resources Information Center

Liao, Ying; Lin, Wen-He

2016-01-01

In the era when digitalization is pursued, numbers are the major medium of information performance and statistics is the primary instrument to interpret and analyze numerical information. For this reason, the cultivation of fundamental statistical literacy should be a key in the learning area of mathematics at the stage of compulsory education.…

OIDDE Learning Model: Improving Higher Order Thinking Skills of Biology Teacher Candidates

ERIC Educational Resources Information Center

Husamah; Fatmawati, Diani; Setyawan, Dwi

2018-01-01

As the massive advancement in 21st century, the role of education is to prepare generations in mastering the skills they need to face the challenges arised in their era. OIDDE is the abbreviation for Orientation, Identify, Discussion, Decision, and Engage in behaviour. The learning model designed by Hudha et al. (2016) is expected to be able to…

A Comparative Study on Various Vocabulary Knowledge Scales for Predicting Vocabulary Pre- Knowledge

ERIC Educational Resources Information Center

Zou, Di; Xie, Haoran; Rao, Yanghui; Wong, Tak-Lam; Wang, Fu Lee; Wu, Qingyuan

2017-01-01

The world has encountered and witnessed the great popularity of various emerging e-learning resources such as massive open online courses (MOOCs), textbooks and videos with the development of the big data era. It is critical to understand the characteristics of users to assist them to find desired and relevant learning resources in such a large…

Teaching Knowledge Management by Combining Wikis and Screen Capture Videos

ERIC Educational Resources Information Center

Makkonen, Pekka; Siakas, Kerstin; Vaidya, Shakespeare

2011-01-01

Purpose: This paper aims to report on the design and creation of a knowledge management course aimed at facilitating student creation and use of social interactive learning tools for enhanced learning. Design/methodology/approach: The era of social media and web 2.0 has enabled a bottom-up collaborative approach and new ways to publish work on the…

Getting down to Dollars and Cents: What Do School Districts Spend to Deliver Student-Centered Learning?

ERIC Educational Resources Information Center

Miller, Lawrence J.; Gross, Betheny; Ouijdani, Monica

2012-01-01

In the era of No Child Left Behind and Race to the Top, school districts are under increasing pressure from policymakers to hold all students to high performance standards. In response, a growing number of schools are embracing the principles of student-centered learning (SCL). SCL is a contemporary approach that combines progressive and…

Brain Disease and the Study of Learning Disabilities in the Netherlands (c. 1950-85)

ERIC Educational Resources Information Center

Bakker, Nelleke

2015-01-01

This paper discusses the role brain disease has played in the discourse and practices of child scientists involved in the study of learning disabilities and behavioural disorders from the 1950s up to the mid-1980s, particularly in the Netherlands as part of a developing international scientific community. In the pre-ADHD era, when child sciences…

Before Coffee, Facebook: New Literacy Learning for 21st Century Teachers

ERIC Educational Resources Information Center

Roach, Audra K.; Beck, Jessica J.

2012-01-01

And so a middle school language arts teacher begins her Saturday. Before coffee, Facebook. In this themed issue on professional development in an era of nick.com, the authors propose that teachers' new literacy learning is as close as their own screens. Teachers, too, live literate lives online in this new age of composition, with impulses to…

Planning for the Digital Classroom and Distributed Learning: Policies and Planning for Online Instructional Resources

ERIC Educational Resources Information Center

McGee, Patricia; Diaz, Veronica

2005-01-01

In an era of state budget cuts and a tight economy, distributed learning is often seen as a way to address the needs of colleges and universities looking for additional revenue sources. Likewise, budding virtual universities, consortia, and corporate partnerships are now providing new ways for institutions to share resources across campuses. The…

Personalised and Self Regulated Learning in the Web 2.0 Era: International Exemplars of Innovative Pedagogy Using Social Software

ERIC Educational Resources Information Center

McLoughlin, Catherine; Lee, Mark J. W.

2010-01-01

Research findings in recent years provide compelling evidence of the importance of encouraging student control over the learning process as a whole. The socially based tools and technologies of the Web 2.0 movement are capable of supporting informal conversation, reflexive dialogue and collaborative content generation, enabling access to a wide…

An Introduction to Quantum Communications Networks; Or, how shall we communicate in the quantum era?

NASA Astrophysics Data System (ADS)

Razavi, Mohsen

2018-05-01

This book fills a gap between experts and non-experts in the field by providing readers with the basic tools to understand the latest developments in quantum communications and its future directions. With the fast pace of developments in quantum technologies, it is more necessary than ever to make the new generation of students in science/engineering familiar with the key ideas behind such disruptive systems. This book describes key applications for quantum networks; local, metropolitan, and global networks; and the industrial outlook for the field.

Selection of patients for heart transplantation in the current era of heart failure therapy.

PubMed

Butler, Javed; Khadim, Ghazanfar; Paul, Kimberly M; Davis, Stacy F; Kronenberg, Marvin W; Chomsky, Don B; Pierson, Richard N; Wilson, John R

2004-03-03

We sought to assess the relationship between survival, peak exercise oxygen consumption (VO(2)), and heart failure survival score (HFSS) in the current era of heart failure (HF) therapy. Based on predicted survival, HF patients with peak VO(2) <14 ml/min/kg or medium- to high-risk HFSS are currently considered eligible for heart transplantation. However, these criteria were developed before the widespread use of beta-blockers, spironolactone, and defibrillators-interventions known to improve the survival of HF patients. Peak VO(2) and HFSS were assessed in 320 patients followed from 1994 to 1997 (past era) and in 187 patients followed from 1999 to 2001 (current era). Outcomes were compared between these two groups of patients and those who underwent heart transplantation from 1993 to 2000. Survival in the past era was 78% at one year and 67% at two years, as compared with 88% and 79%, respectively, in the current era (both p < 0.01). One-year event-free survival (without urgent transplantation or left ventricular assist device) was improved in the current era, regardless of initial peak VO(2): 64% vs. 48% for peak VO(2) <10 ml/min/kg (p = 0.09), 81% vs. 70% for 10 to 14 ml/min/kg (p = 0.05), and 93% vs. 82% for >14 ml/min/kg (p = 0.04). Of the patients with peak VO(2) of 10 to 14 ml/min/kg, 55% had low-risk HFSS and exhibited 88% one-year event-free survival. One-year survival after transplantation was 88%, which is similar to the 85% rate reported by the United Network for Organ Sharing for 1999 to 2000. Survival for HF patients in the current era has improved significantly, necessitating re-evaluation of the listing criteria for heart transplantation.

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.

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.

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.

Extending network approach to language dynamics and human cognition. Comment on "Approaching human language with complex networks" by Cong and Liu

NASA Astrophysics Data System (ADS)

Gong, Tao; Shuai, Lan; Wu, Yicheng

2014-12-01

By analyzing complex networks constructed from authentic language data, Cong and Liu [1] advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system [2]. Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.

The chemical disinfection of trout ponds

USGS Publications Warehouse

Fish, F.F.

1933-01-01

The need for knowledge concerning the prevention and control of fish diseases has never been greater than it is in this present era of economy when two fish must be raised in the same water which once supported but one. Fish pathologists have contributed a great deal to our knowledge of fish diseases, but there is still much to be learned, particularly concerning better methods of preventing and eliminating diseases among our trout. In this era of circular pools and raceways, our disease elimination is way back in the early days of standard troughs.

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.

Social Networks and Health.

PubMed

Perdiaris, Christos; Chardalias, Konstantinos; Magita, Andrianna; Mechili, Aggelos E; Diomidous, Marianna

2015-01-01

Nowadays the social networks have been developed into an advanced communications tool, which is important for all people to contact each other. These specific networks do offer lots of options as well as plenty of advantages and disadvantages. The social websites are many in number and titles, such as the facebook, the twitter, the bandoo etc. One of the most important function-mechanisms for the social network websites, are the marketing tools. The future goal is suggested to be the evolution of these programs. The development of these applications, which is going to lead into a new era for the social digital communication between the internet users, all around the globe.

Primary Care Clinician Expectations Regarding Aging

PubMed Central

Davis, Melinda M.; Bond, Lynne A.; Howard, Alan; Sarkisian, Catherine A.

2011-01-01

Purpose: Expectations regarding aging (ERA) in community-dwelling older adults are associated with personal health behaviors and health resource usage. Clinicians’ age expectations likely influence patients’ expectations and care delivery patterns; yet, limited research has explored clinicians’ age expectations. The Expectations Regarding Aging Survey (ERA-12) was used to assess (a) age expectations in a sample of primary care clinicians practicing in the United States and (b) clinician characteristics associated with ERA-12 scores. Design and Methods: This study was a cross-sectional survey of primary care clinicians affiliated with 5 practice-based research networks, October 2008 to June 2009. A total of 374 of the 1,510 distributed surveys were returned (24.8% response rate); 357 analyzed. Mean respondent age was 48.6 years (SD = 11.6; range 23–87 years); 88.0% physicians, 96.0% family medicine, 94.9% White, and 61.9% male. Results: Female clinicians reported higher ERA-12 scores; clinicians’ age expectations decreased with greater years in practice. Among the clinicians, higher ERA-12 scores were associated with higher clinician ratings of the importance of and personal skill in administering preventive counseling and the importance of delivering preventive services. Agreement with individual ERA-12 items varied widely. Implications: Unrealistically high or low ERA could negatively influence the quality of care provided to patients and patients’ own age expectations. Research should examine the etiology of clinicians’ age expectations and their association with older adult diagnoses and treatment. Medical education must incorporate strategies to promote clinician attitudes that facilitate successful patient aging. PMID:21430129

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

Once upon a Time … the Power of Story and Learning Journals.

