Analytical Redundancy Design for Aeroengine Sensor Fault Diagnostics Based on SROS-ELM

Directory of Open Access Journals (Sweden)

Jun Zhou

2016-01-01

Full Text Available Analytical redundancy technique is of great importance to guarantee the reliability and safety of aircraft engine system. In this paper, a machine learning based aeroengine sensor analytical redundancy technique is developed and verified through hardware-in-the-loop (HIL simulation. The modified online sequential extreme learning machine, selective updating regularized online sequential extreme learning machine (SROS-ELM, is employed to train the model online and estimate sensor measurements. It selectively updates the output weights of neural networks according to the prediction accuracy and the norm of output weight vector, tackles the problems of singularity and ill-posedness by regularization, and adopts a dual activation function in the hidden nodes combing neural and wavelet theory to enhance prediction capability. The experimental results verify the good generalization performance of SROS-ELM and show that the developed analytical redundancy technique for aeroengine sensor fault diagnosis based on SROS-ELM is effective and feasible.

A Review of Multimedia Learning Principles: Split-Attention, Modality, and Redundancy Effects

Directory of Open Access Journals (Sweden)

Pelin YUKSEL ARSLAN

2012-02-01

Full Text Available This study aims to present a literature review on three principles of multimedia learningincluding split attention, modality, and redundancy effects with regard to their contribution to cognitiveload theory. According to cognitive load theory, information should be presented by considering excessiveload on working memory. The first principle states that attending to two distinct sources of informationmay impose a high cognitive load, and this process is referred to as the split-attention effect (Kalyuga,Chandler & Sweller, 1991, 1992. The second principle, Modality effect claims that on-screen text shouldbe presented in an auditory form instead of visually when designing a multimedia environment (Moreno &Mayer, 1999. Using more than one sensory mode augments forceful working memory that producesprogressive effects on learning. The third principle redundancy effect occurs when information presentedrepeatedly interferes with learning. This study provides guidance how to create more effective instructionwith multimedia materials for instructional designers.

Learning Networks, Networked Learning

NARCIS (Netherlands)

Sloep, Peter; Berlanga, Adriana

2010-01-01

Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

A Modular Approach to Redundant Robot Control

International Nuclear Information System (INIS)

Anderson, R.J.

1997-12-01

This paper describes a modular approach for computing redundant robot kinematics. First some conventional redundant control methods are presented and shown to be 'passive control laws', i.e. they can be represented by a network consisting of passive elements. These networks are then put into modular form by applying scattering operator techniques. Additional subnetwork modules can then be added to further shape the motion. Modules for obstacle detection, joint limit avoidance, proximity sensing, and for imposing nonlinear velocity constraints are presented. The resulting redundant robot control system is modular, flexible and robust

Southern California Seismic Network: New Design and Implementation of Redundant and Reliable Real-time Data Acquisition Systems

Science.gov (United States)

Saleh, T.; Rico, H.; Solanki, K.; Hauksson, E.; Friberg, P.

2005-12-01

The Southern California Seismic Network (SCSN) handles more than 2500 high-data rate channels from more than 380 seismic stations distributed across southern California. These data are imported real-time from dataloggers, earthworm hubs, and partner networks. The SCSN also exports data to eight different partner networks. Both the imported and exported data are critical for emergency response and scientific research. Previous data acquisition systems were complex and difficult to operate, because they grew in an ad hoc fashion to meet the increasing needs for distributing real-time waveform data. To maximize reliability and redundancy, we apply best practices methods from computer science for implementing the software and hardware configurations for import, export, and acquisition of real-time seismic data. Our approach makes use of failover software designs, methods for dividing labor diligently amongst the network nodes, and state of the art networking redundancy technologies. To facilitate maintenance and daily operations we seek to provide some separation between major functions such as data import, export, acquisition, archiving, real-time processing, and alarming. As an example, we make waveform import and export functions independent by operating them on separate servers. Similarly, two independent servers provide waveform export, allowing data recipients to implement their own redundancy. The data import is handled differently by using one primary server and a live backup server. These data import servers, run fail-over software that allows automatic role switching in case of failure from primary to shadow. Similar to the classic earthworm design, all the acquired waveform data are broadcast onto a private network, which allows multiple machines to acquire and process the data. As we separate data import and export away from acquisition, we are also working on new approaches to separate real-time processing and rapid reliable archiving of real-time data

Information theory and artificial grammar learning: inferring grammaticality from redundancy.

Science.gov (United States)

Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

2016-03-01

In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network.

Science.gov (United States)

Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang

2017-01-01

By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network.

Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites

Directory of Open Access Journals (Sweden)

F. Madonna

2014-11-01

Full Text Available The potential for measurement redundancy to reduce uncertainty in atmospheric variables has not been investigated comprehensively for climate observations. We evaluated the usefulness of entropy and mutual correlation concepts, as defined in information theory, for quantifying random uncertainty and redundancy in time series of the integrated water vapour (IWV and water vapour mixing ratio profiles provided by five highly instrumented GRUAN (GCOS, Global Climate Observing System, Reference Upper-Air Network stations in 2010–2012. Results show that the random uncertainties on the IWV measured with radiosondes, global positioning system, microwave and infrared radiometers, and Raman lidar measurements differed by less than 8%. Comparisons of time series of IWV content from ground-based remote sensing instruments with in situ soundings showed that microwave radiometers have the highest redundancy with the IWV time series measured by radiosondes and therefore the highest potential to reduce the random uncertainty of the radiosondes time series. Moreover, the random uncertainty of a time series from one instrument can be reduced by ~ 60% by constraining the measurements with those from another instrument. The best reduction of random uncertainty is achieved by conditioning Raman lidar measurements with microwave radiometer measurements. Specific instruments are recommended for atmospheric water vapour measurements at GRUAN sites. This approach can be applied to the study of redundant measurements for other climate variables.

Learners misperceive the benefits of redundant text in multimedia learning.

Science.gov (United States)

Fenesi, Barbara; Kim, Joseph A

2014-01-01

Research on metacognition has consistently demonstrated that learners fail to endorse instructional designs that produce benefits to memory, and often prefer designs that actually impair comprehension. Unlike previous studies in which learners were only exposed to a single multimedia design, the current study used a within-subjects approach to examine whether exposure to both redundant text and non-redundant text multimedia presentations improved learners' metacognitive judgments about presentation styles that promote better understanding. A redundant text multimedia presentation containing narration paired with verbatim on-screen text (Redundant) was contrasted with two non-redundant text multimedia presentations: (1) narration paired with images and minimal text (Complementary) or (2) narration paired with minimal text (Sparse). Learners watched presentation pairs of either Redundant + Complementary, or Redundant + Sparse. Results demonstrate that Complementary and Sparse presentations produced highest overall performance on the final comprehension assessment, but the Redundant presentation produced highest perceived understanding and engagement ratings. These findings suggest that learners misperceive the benefits of redundant text, even after direct exposure to a non-redundant, effective presentation.

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

Science.gov (United States)

Brady, Arthur; Maxwell, Kyle; Daniels, Noah; Cowen, Lenore J

2009-01-01

As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model) motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all). We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Beyond redundancy how geographic redundancy can improve service availability and reliability of computer-based systems

CERN Document Server

Bauer, Eric; Eustace, Dan

2012-01-01

"While geographic redundancy can obviously be a huge benefit for disaster recovery, it is far less obvious what benefit is feasible and likely for more typical non-catastrophic hardware, software, and human failures. Georedundancy and Service Availability provides both a theoretical and practical treatment of the feasible and likely benefits of geographic redundancy for both service availability and service reliability. The text provides network/system planners, IS/IT operations folks, system architects, system engineers, developers, testers, and other industry practitioners with a general discussion about the capital expense/operating expense tradeoff that frames system redundancy and georedundancy"--

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Science.gov (United States)

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Neural redundancy applied to the parity space for signal validation

International Nuclear Information System (INIS)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Martinez, Aquilino Senra

2005-01-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

Neural redundancy applied to the parity space for signal validation

Energy Technology Data Exchange (ETDEWEB)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: cmnap@ien.gov.br; Martinez, Aquilino Senra [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia]. E-mail: aquilino@lmp.br

2005-07-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

Directory of Open Access Journals (Sweden)

Arthur Brady

Full Text Available As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all. We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

Redundancy Elimination in DTN via ACK Mechanism

Directory of Open Access Journals (Sweden)

Xiqing Zhang

2015-08-01

Full Text Available The traditional routing protocols for delay tolerant networks (DTN usually take the strategy of spreading multiple copies of one message to the networks. When one copy reaches destination, the transmission of other copies not only waste the bandwidth but also deprive other messages of the opportunities for transmission. This paper brings up a mechanism to eliminate the redundant copies. By adding an acknowledge field to the packet header to delete redundant copies, it can degrade the network overhead while improve the delivery ratio. Simulation results confirm that the proposed method can improve the performance of epidemic and Spray and Wait routing protocol.