PubMed

Lewis, Melinda

2004-05-01

This paper will invite you to consider the role of stories for learning and the use of learning journals as a tool to create meaning. The application of story and story culture in higher education, academia and management contexts will be presented. As an example, an old Punjabi tale will be adapted for use when managing and inspiring teams in the workplace. Storytelling is experiencing a revival and being used in the corporate sector to ignite action in knowledge-era organisations.

Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

NASA Astrophysics Data System (ADS)

Romero, David; Molina, Arturo

Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

On-the-Job Evidence-Based Medicine Training for Clinician-Scientists of the Next Generation

PubMed Central

Leung, Elaine YL; Malick, Sadia M; Khan, Khalid S

2013-01-01

Clinical scientists are at the unique interface between laboratory science and frontline clinical practice for supporting clinical partnerships for evidence-based practice. In an era of molecular diagnostics and personalised medicine, evidence-based laboratory practice (EBLP) is also crucial in aiding clinical scientists to keep up-to-date with this expanding knowledge base. However, there are recognised barriers to the implementation of EBLP and its training. The aim of this review is to provide a practical summary of potential strategies for training clinician-scientists of the next generation. Current evidence suggests that clinically integrated evidence-based medicine (EBM) training is effective. Tailored e-learning EBM packages and evidence-based journal clubs have been shown to improve knowledge and skills of EBM. Moreover, e-learning is no longer restricted to computer-assisted learning packages. For example, social media platforms such as Twitter have been used to complement existing journal clubs and provide additional post-publication appraisal information for journals. In addition, the delivery of an EBLP curriculum has influence on its success. Although e-learning of EBM skills is effective, having EBM trained teachers available locally promotes the implementation of EBM training. Training courses, such as Training the Trainers, are now available to help trainers identify and make use of EBM training opportunities in clinical practice. On the other hand, peer-assisted learning and trainee-led support networks can strengthen self-directed learning of EBM and research participation among clinical scientists in training. Finally, we emphasise the need to evaluate any EBLP training programme using validated assessment tools to help identify the most crucial ingredients of effective EBLP training. In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation. In addition, future studies evaluating the effectiveness of EBM training should use validated outcome tools, endeavour to achieve adequate power and consider the effects of EBM training on learning environment and patient outcomes. PMID:24151345

On-the-Job Evidence-Based Medicine Training for Clinician-Scientists of the Next Generation.

PubMed

Leung, Elaine Yl; Malick, Sadia M; Khan, Khalid S

2013-08-01

Clinical scientists are at the unique interface between laboratory science and frontline clinical practice for supporting clinical partnerships for evidence-based practice. In an era of molecular diagnostics and personalised medicine, evidence-based laboratory practice (EBLP) is also crucial in aiding clinical scientists to keep up-to-date with this expanding knowledge base. However, there are recognised barriers to the implementation of EBLP and its training. The aim of this review is to provide a practical summary of potential strategies for training clinician-scientists of the next generation. Current evidence suggests that clinically integrated evidence-based medicine (EBM) training is effective. Tailored e-learning EBM packages and evidence-based journal clubs have been shown to improve knowledge and skills of EBM. Moreover, e-learning is no longer restricted to computer-assisted learning packages. For example, social media platforms such as Twitter have been used to complement existing journal clubs and provide additional post-publication appraisal information for journals. In addition, the delivery of an EBLP curriculum has influence on its success. Although e-learning of EBM skills is effective, having EBM trained teachers available locally promotes the implementation of EBM training. Training courses, such as Training the Trainers, are now available to help trainers identify and make use of EBM training opportunities in clinical practice. On the other hand, peer-assisted learning and trainee-led support networks can strengthen self-directed learning of EBM and research participation among clinical scientists in training. Finally, we emphasise the need to evaluate any EBLP training programme using validated assessment tools to help identify the most crucial ingredients of effective EBLP training. In summary, we recommend on-the-job training of EBM with additional focus on overcoming barriers to its implementation. In addition, future studies evaluating the effectiveness of EBM training should use validated outcome tools, endeavour to achieve adequate power and consider the effects of EBM training on learning environment and patient outcomes.

Competences in Social Media Use in the Area of Health and Healthcare.

PubMed

Kouri, Pirkko; Rissanen, Marja-Liisa; Weber, Patrick; Park, Hyeoun-Ae

2017-01-01

In today's life, social media offer new working ways. People are increasingly expanding interactions from face-to-face meetings to online ways of communication, networking, searching, creating and sharing information, and furthermore taking care of patients/citizens via tweeting care, Facebook care, blogging care, vlogging care, infotainment care, gamification-care, infographic care, for instance. This chapter discusses the utilisation of social media in the healthcare domain including nursing education, practice and research. When in the current healthcare era, social media is used effectively and purposefully, it can give all of us a greater choice in how we live, how we take care of our health and how we learn and build both our professional competences and produce evidence-based, qualified data. Nurses need continuous education and proper tools to take the most of the benefits of social media, not forgetting privacy and ethical issues. This use of social media in professional nursing generates the need for new competences.

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…

The Teaching and Learning of Foreign Languages in Spain from the Sixteenth to Eighteenth Centuries: Influences and Inter-Relationships

ERIC Educational Resources Information Center

López, Javier Suso

2018-01-01

This study of the learning of foreign languages in Spain between the sixteenth and eighteenth centuries sets out to demonstrate the importance of situating research on any given era in a pan-European plurilingual context. A brief historiographical overview is followed by a discussion of language teaching materials and methods from a sociocultural…

Competitive Debate as Competency-Based Learning: Civic Engagement and Next-Generation Assessment in the Era of the Common Core Learning Standards

ERIC Educational Resources Information Center

McIntosh, Jonathan; Milam, Myra

2016-01-01

As the adoption and execution of the Common Core State Standards (CCSS) have steadily increased, the debate community is presented with an opportunity to be more forward thinking and sustainable through the translation to curriculum planning and next-generation assessment as a movement towards Performance-Based Assessments. This paper focuses on…

Leadership Implications for the Implementation of Culturally Responsive Teaching and Learning Programs

ERIC Educational Resources Information Center

Knickman, Kevin; Schulte, Lindsay; Schwemmer, Gabrielle; Young, Henrietta

2011-01-01

This document is a Problem Based Learning project addressing the No Child Left Behind Act of 2001 and the achievement gap issue that lingers in public education. It centers on the ideas of cultural sensitivity as a means of educating all students in an era of accountability. The goal of the project was to address the problem of minority student…

College Students, the GI Bill, and the Proliferation of Online Learning: A History of Learning and Contemporary Challenges

ERIC Educational Resources Information Center

McMurray, Andrew J.

2007-01-01

Recent developments in areas of online education and the modernization of the GI Bill of Rights in the form of the Montgomery GI Bill have served to enact an unparalleled era in the history of higher education. Now, more than ever, servicemen and servicewomen have both the financial resources and the technological resources to pursue higher…

Service Learning and the Core Curriculum: Two Models for Doing Theology as Service Learning in the Core

ERIC Educational Resources Information Center

McMahon, Christopher

2014-01-01

In principle, theology ought to play a decisive role in the mission and identity of Catholic colleges and universities, but theology's role often comes under fire from students and other constituencies who consider theology an uncritical intrusion into the curriculum or a holdover from a bygone era. This essay reflects on the role of theology…

Learning Disabilities and Employment before and in the Americans with Disabilities Act Era: Progress or a Bridge Too Far?

ERIC Educational Resources Information Center

Gerber, Paul J.; Batalo, Cecilia G.; Achola, Edwin O.

2011-01-01

The Americans with Disabilities Act of 1990 (ADA) and its amendments have been in existence for a little more than twenty years. Title One, which pertains to employment, has had a bearing on employment for persons with disabilities, particularly the high incidence category of learning disabilities, who for the most part work in competitive…

School Finance in the Digital-Learning Era. Creating Sound Policy for Digital Learning. A Working Paper Series from the Thomas B. Fordham Institute

ERIC Educational Resources Information Center

Hill, Paul T.

2011-01-01

America's system for financing K-12 education is not neutral about innovation and the use of new technologies. Indeed, that system is stacked against them. To remedy this, our education-funding system needs to shift dramatically. Instead of today's model--which rigidly funds programs, staff positions, and administrative structures, instead of…

Education Alignment and Accountability in an Era of Convergence: Policy Insights from States with Individual Learning Plans and Policies

ERIC Educational Resources Information Center

Phelps, L. Allen; Durham, Julie; Wills, Joan

2011-01-01

In response to the rising demand for market-responsive education reform across the U.S., since 1998 more than twenty states have created Individual Learning or Graduation Plan (ILP/IGP) state policies. Using extensive policy document analyses and stakeholder interview data from four early-adopting ILP/IGP states, the goal of this four-state case…

Cesar Chavez--Grade Eleven Model Curriculum and Resources.

ERIC Educational Resources Information Center

California State Dept. of Education, Sacramento.