SIMULATION MODEL FOR DESIGN SUPPORT OF INFOCOMM REDUNDANT SYSTEMS

Directory of Open Access Journals (Sweden)

V. A. Bogatyrev

2016-09-01

Full Text Available Subject of Research. The paper deals with the effectiveness of multipath transfer of request copies through the network and their redundant service without the use of laborious analytical modeling. The model and support tools for the design of highly reliable distributed systems based on simulation modeling have been created. Method. The effectiveness of many variants of service organization and delivery through the network to the query servers is formed and analyzed. Options for providing redundant service and delivery via the network to the servers of request copies are also considered. The choice of variants for the distribution and service of requests is carried out taking into account the criticality of queries to the time of their stay in the system. The request is considered successful if at least one of its copies is accurately delivered to the working server, ready to service the request received through a network, if it is fulfilled in the set time. Efficiency analysis of the redundant transmission and service of requests is based on the model built in AnyLogic 7 simulation environment. Main Results. Simulation experiments based on the proposed models have shown the effectiveness of redundant transmission of copies of queries (packets to the servers in the cluster through multiple paths with redundant service of request copies by a group of servers in the cluster. It is shown that this solution allows increasing the probability of exact execution of at least one copy of the request within the required time. We have carried out efficiency evaluation of destruction of outdated request copies in the queues of network nodes and the cluster. We have analyzed options for network implementation of multipath transfer of request copies to the servers in the cluster over disjoint paths, possibly different according to the number of their constituent nodes. Practical Relevance. The proposed simulation models can be used when selecting the optimal

A Comparison of Routing Protocol for WSNs: Redundancy Based Approach A Comparison of Routing Protocol for WSNs: Redundancy Based Approach

Directory of Open Access Journals (Sweden)

Anand Prakash

2014-03-01

Full Text Available Wireless Sensor Networks (WSNs with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network localization as much as one could, for efficient and optimal utilization of resources. Different approaches based upon redundancy are proposed for optimum functionality. Localization is always related with redundancy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose a comparison of classic flooding and the gossip protocol for homogenous networks which enhances stability and throughput quiet significantly.  

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

Science.gov (United States)

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.

Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

Directory of Open Access Journals (Sweden)

Peter Sloep

2011-10-01

Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition

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Sheng Shen

2018-04-01

Full Text Available The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1 Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2 Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods.

Learning Analytics for Networked Learning Models

Science.gov (United States)

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…

One Size Does Not Fit All: Older Adults Benefit From Redundant Text in Multimedia Instruction

Directory of Open Access Journals (Sweden)

Barbara eFenesi

2015-07-01

Full Text Available The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration, Redundant (audio narration with redundant text, or Complementary (audio narration with non–redundant text and images. Younger participants learned better when audio narration was paired with relevant images compared to when audio narration was paired with redundant text. However, older participants learned best when audio narration was paired with redundant text. Younger adults, who presumably have a higher working memory capacity, appear to benefit more from complementary information that may drive deeper conceptual processing. In contrast, older adults learn better from presentations that support redundant coding across modalities, which may help mitigate the effects of age-related decline in working memory capacity. Additionally, several misconceptions of design quality appeared across age groups: both younger and older participants positively rated less effective designs. Findings suggest that one-size does not fit all, with older adults requiring unique multimedia design tailored to their cognitive abilities for effective learning.

One size does not fit all: older adults benefit from redundant text in multimedia instruction.

Science.gov (United States)

Fenesi, Barbara; Vandermorris, Susan; Kim, Joseph A; Shore, David I; Heisz, Jennifer J

2015-01-01

The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration), Redundant (audio narration with redundant text), or Complementary (audio narration with non-redundant text and images). Younger participants learned better when audio narration was paired with relevant images compared to when audio narration was paired with redundant text. However, older participants learned best when audio narration was paired with redundant text. Younger adults, who presumably have a higher working memory capacity (WMC), appear to benefit more from complementary information that may drive deeper conceptual processing. In contrast, older adults learn better from presentations that support redundant coding across modalities, which may help mitigate the effects of age-related decline in WMC. Additionally, several misconceptions of design quality appeared across age groups: both younger and older participants positively rated less effective designs. Findings suggest that one-size does not fit all, with older adults requiring unique multimedia design tailored to their cognitive abilities for effective learning.

Learning Networks for Lifelong Learning

OpenAIRE

Sloep, Peter

2009-01-01

Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

Adaptive control of nonlinear system using online error minimum neural networks.

Science.gov (United States)

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

On Redundancy in Describing Linguistic Systems

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Vladimir Borissov Pericliev

2015-12-01

Full Text Available On Redundancy in Describing Linguistic Systems The notion of system of linguistic elements figures prominently in most post-Saussurian linguistics up to the present. A “system” is the network of the contrastive (or, distinctive features each element in the system bears to the remaining elements. The meaning (valeur of each element in the system is the set of features that are necessary and jointly sufficient to distinguish this element from all others. The paper addresses the problems of “redundancy”, i.e. the occurrence of features that are not strictly necessary in describing an element in a system. Redundancy is shown to smuggle into the description of linguistic systems, this infelicitous practice illustrated with some examples from the literature (e.g. the classical phonemic analysis of Russian by Cherry, Halle, and Jakobson, 1953. The logic and psychology of the occurrence of redundancy are briefly sketched and it is shown that, in addition to some other problems, redundancy leads to a huge and unresolvable ambiguity of descriptions of linguistic systems (the Buridan’s ass problem.

One size does not fit all: older adults benefit from redundant text in multimedia instruction

OpenAIRE

Fenesi, Barbara; Vandermorris, Susan; Kim, Joseph A.; Shore, David I.; Heisz, Jennifer J.

2015-01-01

The multimedia design of presentations typically ignores that younger and older adults have varying cognitive strengths and weaknesses. We examined whether differential instructional design may enhance learning in these populations. Younger and older participants viewed one of three computer-based presentations: Audio only (narration), Redundant (audio narration with redundant text), or Complementary (audio narration with non–redundant text and images). Younger participants learned better ...

Redundancy and cooperativity in the mechanics of compositely crosslinked filamentous networks.

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Moumita Das

Full Text Available The cytoskeleton of living cells contains many types of crosslinkers. Some crosslinkers allow energy-free rotations between filaments and others do not. The mechanical interplay between these different crosslinkers is an open issue in cytoskeletal mechanics. Therefore, we develop a theoretical framework based on rigidity percolation to study a generic filamentous system containing both stretching and bond-bending forces to address this issue. The framework involves both analytical calculations via effective medium theory and numerical simulations on a percolating triangular lattice with very good agreement between both. We find that the introduction of angle-constraining crosslinkers to a semiflexible filamentous network with freely rotating crosslinks can cooperatively lower the onset of rigidity to the connectivity percolation threshold-a result argued for years but never before obtained via effective medium theory. This allows the system to ultimately attain rigidity at the lowest concentration of material possible. We further demonstrate that introducing angle-constraining crosslinks results in mechanical behaviour similar to just freely rotating crosslinked semflexible filaments, indicating redundancy and universality. Our results also impact upon collagen and fibrin networks in biological and bio-engineered tissues.

Remote Autonomous Sensor Networks: A Study in Redundancy and Life Cycle Costs

Science.gov (United States)

Ahlrichs, M.; Dotson, A.; Cenek, M.