In grade 11, students use the life and work of Cesar E. Chavez as a case study to trace the major historical eras and events of the 20th century. Students start by studying the Chavez family at the beginning of the 20th century, learning about them as they struggled to acquire a farm in Arizona and raise a family. They also learn how they faced…

Indigenous Tertiary Education--We Are All Learning: Both-Ways Pedagogy in the Northern Territory of Australia

ERIC Educational Resources Information Center

Bat, Melodie; Kilgariff, Claire; Doe, Tina

2014-01-01

In this new era in tertiary education in Australia, the opportunity exists not only to meet the needs of Aboriginal and Torres Strait Islander students and thus redress low access and participation rates, but also to build a system that privileges Aboriginal and Torres Strait Islander knowledges and ways of learning. To be able to do such a thing…

Creating Collaborative and Convenient Learning Environment Using Cloud-Based Moodle LMS: An Instructor and Administrator Perspective

ERIC Educational Resources Information Center

Kumar, Vikas; Sharma, Deepika

2016-01-01

Students in the digital era are habitual of using digital devices not only for playing and interacting with their friends and peers, but also as a tool for education and learning. These digital natives are highly obsessed with the internet driven portable devices and always demand for a multimedia rich content. This specific demand needs to be…

Improving Science Pedagogic Quality in Elementary School Using Process Skill Approach Can Motivate Student to Be Active in Learning

ERIC Educational Resources Information Center

Sukiniarti

2016-01-01

On global era todays, as the professional teacher should be improving their pedagogic competency, including to improve their science pedagogy quality. This study is aimed to identify: (1) Process skill approach which has been used by Elementary School Teacher in science learning; (2) Teacher's opinion that process skill can motivate the student to…

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. PMID:26928718

Deciphering Biochemical Network: from particles to planes then to spaces

NASA Astrophysics Data System (ADS)

Ye, Xinhao; Zhang, Siliang; Engineer Research CenterBiotechnology, National

2004-03-01

Today when we are still infatuated with the booming systematic fashion in life science, we, especially as biologist, ironically have fallen down into a sub-systematic maze. That is, although rapid advances in "omics" sciences ceaselessly provided so-called global or large-scale maps to exhibit the corresponding subnet, seldom paid attention to connecting these distinct but close-knit functional modules. Fortunately, a group of physicists recently cast off this natural moat and integrated multi-scale biological network into a simple life's pyramid. However, if extended this pyramid to a 3D structure in view of XYZ axis constructed by the temporal, spatial and organized characteristics respectively, it should be noted that this from-universal-to-particular pyramid is only a transverse section while the achievements in diverse "omics" sciences consist of relative longitudinal ones. On that footing, if analogizing the development of systems biology in last decades as a huge leap from discrete particles (typically in "a paper = a gene" era) to several planes (that is relative to corresponding OMICS science), we might rationally predict a next "space" era is coming soon to untangle and map the multi-tiered biological network really in a whole.

E3 Success Story - Sparking Powerful Change Through the Green Suppliers Network: Byrne Electrical Specialists, Inc.

EPA Pesticide Factsheets

E3 – Economy, Energy, and Environment – is a coordinated federal and local technical assistance initiative that is helping manufacturers across the nation adapt and thrive in a new business era focused on sustainability.

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…

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.

Resting state brain networks and their implications in neurodegenerative disease

NASA Astrophysics Data System (ADS)

Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

2012-10-01

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

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.

Virtual Libraries: Service Realities.

ERIC Educational Resources Information Center

Novak, Jan

This paper discusses client service issues to be considered when transitioning to a virtual library situation. Themes related to the transitional nature of society in the knowledge era are presented, including: paradox and a contradictory nature; blurring of boundaries; networks, systems, and holistic thinking; process/not product, becoming/not…

Feminine Desire in the Age of Satellite Television.

ERIC Educational Resources Information Center

Curtin, Michael

1999-01-01

Contributes to scholarship on global media conglomerates, cultural expression, and feminism. Delineates the corporate logic of culture industries in the neo-network era. Shows, using the television show "Absolutely Fabulous," how media firms benefit from transnational circulation of multiple and alternative representations of feminine…

Independent Prognostic Value of Serum Markers in Diffuse Large B-Cell Lymphoma in the Era of the NCCN-IPI.

PubMed

Melchardt, Thomas; Troppan, Katharina; Weiss, Lukas; Hufnagl, Clemens; Neureiter, Daniel; Tränkenschuh, Wolfgang; Schlick, Konstantin; Huemer, Florian; Deutsch, Alexander; Neumeister, Peter; Greil, Richard; Pichler, Martin; Egle, Alexander

2015-12-01

Several serum parameters have been evaluated for adding prognostic value to clinical scoring systems in diffuse large B-cell lymphoma (DLBCL), but none of the reports used multivariate testing of more than one parameter at a time. The goal of this study was to validate widely available serum parameters for their independent prognostic impact in the era of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) score to determine which were the most useful. This retrospective bicenter analysis includes 515 unselected patients with DLBCL who were treated with rituximab and anthracycline-based chemoimmunotherapy between 2004 and January 2014. Anemia, high C-reactive protein, and high bilirubin levels had an independent prognostic value for survival in multivariate analyses in addition to the NCCN-IPI, whereas neutrophil-to-lymphocyte ratio, high gamma-glutamyl transferase levels, and platelets-to-lymphocyte ratio did not. In our cohort, we describe the most promising markers to improve the NCCN-IPI. Anemia and high C-reactive protein levels retain their power in multivariate testing even in the era of the NCCN-IPI. The negative role of high bilirubin levels may be associated as a marker of liver function. Further studies are warranted to incorporate these markers into prognostic models and define their role opposite novel molecular markers. Copyright © 2015 by the National Comprehensive Cancer Network.

Embedded resource accounting for coupled natural-human systems: An application to water resource impacts of the western U.S. electrical energy trade

NASA Astrophysics Data System (ADS)

Ruddell, Benjamin L.; Adams, Elizabeth A.; Rushforth, Richard; Tidwell, Vincent C.

2014-10-01

In complex coupled natural-human systems (CNH), multitype networks link social, environmental, and economic systems with flows of matter, energy, information, and value. Embedded Resource Accounting (ERA) is a systems analysis framework that includes the indirect connections of a multitype CNH network. ERA is conditioned on perceived system boundaries, which may vary according to the accountant's point of view. Both direct and indirect impacts are implicit whenever two subnetworks interact in such a system; the ratio of two subnetworks' impacts is the embedded intensity. For trade in the services of water, this is understood as the indirect component of a water footprint, and as "virtual water" trade. ERA is a generalization of input-output, footprint, and substance flow methods, and is a type of life cycle analysis. This paper presents results for the water and electrical energy system in the western U.S. This system is dominated by California, which outsources the majority of its water footprint of electrical energy. Electricity trade increases total water consumption for electricity production in the western U.S. by 15% and shifts water use to water-stressed Colorado River Basin States. A systemic underaccounting for water footprints occurs because state-level processes discount a portion of the water footprint occurring outside of the state boundary.

Application of high performance asynchronous socket communication in power distribution automation

NASA Astrophysics Data System (ADS)

Wang, Ziyu

2017-05-01

With the development of information technology and Internet technology, and the growing demand for electricity, the stability and the reliable operation of power system have been the goal of power grid workers. With the advent of the era of big data, the power data will gradually become an important breakthrough to guarantee the safe and reliable operation of the power grid. So, in the electric power industry, how to efficiently and robustly receive the data transmitted by the data acquisition device, make the power distribution automation system be able to execute scientific decision quickly, which is the pursuit direction in power grid. In this paper, some existing problems in the power system communication are analysed, and with the help of the network technology, a set of solutions called Asynchronous Socket Technology to the problem in network communication which meets the high concurrency and the high throughput is proposed. Besides, the paper also looks forward to the development direction of power distribution automation in the era of big data and artificial intelligence.

[The future of the European nephrology belongs to the young: the Young Nephrologists' Platform].

PubMed

Bolignano, Davide

2014-01-01

Young people are the future of research, especially in nephrology. The prevalence of young nephrologists within the main scientific European societies varies from the 12% to 34% and the 20% of the ERA-EDTA members are less than 40 years old in 2013. Recently, the ERA-EDTA has launched a new platform, the Young Nephrologists Platform (YNP), which the aim is to become the first modern network of young nephrologists from Europe and beyond. YNP aims at promoting the aggregation of young people through modern communication channels such as social networks, blogs and through the construction of a database collecting information on attitudes and personal experiences of each young nephrologist. A mentorship program, focused and young-oriented clinical courses on hot topics and the direct involvement of young nephrologists in working groups and scientific studies are some of the other interesting initiatives driven by YNP. The future of nephrology belongs to the young and YNP could represent a good springboard for the professional growth of young nephrologists.

Preparing a New Generation of Clinicians for the Era of Big Data

PubMed Central

Moskowitz, Ari; McSparron, Jakob; Stone, David J.; Celi, Leo Anthony

2015-01-01

Synopsis As medicine becomes increasingly complex and financially constrained, it will be the responsibility of every clinician to understand and participate in the enterprise of extracting lessons learned from digitally captured patient care. PMID:25688383

‘E-learning’ modalities in the current era of Medical Education in Pakistan

PubMed Central

Jawaid, Masood; Aly, Syed Moyn

2014-01-01

There are a number of e-Learning modalities, some or all of which may be used throughout a medical, dental, nursing or any other health related undergraduate curriculum. The purpose of this paper is to briefly describe what e-learning is along with some of the modalities, their common advantages and limitations. This publication ends with practical implications of these modalities for Pakistan. PMID:25225547

Developing Conceptual Framework for Revising Self-Learning Materials (SLMs) of the Open School (OS) of Bangladesh Open University (BOU) at a Digital Environment

ERIC Educational Resources Information Center

Yeasmin, Sabina; Murthy, C. R. K.

2011-01-01

Bangladesh Open University (BOU) runs school programs as part of its academic activities through open schooling since its inception. As of today, the Open School uses the first generation self-learning materials (SLMs) written, before an era, following an in-house style and template. The concerned faculty member corrects, every year, texts before…

Developing Conceptual Framework for Revising Self-Learning Materials (SLMs) of the Open School (OS) of Bangladesh Open University (BOU) at a Digital Environment

ERIC Educational Resources Information Center

Yeasmin, Sabina; Murthy, C. R. K.

2012-01-01

Bangladesh Open University (BOU) runs school programs as part of its academic activities through open schooling since its inception. As of today, the Open School uses the first generation self-learning materials (SLMs) written, before an era, following an in-house style and template. The concerned faculty member corrects, every year, texts before…

Vascular surgery trainees still need to learn how to sew: importance of learning surgical techniques in the era of endovascular surgery.

PubMed

Aziz, Faisal

2015-01-01

Vascular surgery represents one of the most rapidly evolving specialties in the field of surgery. It was merely 100 years ago when Dr. Alexis Carrel described vascular anastomosis. Over the course of next several decades, vascular surgeons distinguished themselves from general surgeons by horning the techniques of vascular surgery operations. In the era of minimally invasive interventions, the number of endovascular interventions performed by vascular surgeons has increased exponentially. Vascular surgery trainees in the current times spend considerable time in mastering the techniques of endovascular operations. Unfortunately, the reduction in number of open surgical operations has lead to concerns in regards to adequacy of learning open surgical techniques. In future, majority of vascular interventions will be done with minimally invasive techniques. Combination of poor training in open operations and increasing complexity of open surgical operations may lead to poor surgical outcomes. It is the need of the hour for vascular surgery trainees to realize the importance of learning and mastering open surgical techniques. One of the most distinguishing features of contemporary vascular surgeons is their ability to perform both endovascular and open vascular surgery operations, and we should strive to maintain our excellence in both of these arenas.

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.

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.

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…

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

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.

The National Network of Fusion Centers: Perception and Reality

DTIC Science & Technology

2014-12-01

growing exponentially to the post-recession era of austerity. As this pendulum moved from one side to the other, perceptions and attitudes about the...decline. The article provides insight into the advantages and drawbacks of the development of a national marketing strategy and highlights factors

Automated conflict resolution issues

NASA Technical Reports Server (NTRS)

Wike, Jeffrey S.

1991-01-01

A discussion is presented of how conflicts for Space Network resources should be resolved in the ATDRSS era. The following topics are presented: a description of how resource conflicts are currently resolved; a description of issues associated with automated conflict resolution; present conflict resolution strategies; and topics for further discussion.

Accountability Practices in Adult Education: Insights from Actor-Network Theory

ERIC Educational Resources Information Center

Fenwick, Tara

2010-01-01

Accountability mechanisms in adult education, their constitution and their effects, are of increasing concern in an era threatening massive reductions to resources for adult education activity. Such mechanisms are frequently portrayed as unassailably oppressive. However, alternative analyses have illuminated contradictions and ambiguities in the…

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

NASA Astrophysics Data System (ADS)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

Extending Asia Pacific bioinformatics into new realms in the "-omics" era.

PubMed

Ranganathan, Shoba; Eisenhaber, Frank; Tong, Joo Chuan; Tan, Tin Wee

2009-12-03

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation dating back to 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 7-11, 2009 at Biopolis, Singapore. Besides bringing together scientists from the field of bioinformatics in this region, InCoB has actively engaged clinicians and researchers from the area of systems biology, to facilitate greater synergy between these two groups. InCoB2009 followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India), Hong Kong and Taipei (Taiwan), with InCoB2010 scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010. The Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and symposia on Clinical Bioinformatics (CBAS), the Singapore Symposium on Computational Biology (SYMBIO) and training tutorials were scheduled prior to the scientific meeting, and provided ample opportunity for in-depth learning and special interest meetings for educators, clinicians and students. We provide a brief overview of the peer-reviewed bioinformatics manuscripts accepted for publication in this supplement, grouped into thematic areas. In order to facilitate scientific reproducibility and accountability, we have, for the first time, introduced minimum information criteria for our pubilcations, including compliance to a Minimum Information about a Bioinformatics Investigation (MIABi). As the regional research expertise in bioinformatics matures, we have delineated a minimum set of bioinformatics skills required for addressing the computational challenges of the "-omics" era.

Patient-Centered Communication: Exploring the Dentist's Role in the Era of e-Patients and Health 2.0.

PubMed

Seymour, Brittany; Yang, Helen; Getman, Rebekah; Barrow, Jane; Kalenderian, Elsbeth

2016-06-01

In today's digital era, people are increasingly relying on the Internet-including social media-to access health information and inform their health decisions. This article describes an exploratory initiative to better understand and define the role of dentists in patient education in the context of e-patients and Health 2.0. This initiative consisted of four phases. In Phase I, an interdisciplinary expert advisory committee was assembled for a roundtable discussion about patients' health information-seeking behaviors online. In Phase II, a pilot case study was conducted, with methods and analysis informed by Phase I recommendations. Phase III consisted of a debriefing conference to outline future areas of research on modernizing health communication strategies. In Phase IV, the findings and working theories were presented to 75 dental students, who then took a survey regarding their perspectives with the objective of guiding potential curriculum design for predoctoral courses. The results of the survey showed that the validity of online content was often secondary to the strength of the network sharing it and that advocacy online could be more effective if it allowed for emotional connections with peers rather than preserving accuracy of the information. Students expressed high interest in learning how to harness modern health communications in their clinical care since the role of the dentist is evolving from giving information to giving personalized guidance against the backdrop of an often contradictory modern information environment. The authors recommend that the dental profession develop patient-centered health communication training for predoctoral students and professional development and continuing education for practicing professionals.

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.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

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…

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…

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Note-taking and Handouts in The Digital Age.

PubMed

Stacy, Elizabeth Moore; Cain, Jeff

2015-09-25

Most educators consider note-taking a critical component of formal classroom learning. Advancements in technology such as tablet computers, mobile applications, and recorded lectures are altering classroom dynamics and affecting the way students compose and review class notes. These tools may improve a student's ability to take notes, but they also may hinder learning. In an era of dynamic technology developments, it is important for educators to routinely examine and evaluate influences on formal and informal learning environments. This paper discusses key background literature on student note-taking, identifies recent trends and potential implications of mobile technologies on classroom note-taking and student learning, and discusses future directions for note-taking in the context of digitally enabled lifelong learning.

A History and Overview of the Behavioral Neuroscience of Learning and Memory.

PubMed

Clark, Robert E

2018-01-01

Here, I provide a basic history of important milestones in the development of theories for how the brain accomplishes the phenomenon of learning and memory. Included are the ideas of Plato, René Descartes, Théodule Ribot, William James, Ivan Pavlov, John Watson, Karl Lashley, and others. The modern era of learning and memory research begins with the description of H.M. by Brenda Milner and the gradual discovery that the brain contains multiple learning and memory systems that are supported by anatomically discrete brain structures. Finally, a brief overview is provided for the chapters that are included in current topics in Behavioral Neuroscience-Learning and Memory.

A History and Overview of the Behavioral Neuroscience of Learning and Memory.

PubMed

Clark, Robert E

2018-01-05

Here, I provide a basic history of important milestones in the development of theories for how the brain accomplishes the phenomenon of learning and memory. Included are the ideas of Plato, René Descartes, Théodule Ribot, William James, Ivan Pavlov, John Watson, Karl Lashley, and others. The modern era of learning and memory research begins with the description of H.M. by Brenda Milner and the gradual discovery that the brain contains multiple learning and memory systems that are supported by anatomically discrete brain structures. Finally, a brief overview is provided for the chapters that are included in current topics in Behavioral Neuroscience-Learning and Memory.

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?;…

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.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

PubMed Central

Vadillo, Miguel A; Curcin, Vasa

2017-01-01

Background Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. PMID:28663166

9th International Symposium on the Biosafety of Genetically Modified Organisms. Session II: Identifying and defining hazards and potential consequences I: Concepts for problem formulation and non-target risk assessment.

PubMed

Bigler, Franz

2006-01-01

The scientific organizers of the symposium put much emphasis on the identification and definition of hazard and the potential consequences thereof and three full sessions with a total of 13 presentations encompassing a wide range of related themes were planned for this topic. Unfortunately, one talk had to be cancelled because of illness of the speaker (BM Khadi, India). Some presentations covered conceptual approaches for environmental risk assessment (ERA) of GM plants (problem formulation in the risk assessment framework, familiarity approach, tiered and methodological frameworks, non-target risk assessment) and the use of models in assessing invasiveness and weediness of GM plants. Other presentations highlighted the lessons learned for future ERA from case studies and commercialized GM crops, and from monitoring of unintended releases to the environment. When the moderators of the three sessions came together after the presentations to align their summaries, there was an obvious need to restructure the 12 presentations in a way that allowed for a consistent summarizing discussion. The following new organization of the 12 talks was chosen: (1) Concepts for problem formulation and non-target risk assessment, (2) Modeling as a tool for predicting invasiveness of GM plants, (3) Case-studies of ERA of large-scale release, (4) Lessons learned for ERA from a commercialized GM plant, (5) Monitoring of unintended release of Bt maize in Mexico. The new thematic structure facilitates a more in-depth discussion of the presentations related to a specific topic, and the conclusions to be drawn are thus more consistent. Each moderator agreed to take responsibility for summarizing one or more themes and to prepare the respective report.