2017-12-01

The remote nature of the United States and Canada border and their extreme seasonal shifts has made monitoring much of the area impossible using conventional monitoring techniques. Currently, the United States has large gaps in its ability to detect movement on an as-needed-basis in remote areas. The proposed autonomous sensor network aims to meet that need by developing a product that is low cost, robust, and can be deployed on an as-needed-basis for short term monitoring events. This is accomplished by identifying radio frequency disturbance and acoustic disturbance. This project aims to validate the proposed design and offer optimization strategies by conducting a redundancy model as well as performing a Life Cycle Assessment (LCA). The model will incorporate topological, meteorological, and land cover datasets to estimate sensor loss over a three-month period, ensuring that the remaining network does not have significant gaps in coverage which preclude being able to receive and transmit data. The LCA will investigate the materials used to create the sensor to generate an estimate of the total environmental energy that is utilized to create the network and offer alternative materials and distribution methods that can lower this cost. This platform can function as a stand-alone monitoring network or provide additional spatial and temporal resolution to existing monitoring networks. This study aims to create the framework to determine if a sensor's design and distribution is appropriate for the target environment. The incorporation of a LCA will seek to answer if the data a proposed sensor network will collect outweighs the environmental damage that will result from its deployment. Furthermore, as the arctic continues to thaw and economic development grows, the methodology described in paper will function as a guidance document to ensure that future sensor networks have a minimal impact on these pristine areas.

Information filtering based on corrected redundancy-eliminating mass diffusion.

Science.gov (United States)

Zhu, Xuzhen; Yang, Yujie; Chen, Guilin; Medo, Matus; Tian, Hui; Cai, Shi-Min

2017-01-01

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

Research, Boundaries, and Policy in Networked Learning

DEFF Research Database (Denmark)

This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

Distributed redundancy and robustness in complex systems

KAUST Repository

Randles, Martin

2011-03-01

The uptake and increasing prevalence of Web 2.0 applications, promoting new large-scale and complex systems such as Cloud computing and the emerging Internet of Services/Things, requires tools and techniques to analyse and model methods to ensure the robustness of these new systems. This paper reports on assessing and improving complex system resilience using distributed redundancy, termed degeneracy in biological systems, to endow large-scale complicated computer systems with the same robustness that emerges in complex biological and natural systems. However, in order to promote an evolutionary approach, through emergent self-organisation, it is necessary to specify the systems in an \\'open-ended\\' manner where not all states of the system are prescribed at design-time. In particular an observer system is used to select robust topologies, within system components, based on a measurement of the first non-zero Eigen value in the Laplacian spectrum of the components\\' network graphs; also known as the algebraic connectivity. It is shown, through experimentation on a simulation, that increasing the average algebraic connectivity across the components, in a network, leads to an increase in the variety of individual components termed distributed redundancy; the capacity for structurally distinct components to perform an identical function in a particular context. The results are applied to a specific application where active clustering of like services is used to aid load balancing in a highly distributed network. Using the described procedure is shown to improve performance and distribute redundancy. © 2010 Elsevier Inc.

Minimizing Redundant Messages and Improving Search Efficiency under Highly Dynamic Mobile P2P Network

Directory of Open Access Journals (Sweden)

Ajay Arunachalam

2016-02-01

Full Text Available Resource Searching is one of the key functional tasks in large complex networks. With the P2P architecture, millions of peers connect together instantly building a communication pattern. Searching in mobile networks faces additional limitations and challenges. Flooding technique can cope up with the churn and searches aggressively by visiting almost all the nodes. But it exponentially increases the network traffic and thus does not scale well. Further the duplicated query messages consume extra battery power and network bandwidth. The blind flooding also suffers from long delay problem in P2P networks. In this paper, we propose optimal density based flooding resource discovery schemes. Our first model takes into account local graph topology information to supplement the resource discovery process while in our extended version we also consider the neighboring node topology information along with the local node information to further effectively use the mobile and network resources. Our proposed method reduces collision at the same time minimizes effect of redundant messages and failures. Overall the methods reduce network overhead, battery power consumption, query delay, routing load, MAC load and bandwidth usage while also achieving good success rate in comparison to the other techniques. We also perform a comprehensive analysis of the resource discovery schemes to verify the impact of varying node speed and different network conditions.

Conditions for Productive Learning in Network Learning Environments

DEFF Research Database (Denmark)

Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

2004-01-01

are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach......The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Brouns, Francis; Sloep, Peter

2009-01-01

Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

Information filtering based on corrected redundancy-eliminating mass diffusion.