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.

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

The Organization of Higher Education: Managing Colleges for a New Era

ERIC Educational Resources Information Center

Bastedo, Michael N., Ed.

2012-01-01

Colleges and universities are best understood as networks of departments working together to fulfill a mission of education, innovation, and community partnership. To better understand how these large and complex institutions function, scholars can apply organizational and strategic planning concepts made familiar by business management. This book…

Emerging Technologies Integrating Technology into Study Abroad

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2016-01-01

"Ready access to travel and to technology-enhanced social networking (e.g., Facebook or Skype) has changed the nature of study abroad to the point where today's experiences are fundamentally different from those of earlier eras" (Kinginger, 2013a, p. 345). In addition to more travel options and greater technology availability, study…

The Globalization of Higher Education through the Lens of Technology and Accountability

ERIC Educational Resources Information Center

Woodard, Howard C.; Shepherd, Sonya S.; Crain-Dorough, Mindy; Richardson, Michael D.

2011-01-01

Technology has ushered in a new era in higher education making knowledge of technology essential for administrators. Technology is transforming higher education by providing a global interconnectedness that reshapes educational, social, economic and cultural life. The globalization of networks based on travel, mobile phones, broad-band Internet…

Electronic Advocacy and Social Welfare Policy Education

ERIC Educational Resources Information Center

Moon, Sung Seek; DeWeaver, Kevin L.

2005-01-01

The rapid increase in the number of low-cost computers, the proliferation of user-friendly software, and the development of electronic networks have created the "informatics era." The Internet is a rapidly growing communication resource that is becoming mainstream in the American society. Computer-based electronic political advocacy by social…

Francis Wayland Parker's Morning Exercise and the Progressive Movement

ERIC Educational Resources Information Center

Schmitt, Natalie Crohn

2010-01-01

In the progressive era, the distinguished political scientist Robert Putnam explains, progressives invested heavily in "social capital," that is, in the stock of active connections, social networks, shared values, norms of reciprocity, trustworthiness, and friendship that bind people together (Putnam 2000, 395). They were, he argues,…

Automating the conflict resolution process

NASA Technical Reports Server (NTRS)

Wike, Jeffrey S.

1991-01-01

The purpose is to initiate a discussion of how the conflict resolution process at the Network Control Center can be made more efficient. Described here are how resource conflicts are currently resolved as well as the impacts of automating conflict resolution in the ATDRSS era. A variety of conflict resolution strategies are presented.

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,…

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

Discovery of Deep Structure from Unlabeled Data

DTIC Science & Technology

2014-11-01

GPU processors . To evaluate the unsupervised learning component of the algorithms (which has become of less importance in the era of “big data...representations to those in biological visual, auditory, and somatosensory cortex ; and ran numerous control experiments investigating the impact of

Where Are You Going in the Next Millennium?

ERIC Educational Resources Information Center

Hay, LeRoy E.

1999-01-01

Public education should no longer reflect agricultural or industrial era learning modes. Third-millennium administrators must recognize certain societal trends: the "net generation" of students, predominance of technology, electronic schools, the information deluge and the democratization of information, the age of convenience and…

College Preparatory Mathematics: Change from Within.

ERIC Educational Resources Information Center

Kysh, Judith M.

1995-01-01

The College Preparatory Mathematics: Change from Within Project (CPM) was created to develop a rich, integrated mathematics curriculum, based on the best current wisdom of how people learn and the mathematics needed in an era of computers, and involving teachers in materials development. (MKR)

Keynote Speeches.

ERIC Educational Resources Information Center

2000

This document contains the six of the seven keynote speeches from an international conference on vocational education and training (VET) for lifelong learning in the information era. "IVETA (International Vocational Education and Training Association) 2000 Conference 6-9 August 2000" (K.Y. Yeung) discusses the objectives and activities…

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…

Reductions in Radiographic Progression in Early Rheumatoid Arthritis Over Twenty-Five Years: Changing Contribution From Rheumatoid Factor in Two Multicenter UK Inception Cohorts.

PubMed

Carpenter, Lewis; Norton, Sam; Nikiphorou, Elena; Jayakumar, Keeranur; McWilliams, Daniel F; Rennie, Kirsten L; Dixey, Josh; Kiely, Patrick; Walsh, David Andrew; Young, Adam

2017-12-01

To assess the 5-year progression of erosions and joint space narrowing (JSN) and their associations with rheumatoid factor (RF) status in 2 large, multicenter, early rheumatoid arthritis cohorts, spanning 25 years. Radiographic joint damage was recorded using the Sharp/van der Heijde (SHS) method in the Early Rheumatoid Arthritis Study (ERAS), 1986-2001, and the Early Rheumatoid Arthritis Network (ERAN), 2002-2013. Mixed-effects negative binomial regression estimated changes in radiographic damage over 5 years, including erosions and JSN, separately. RF, along with age, sex, and baseline markers of disease activity were controlled for. A total of 1,216 patients from ERAS and 446 from ERAN had radiographic data. Compared to ERAS, ERAN patients had a lower mean total SHS score at baseline (ERAN 6.2 versus ERAS 10.5; P < 0.001) and mean annual rate of change (ERAN 2.5 per year versus ERAS 6.9 per year; P < 0.001). Seventy-four percent of ERAS and 27% of ERAN patients progressed ≥5 units. Lower scores at baseline in ERAN were largely driven by reductions in JSN (ERAS 3.9 versus ERAN 1.2; P < 0.001), along with erosions (ERAS 1.9 versus ERAN 0.8; P < 0.001). RF was associated with greater progression in each cohort, but the absolute difference in mean annual rate of change for RF-positive patients was substantially higher for ERAS (RF positive 8.6 versus RF negative 5.1; P < 0.001), relative to ERAN (RF positive 2.0 versus RF negative 1.9; P = 0.855). Radiographic progression was shown to be significantly reduced between the 2 cohorts, and was associated with lower baseline damage and other factors, including changes in early disease-modifying antirheumatic drug use. The impact of RF status as a prognostic marker of clinically meaningful change in radiographic progression has markedly diminished in the context of more modern treatment. © 2017, American College of Rheumatology.

Evaluation of ERA-Interim precipitation data in complex terrain

NASA Astrophysics Data System (ADS)

Gao, Lu; Bernhardt, Matthias; Schulz, Karsten

2013-04-01

Precipitation controls a large variety of environmental processes, which is an essential input parameter for land surface models e.g. in hydrology, ecology and climatology. However, rain gauge networks provides the necessary information, are commonly sparse in complex terrains, especially in high mountainous regions. Reanalysis products (e.g. ERA-40 and NCEP-NCAR) as surrogate data are increasing applied in the past years. Although they are improving forward, previous studies showed that these products should be objectively evaluated due to their various uncertainties. In this study, we evaluated the precipitation data from ERA-Interim, which is a latest reanalysis product developed by ECMWF. ERA-Interim daily total precipitation are compared with high resolution gridded observation dataset (E-OBS) at 0.25°×0.25° grids for the period 1979-2010 over central Alps (45.5-48°N, 6.25-11.5°E). Wet or dry day is defined using different threshold values (0.5mm, 1mm, 5mm, 10mm and 20mm). The correspondence ratio (CR) is applied for frequency comparison, which is the ratio of days when precipitation occurs in both ERA-Interim and E-OBS dataset. The result shows that ERA-Interim captures precipitation occurrence very well with a range of CR from 0.80 to 0.97 for 0.5mm to 20mm thresholds. However, the bias of intensity increases with rising thresholds. Mean absolute error (MAE) varies between 4.5 mm day-1 and 9.5 mm day-1 in wet days for whole area. In term of mean annual cycle, ERA-Interim almost has the same standard deviation of the interannual variability of daily precipitation with E-OBS, 1.0 mm day-1. Significant wet biases happened in ERA-Interim throughout warm season (May to August) and dry biases in cold season (November to February). The spatial distribution of mean annual daily precipitation shows that ERA-Interim significant underestimates precipitation intensity in high mountains and northern flank of Alpine chain from November to March while pronounced overestimate in the southern flank of Alps. The poor topographical and flow related characteristic representation of ERA-Interim model is possibly responsible for the bias. Particularly, the mountain block effect of moisture is weak captured. The comparison demonstrates that ERA-Interim precipitation intensity needs bias correction for further alpine climate studies, although it reasonably captures precipitation frequency. This critical evaluation not only diagnosed the data quality of ERA-Interim, but also provided the evidence for reanalysis products downscaling and bias correction in complex terrain.

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.

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era.

PubMed

Best, Michele; Sakande, Jean

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state's public health system and is critical to the development of a robust national laboratory response network to meet global health security threats.

De-optical-line-terminal hybrid access-aggregation optical network for time-sensitive services based on software-defined networking orchestration

NASA Astrophysics Data System (ADS)

Bai, Wei; Yang, Hui; Xiao, Hongyun; Yu, Ao; He, Linkuan; Zhang, Jie; Li, Zhen; Du, Yi

2017-11-01

With the increase in varieties of services in network, time-sensitive services (TSSs) appear and bring forward an impending need for delay performance. Ultralow-latency communication has become one of the important development goals for many scenarios in the coming 5G era (e.g., robotics and driverless cars). However, the conventional methods, which decrease delay by promoting the available resources and the network transmission speed, have limited effect; a new breakthrough for ultralow-latency communication is necessary. We propose a de-optical-line-terminal (De-OLT) hybrid access-aggregation optical network (DAON) for TSS based on software-defined networking (SDN) orchestration. In this network, low-latency all-optical communication based on optical burst switching can be achieved by removing OLT. For supporting this network and guaranteeing the quality of service for TSSs, we design SDN-driven control method and service provision method. Numerical results demonstrate the proposed DAON promotes network service efficiency and avoids traffic congestion.

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era

PubMed Central

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state’s public health system and is critical to the development of a robust national laboratory response network to meet global health security threats. PMID:28879137

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.

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.

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.

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.

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.

Online Learning: Cheap Degrees or Educational Pluralization?

ERIC Educational Resources Information Center

Ragusa, Angela T.; Crampton, Andrea

2017-01-01

In an era of shifting social and communication norms, where 76% of Americans surveyed reported they reached for tablets to check online communication before saying "good morning" to partners (Kensington.com, 2014), online education's increased popularity as a "lifestyle" choice is unsurprising (Ragusa, 2007). Qualitative…

Learning Leadership Skills in Elementary School

ERIC Educational Resources Information Center

Bowman, Richard F.

2014-01-01

Leadership is everyone's responsibility-even first graders. The most important contribution that any educator can make in an era of unrelenting change is identifying and developing aspiring leaders. Elementary school teachers can embed leadership development opportunities into the classroom to foster leadership dispositions and skills…

High Tech High.

ERIC Educational Resources Information Center

Hardy, Lawrence

2001-01-01

In an era of high-stakes testing and prescriptive teaching styles, a San Diego charter high school embraces project learning, multilevel classrooms, and video portfolios of student work. The school lacks dining, music, and athletic facilities, but features hefty teacher salaries, student freedom, and real-world problem solving. (MLH)

School Principals and Teacher Contract Non-Renewal

ERIC Educational Resources Information Center

Nixon, Andy; Packard, Abbot L.; Dam, Margaret

2011-01-01

In an era of intense state and federal accountability for teaching and student learning, school principals face noteworthy challenges which typically work against recommending contract non-renewal for teachers. School principals confront tremendous pressure from state and federal accountability legislation to produce evidence of student learning…

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…

Optical fiber cable and wiring techniques for fiber to the home (FTTH)

NASA Astrophysics Data System (ADS)

Takai, Hirofumi; Yamauchi, Osamu

2009-08-01

NTT group's new medium-term management strategy calls for 20 million optical subscribers by 2010, and NTT Laboratories is pushing forward to meet this goal. Before that date, an efficient optical access network must be constructed, and afterwards, when the era of mass optical communications finally arrives, the facilities and equipment supporting the network will have to be effectively operated and maintained. At NTT Access Network Service Systems Laboratories, we are developing various technologies to correspond to the massive deployment of optical broadband services. We are also developing various new technologies for efficiently operating optical access network systems that will continue to expand in the future, and to supply our customers with good services. This paper provides an overview of the new optical access network system technologies that are being developed at NTT Access Network Service Systems Laboratories to address these issues.

Smart home design for electronic devices monitoring based wireless gateway network using cisco packet tracer

NASA Astrophysics Data System (ADS)

Sihombing, Oloan; Zendrato, Niskarto; Laia, Yonata; Nababan, Marlince; Sitanggang, Delima; Purba, Windania; Batubara, Diarmansyah; Aisyah, Siti; Indra, Evta; Siregar, Saut

2018-04-01

In the era of technological development today, the technology has become the need for the life of today's society. One is needed to create a smart home in turning on and off electronic devices via smartphone. So far in turning off and turning the home electronic device is done by pressing the switch or remote button, so in control of electronic device control less effective. The home smart design is done by simulation concept by testing system, network configuration, and wireless home gateway computer network equipment required by a smart home network on cisco packet tracer using Internet Thing (IoT) control. In testing the IoT home network wireless network gateway system, multiple electronic devices can be controlled and monitored via smartphone based on predefined configuration conditions. With the Smart Ho me can potentially increase energy efficiency, decrease energy usage costs, control electronics and change the role of residents.

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

Note-taking and Handouts in The Digital Age

PubMed Central

Stacy, Elizabeth Moore

2015-01-01

Most educators consider note-taking a critical component of formal classroom learning. Advancements in technology such as tablet computers, mobile applications, and recorded lectures are altering classroom dynamics and affecting the way students compose and review class notes. These tools may improve a student’s ability to take notes, but they also may hinder learning. In an era of dynamic technology developments, it is important for educators to routinely examine and evaluate influences on formal and informal learning environments. This paper discusses key background literature on student note-taking, identifies recent trends and potential implications of mobile technologies on classroom note-taking and student learning, and discusses future directions for note-taking in the context of digitally enabled lifelong learning. PMID:27168620

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.

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…

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 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.

Suicide Prevention in the Dot Com Era: Technological Aspects of a University Suicide Prevention Program

ERIC Educational Resources Information Center

Manning, Jessica; VanDeusen, Karen

2011-01-01

Western Michigan University's Suicide Prevention Program utilizes multiple technological components, including an online training course, a Web site, and 2 social networking Web site profiles, as integral aspects of a comprehensive program. This article discusses the development, maintenance, use, and impact of the technological aspects of this…

Student Services Go Social

ERIC Educational Resources Information Center

Villano, Matt; Gullon, Monica

2009-01-01

Like fine wines, Web 2.0 technologies get better with age. Gone are the days of the pointless chat room; this is the era of social networking juggernauts such as Facebook, MySpace, and Friendster. Services offered by these firms are helpful in facilitating connections among users in every industry and of every age. In higher education, however, a…

Information Management Basics in a New Technological Era. Proceedings of the 1984 CAUSE National Conference (Orlando, Florida, December 1984).

ERIC Educational Resources Information Center

CAUSE, Boulder, CO.

Proceedings of the 1984 CAUSE conference on information management and new technologies are presented. Contents include 49 papers covering seven subject areas: issues in higher education, managing the information resource, innovative technologies, office automation/networking, microcomputer issues and applications, promises and perils of…

Going Virtual to Engage a Global Museum Community

ERIC Educational Resources Information Center

Whitney, Katherine

2011-01-01

Created at the dawn of the social networking era, the International Museum of Women (IMOW) is an online museum that has consistently harnessed online technology in the service of its mission. Recognizing that online technology is evolving and ever changing, the museum must be flexible, adapting delivery of its content to the tools available at…

Cheating or Cheated? Surviving Secondary Exit Exams in a Neoliberal Era

ERIC Educational Resources Information Center

Buckner, Elizabeth; Hodges, Rebecca

2016-01-01

Cheating on exams is a rampant and highly developed practice among youth in the Arab world, often involving elaborate networks, advanced technology and adult authorities. Rather than viewing cheating as mere laziness or immorality, this article interrogates the social meanings of cheating by comparing the practices and discourses of cheating on…

Mobile-Computing Trends: Lighter, Faster, Smarter

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2008-01-01

The new era of mobile computing promises greater variety in applications, highly improved usability, and speedier networking. The 3G iPhone from Apple is the poster child for this trend, but there are plenty of other developments that point in this direction. Previous surveys, in LLT, and by researchers at the UK's Open University, have…

Innovation on Energy Power Technology (22)Challenge to Development of Expert System stored Knowledge of Expert Power Network Operators

NASA Astrophysics Data System (ADS)

Sakaguchi, Hideharu

Do you remember an expert system? I think there are various impressions about the system. For example, some might say “It reminds me of old days”. On the other hand, some might say “It was really troublesome”. About 25 years ago, from late 1980s to the middle of 1990s, when the Showa era was about to change into the Heisei Era, artificial intelligence boomed. Research and development for an expert system which was equipped with expertise and worked as smart as expert, was advanced in various fields. Our company also picked up the system as the new system which covered weak point of conventional computer technology. We started research and development in 1984, and installed an expert system in a SCADA system, which started operating in March 1990 in the Fukuoka Integrated Control Center. In this essay, as an electric power engineer who involved in development at that time, I introduce the situation and travail story about developing an expert system which support restorative actions from the outage and overload condition of power networks.

Drosophila and experimental neurology in the post-genomic era.

PubMed

Shulman, Joshua M

2015-12-01

For decades, the fruit fly, Drosophila melanogaster, has been among the premiere genetic model systems for probing fundamental neurobiology, including elucidation of mechanisms responsible for human neurologic disorders. Flies continue to offer virtually unparalleled versatility and speed for genetic manipulation, strong genomic conservation, and a nervous system that recapitulates a range of cellular and network properties relevant to human disease. I focus here on four critical challenges emerging from recent advances in our understanding of the genomic basis of human neurologic disorders where innovative experimental strategies are urgently needed: (1) pinpointing causal genes from associated genomic loci; (2) confirming the functional impact of allelic variants; (3) elucidating nervous system roles for novel or poorly studied genes; and (4) probing network interactions within implicated regulatory pathways. Drosophila genetic approaches are ideally suited to address each of these potential translational roadblocks, and will therefore contribute to mechanistic insights and potential breakthrough therapies for complex genetic disorders in the coming years. Strategic collaboration between neurologists, human geneticists, and the Drosophila research community holds great promise to accelerate progress in the post-genomic era. Copyright © 2015 Elsevier Inc. All rights reserved.

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…

The Ontology of Science Teaching in the Neoliberal Era

ERIC Educational Resources Information Center

Sharma, Ajay

2017-01-01

Because of ever stricter standards of accountability, science teachers are under an increasing and unrelenting pressure to demonstrate the effects of their teaching on student learning. Econometric perspectives of "teacher quality" have become normative in assessment of teachers' work for accountability purposes. These perspectives seek…

Standardization of SOPs to Evaluations: Impacts on Regulatory Decisions using Learning and Memory as Case Studies

EPA Science Inventory

In an era of global trade and regulatory cooperation, consistent and scientifically based interpretation of developmental neurotoxicity (DNT) studies is essential, particularly for non­ standard assays and variable endpoints. Because there is flexibility in the selection of ...

The transformation of disabilities organizations.

PubMed

Schalock, Robert L; Verdugo, Miguel-Angel

2013-08-01

This article summarizes the five major characteristics of the transformation era and describes how intellectual and closely related developmental disabilities organizations can apply specific transformation strategies associated with each characteristic. Collectively, the characteristics and strategies provide a framework for transformation thinking, learning, and acting. Specific application examples are given.

Programme Development. Paper Presentations: Session F.

ERIC Educational Resources Information Center

2000

This document contains 35 papers from the program development section of an international conference on vocational education and training (VET) for lifelong learning in the information era. The following are among the papers included: "Using Quality Indicators to Create World-Class Curricula: From Concept to Application" (Curtis Finch,…

Discourse, Power, Social Abuse and Vigilance: Learning about the "Holodomor" in English Studies Classrooms.

ERIC Educational Resources Information Center

Robertson, Judith P.

1997-01-01

Explores some of the possibilities and problems of teaching secondary school students about genocide through the study of language used to describe the event. Focuses on Eastern Europe during the Stalin era when a catastrophe known as the "Holodomor" occurred. (PA)

International Issues. Paper Presentations: Session C.

ERIC Educational Resources Information Center

2000

This document contains eight papers from the international issues section of an international conference on vocational education and training (VET) for lifelong learning in the information era. The following papers are included: "The Impact of Globalisation and the Changing Nature of Work on Vocational Education and Training" (Chris…

Social and Ethical Issues. Paper Presentations: Session A.

ERIC Educational Resources Information Center

2000

This document contains nine papers from the social and ethical issues section of an international conference on vocational education and training (VET) for lifelong learning in the information era. The following papers are included: "Attitudes of University Faculty Members toward Students with Disabilities" (Marie F. Kraska);…

Lifelong Learning Tendencies of Prospective Teachers

ERIC Educational Resources Information Center

Cetin, Saban; Cetin, Filiz

2017-01-01

Stunning developments in this era have brought different meanings in both educational conditions and time and space in education. Developing technologies have made education applicable everywhere. In other words, education has been taken outside of the known space (classic school walls). Individuals' constant innovation has caused the development…

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.

Understanding the I/O Performance Gap Between Cori KNL and Haswell

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

Liu, Jialin; Koziol, Quincey; Tang, Houjun

2017-05-01

The Cori system at NERSC has two compute partitions with different CPU architectures: a 2,004 node Haswell partition and a 9,688 node KNL partition, which ranked as the 5th most powerful and fastest supercomputer on the November 2016 Top 500 list. The compute partitions share a common storage configuration, and understanding the IO performance gap between them is important, impacting not only to NERSC/LBNL users and other national labs, but also to the relevant hardware vendors and software developers. In this paper, we have analyzed performance of single core and single node IO comprehensively on the Haswell and KNL partitions,more » and have discovered the major bottlenecks, which include CPU frequencies and memory copy performance. We have also extended our performance tests to multi-node IO and revealed the IO cost difference caused by network latency, buffer size, and communication cost. Overall, we have developed a strong understanding of the IO gap between Haswell and KNL nodes and the lessons learned from this exploration will guide us in designing optimal IO solutions in many-core era.« less

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…

An Interactive Learning Environment for Teaching the Imperative and Object-Oriented Programming Techniques in Various Learning Contexts

NASA Astrophysics Data System (ADS)

Xinogalos, Stelios

The acquisition of problem-solving and programming skills in the era of knowledge society seems to be particularly important. Due to the intrinsic difficulty of acquiring such skills various educational tools have been developed. Unfortunately, most of these tools are not utilized. In this paper we present the programming microworlds Karel and objectKarel that support the procedural-imperative and Object-Oriented Programming (OOP) techniques and can be used for supporting the teaching and learning of programming in various learning contexts and audiences. The paper focuses on presenting the pedagogical features that are common to both environments and mainly on presenting the potential uses of these environments.

Nurses on a mission: a professional service learning experience with the inner-city homeless.

PubMed

Lashley, Mary

2007-01-01

Nursing students can play a vital role in addressing the health care needs of the homeless. Through professional service learning experiences in community-based settings, students learn how to partner with key community leaders and agencies to meet the needs of underserved populations and provide culturally competent care to diverse populations. This article describes the development of a professional service learning experience with the homeless in which a community-academic partnership was created to meet community needs. In an era of declining health care resources, such innovative partnerships serve to reduce health disparities and improve access to care while preparing students for community-based practice with at-risk and vulnerable populations.

Stable isotope analysis of a pre-Hispanic Andean community: Reconstructing pre-Wari and Wari era diets in the hinterland of the Wari empire, Peru.

PubMed

Tung, Tiffiny A; Knudson, Kelly J

2018-01-01

Stable carbon and nitrogen isotope analysis is used to reconstruct diet among a pre-Hispanic population from the Peruvian Andes to evaluate whether local foodways changed with Wari imperial influence in the region. This study also compares local diet to other Wari-era sites. Samples derive from the site of Beringa in Peru and correspond primarily to pre-Wari (200-600 CE) and Wari (600-1,000 CE). We examine stable carbon isotopes from enamel (n = 29) and bone apatite (n = 22), and stable carbon and nitrogen isotopes from bone collagen (n = 29), and we present stable carbon and nitrogen isotope data on archaeological and modern fauna (n = 37) and plants (n = 19) from the region. There were no significant differences in either δ 13 C or δ 15 N from the pre-Wari to Wari era, indicating that those measurable aspects of diet did not change with Wari influence. There were no sex-based differences among juveniles (as inferred from δ 13 C from enamel carbonates) nor among adults (based on δ 13 C and δ 15 N from adult bone collagen). Comparisons to other Wari era sites show that Beringa individuals exhibited significantly lower δ 13 C values, suggesting that they consumed significantly less maize, a socially valued food. Further, the Froehle et al. (2012) stable isotope model suggests that the majority of the Beringa individuals consumed more C 3 than C 4 plants, and dietary protein was derived primarily from terrestrial animals and some marine resources. The similar diets from pre-Wari to Wari times hint at strong local dietary traditions and durable food trade networks during the period of Wari imperial influence. The presence of limited marine foods in the diet suggests a trade network with coastal groups or sojourns to the coast to gather marine resources. © 2017 Wiley Periodicals, Inc.

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.

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.

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.

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…

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.

Information technology, Part 3. The technology hierarchy.

PubMed

Ruffin, M

1996-09-01

The era of the networked society--and medical care depending on networked intelligence--is dawning. Physicians need to plan for office practice information systems in common, with an eye to conveying data electronically between all the locations of care and all the providers involved in caring for defined populations of people. The shared database will become the most important asset of the collection of providers who make up the delivery system that creates it. This will be accomplished by layering technology on local and wide-area networks of group practices, hospitals, health plans, and payers and developing standards that make data accessible in the same format to all users, no matter where they are.

Optical subnet concepts for the deep space network

NASA Technical Reports Server (NTRS)

Shaik, K.; Wonica, D.; Wilhelm, M.

1993-01-01

This article describes potential enhancements to the Deep Space Network, based on a subnet of receiving stations that will utilize optical communications technology in the post-2010 era. Two optical subnet concepts are presented that provide full line-of-sight coverage of the ecliptic, 24 hours a day, with high weather availability. The technical characteristics of the optical station and the user terminal are presented, as well as the effects of cloud cover, transmittance through the atmosphere, and background noise during daytime or nighttime operation on the communications link. In addition, this article identifies candidate geographic sites for the two network concepts and includes a link design for a hypothetical Pluto mission in 2015.

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

Education of the modern surgical resident: novel approaches to learning in the era of the 80-hour workweek.

PubMed

Nguyen, Liz; Brunicardi, F Charles; Dibardino, Daniel J; Scott, Bradford G; Awad, Samir S; Bush, Ruth L; Brandt, Mary L

2006-06-01

Implementation of the 80-hour work week has resulted in limitations on the hours available for resident education, creating a need for innovative approaches to teach surgical residents successfully. Herein we report the methods and results of an innovative didactic learning program at a large academic surgical residency program. Between 2004 and 2005, based on known principles of adult education and innovative learning techniques, a didactic learning program was instituted in a major academic surgery program. The course work consisted of a structured reading program using Schwartz's Textbook of Surgery, with weekly testing and problem-based learning (PBL) groups led by surgical faculty. The residents' progress was assessed by American Board of Surgery In-Training Examination (ABSITE) training scores before and after program implementation. A resident survey was also conducted to assess residents' attitudes toward the new program. Results were reported as a mean, and categoric variables were compared using a paired Student's t-test. During the academic year of the structured reading program, the mean ABSITE score improved by 10% (P=0.02) from the previous year. The postgraduate year 4 class had the largest change, with a score increase of 17% over the previous year's performance (P=0.02). Survey results demonstrated that 64% of the responders agreed that the small-group PBL was preferable for achieving educational goals. Furthermore, 89% of residents responded that the PBL groups improved interaction between residents and faculty members. An innovative formal learning program based on a major surgical textbook with weekly testing and small group sessions can significantly improve surgical training in the modern era of work-hour restrictions. Furthermore, surgical trainees find this format to be innovative and useful for improving didactic teaching.

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

The Vietnam Era: A Guide to Teaching Resources.

ERIC Educational Resources Information Center

Indochina Curriculum Group, Cambridge, MA.

This annotated resource guide contains information and learning activities on the Vietnam War for use by high school history teachers. Annotations of primary source materials, resource materials, textbooks, general interest books, and film and slide shows from several competing points of view are included. The developers believe that students who…

Comprehending the Critical Importance of Vocational Technical Education in a Global Economy Era.

ERIC Educational Resources Information Center

Wolansky, William D.

1990-01-01

Industrialized nations have learned that vocational education is essential to developing a skilled work force. Newly industrialized countries competing in the global economy are finding that automation, multinational companies, and rapid growth are making investment in human resources through training a critical strategy. (SK)

Twitter and Physics Professional Development

ERIC Educational Resources Information Center

Nadji, Taoufik

2016-01-01

The advent of Twitter® and other social media services of its type ushered in a new era of professional development in education. This article addresses how a group of users have been employing Twitter to conduct professional development sessions that would benefit their participants by advancing their pedagogical approaches to learning and…

Leading an IT Organization Out of Control

ERIC Educational Resources Information Center

Jackson, Gregory A.

2011-01-01

With the era of control ending for campus IT organizations, leaders need to learn to use some known management approaches and methods in radically different ways. In this article, the author begins with some examples of how technology change, organizational change, and contextual change are eroding centralized control over campus information…

Faith and Learning in a Post-Truth World

ERIC Educational Resources Information Center

Jacobsen, Douglas; Jacobsen, Rhonda Hustedt

2018-01-01

American colleges and universities, along with American culture in general, have entered a new post-truth era. In responding to this new environment, colleges and universities might benefit from a more comprehensive engagement with religion and its complex understanding of truth. The model for engagement proposed here focuses on five educational…

Assessment for Learning in the Accountability Era: Queensland, Australia

ERIC Educational Resources Information Center

Klenowski, Val

2011-01-01

Developments in school education in Australia over the past decade have witnessed the rise of national efforts to reform curriculum, assessment and reporting. Constitutionally the power to decide on curriculum matters still resides with the States. Higher stakes in assessment, brought about by national testing and international comparative…

Can Tag Help Schools Teach?

ERIC Educational Resources Information Center

Robert Wood Johnson Foundation, 2012

2012-01-01

Elementary schools are faced with a challenge: boosting student learning in an era when students face far more than schoolwork-related difficulties. Too often today, kids enter the classroom contending with issues ranging from bullying and emotional trauma to family instability and economic hardship--which can lead to behavioral problems that…

Effective Learner-Centered Approach for Teaching an Introductory Digital Systems Course

ERIC Educational Resources Information Center

Debiec, Piotr

2018-01-01

In the Internet era, students have increasingly lost interest in traditional lectures; as a consequence, their learning motivation and exam performance have decreased. The widespread adoption of learner-centered teaching methods that address this issue faces certain barriers, including: 1) the significant faculty effort necessary to prepare…

After the Fall.

ERIC Educational Resources Information Center

Mercer, Joye

1995-01-01

College and universities that invested in the Foundation for New Era Philanthropy, now bankrupt, now find they were taken in by the organization's investment claims. Suits are being filed on behalf of investors and by the Securities and Exchange Commission, claiming fraud. Even institutions not investing in the charity may learn from the…

Assessment That Informs Practice.

ERIC Educational Resources Information Center

Thorson, Annette, Ed.

2000-01-01

Assessment is more than simply ascribing an 'A' or a 'B' to a particular student achievement. In an era of state-mandated proficiencies and alternative assessment strategies, educators need practical ideas they can use to meaningfully assess their students' learning and their own practice. This issue of "ENC Focus" centers on the topic of inquiry…

Women's Education in Saudi Arabia

ERIC Educational Resources Information Center

Alsuwaida, Nouf

2016-01-01

This paper discusses the historical, political, ideological (value), and government policies of women's education in Saudi Arabia implicated within teaching and learning, how women's higher education has changed over time in the realm of Saudi cultural traditions and religious norms. It also highlights the golden era of women's higher education.…

Human Resource Development and Manpower Training. Paper Presentations: Session B.

ERIC Educational Resources Information Center

2000

This document contains 18 papers from the human resource development and manpower training section of an international conference on vocational education and training (VET) for lifelong learning in the information era. The following papers are included: "Use of Social and Economic Modeling to Plan Vocational Education and Training"…

Developing the 21st-Century Social Studies Skills through Technology Integration

ERIC Educational Resources Information Center

Farisi, Mohammad Imam

2016-01-01

Recently, technology has become an educational necessity in global-digital era. Facing these phenomena, social studies (SS) should make innovations related to changes of 21st-century skills and learning paradigm, which is characterized by the principles of disclosure of information, computing, automation, and communication. Technology integration…

Designing Participatory Learning

ERIC Educational Resources Information Center

Vartiainen, Henriikka

2014-01-01

The beginning of the twenty-first century has been described as a time of development for social innovations through which people use, share, and create knowledge in ways that differ fundamentally from those of previous eras. The topical and widely accepted focus of education should be toward twenty-first-century skills. However, there is no…

Houle's Typology: Time for Reconsideration.

ERIC Educational Resources Information Center

Gordon, Howard R. D.

According to Houle's 1961 typology, adult learners may be classified as being primarily goal-oriented, activity-oriented, or learning-oriented learners. Since 1961, society has moved from an industrial age to a post-technological era of information and service. In view of the extensive social changes that have occurred since 1961, Houle's typology…

Research for the Classroom: Standards, Standardization, and Student Learning

ERIC Educational Resources Information Center

Gorlewski, Julie

2013-01-01

In this era of hyper-accountability, teachers are under ever-increasing pressure to demonstrate their worth--often using evidence that is far removed from what seems to be in the best interests of the students. Terms such as "value-added," "evidence-based," and "data-driven" dominate discussions about teaching…

Getting into ENSO

ERIC Educational Resources Information Center

Lee, Hyonyong; Jax, Dan

2004-01-01

To develop scientific literacy in today's global era, however, it is important that students learn about interactions within the Earth's systems worldwide. A unit exploring El Nino and La Nina-phenomena that can result in extreme weather events in locations all around the world-can help bridge this gap and broaden students awareness of global…

An Examination of Advisor Concerns in the Era of Academic Analytics

ERIC Educational Resources Information Center

Daughtry, Jeremy J.

2017-01-01

Performance-based funding models are increasingly becoming the norm for many institutions of higher learning. Such models place greater emphasis on student retention and success metrics, for example, as requirements for receiving state appropriations. To stay competitive, universities have adopted academic analytics technologies capable of…

Learning to Compute: Computerization and Ordinary, Everyday Life

ERIC Educational Resources Information Center

Sullivan, Joseph F.

2009-01-01

This study utilizes the basic framework of classical sociology as a foundation for examining the intersection of the structural history of the computer revolution with ordinary, everyday life. Just as the classical forefathers of modern sociology--Marx, Durkheim, and Weber--attempted to understand their eras of structural transformation, this…

The Programmed Instruction Era: When Effectiveness Mattered

ERIC Educational Resources Information Center

Molenda, Michael

2008-01-01

Programmed instruction (PI) was devised to make the teaching-learning process more humane by making it more effective and customized to individual differences. B.F. Skinner's original prescription was modified by later innovators to incorporate more human interaction, social reinforcers and other forms of feedback, larger and more flexible chunks…

Effects of Organizational Trust on Organizational Learning and Creativity

ERIC Educational Resources Information Center

Jiang, Yi; Chen, Wen-Ke

2017-01-01

In the knowledge economy era, the competitive advantage of an enterprise is established on intangible resources and capability. Trust allows individuals acquiring and exchanging intellectual capitals, especially in ambiguous and uncertain situations, and knowledge exchange relies on the existence of trust. Different from past other industries,…

Achieving More, Spending Less in Schools, Districts, and States

ERIC Educational Resources Information Center

Walberg, Herbert J.

2011-01-01

In an era of financial stringency and demands for better school performance, it is useful to think about several means of raising school productivity: (1) increase learning effectiveness without increasing costs; (2) reduce costs without diminishing effectiveness; or (3) both, that is, increase effectiveness and simultaneously reduce costs. The…

Running on Empty? Finding the Time and Capacity to Lead Learning

ERIC Educational Resources Information Center

Hallinger, Philip; Murphy, Joseph F.

2013-01-01

In recent years, policy changes in American education have refocused a spotlight on principal instructional leadership. Although in previous eras the professional literature exhorted principals to "be instructional leaders," there were few sanctions if they failed to do so. In the current policy context, however, instructional leadership has…

A Course on the Constitution.

ERIC Educational Resources Information Center

Baker, Thomas E.; Viator, James E.

1990-01-01

A law school course about the Constitution's history and theory in the era of its framers is described. The course explores their learning, ideas, and vision and examines the document's intellectual background, writing and ratification processes, major issues and alternatives confronted, and ideas about its function as a form of government. (MSE)

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.

The Reconstruction and Analysis of Gene Regulatory Networks.

PubMed

Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.