Directory of Open Access Journals (Sweden)

Xuzhen Zhu

Full Text Available Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

Network Learning and Innovation in SME Formal Networks

Directory of Open Access Journals (Sweden)

Jivka Deiters

2013-02-01

Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Meyer Patrick

2007-01-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Patrick E. Meyer

2007-06-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

Science.gov (United States)

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.

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Blending Formal and Informal Learning Networks for Online Learning

Science.gov (United States)

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…

Networked professional learning

NARCIS (Netherlands)

Sloep, Peter

2013-01-01

Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.

Sharing the cost of redundant items

DEFF Research Database (Denmark)

Hougaard, Jens Leth; Moulin, Hervé

2014-01-01

We ask how to share the cost of finitely many public goods (items) among users with different needs: some smaller subsets of items are enough to serve the needs of each user, yet the cost of all items must be covered, even if this entails inefficiently paying for redundant items. Typical examples...... are network connectivity problems when an existing (possibly inefficient) network must be maintained. We axiomatize a family cost ratios based on simple liability indices, one for each agent and for each item, measuring the relative worth of this item across agents, and generating cost allocation rules...... additive in costs....

Learning conditional Gaussian networks

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm

International Nuclear Information System (INIS)

Kim, Heungseob; Kim, Pansoo

2017-01-01

To maximize the reliability of a system, the traditional reliability–redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems. - Highlights: • Optimal strategy is proposed to solve reliability redundancy allocation problem. • The redundancy strategy uses parallel genetic algorithm. • Improved reliability function for a cold standby subsystem is suggested. • Proposed redundancy strategy enhances the system reliability.

Trophic redundancy reduces vulnerability to extinction cascades.

Science.gov (United States)

Sanders, Dirk; Thébault, Elisa; Kehoe, Rachel; Frank van Veen, F J

2018-03-06

Current species extinction rates are at unprecedentedly high levels. While human activities can be the direct cause of some extinctions, it is becoming increasingly clear that species extinctions themselves can be the cause of further extinctions, since species affect each other through the network of ecological interactions among them. There is concern that the simplification of ecosystems, due to the loss of species and ecological interactions, increases their vulnerability to such secondary extinctions. It is predicted that more complex food webs will be less vulnerable to secondary extinctions due to greater trophic redundancy that can buffer against the effects of species loss. Here, we demonstrate in a field experiment with replicated plant-insect communities, that the probability of secondary extinctions is indeed smaller in food webs that include trophic redundancy. Harvesting one species of parasitoid wasp led to secondary extinctions of other, indirectly linked, species at the same trophic level. This effect was markedly stronger in simple communities than for the same species within a more complex food web. We show that this is due to functional redundancy in the more complex food webs and confirm this mechanism with a food web simulation model by highlighting the importance of the presence and strength of trophic links providing redundancy to those links that were lost. Our results demonstrate that biodiversity loss, leading to a reduction in redundant interactions, can increase the vulnerability of ecosystems to secondary extinctions, which, when they occur, can then lead to further simplification and run-away extinction cascades. Copyright © 2018 the Author(s). Published by PNAS.

Social Interaction in Learning Networks

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

New designing of E-Learning systems with using network learning

OpenAIRE

Malayeri, Amin Daneshmand; Abdollahi, Jalal

2010-01-01

One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

Motion control of musculoskeletal systems with redundancy.

Science.gov (United States)

Park, Hyunjoo; Durand, Dominique M

2008-12-01

Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle-subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.

Repetitive motion planning and control of redundant robot manipulators

CERN Document Server

Zhang, Yunong

2013-01-01

Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Informa...

Performance of hybrid-ARQ with incremental redundancy over relay channels

KAUST Repository

Chelli, Ali; Alouini, Mohamed-Slim

2012-01-01

In this paper, we consider a relay network consisting of a source, a relay, and a destination. The source transmits a message to the destination using hybrid automatic repeat request (HARQ) with incremental redundancy (IR). The relay overhears

Fastest learning in small-world neural networks

International Nuclear Information System (INIS)

Simard, D.; Nadeau, L.; Kroeger, H.

2005-01-01

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

The Integration of Personal Learning Environments & Open Network Learning Environments

Science.gov (United States)

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…

Effects of Modality and Redundancy Principles on the Learning and Attitude of a Computer-Based Music Theory Lesson among Jordanian Primary Pupils

Science.gov (United States)

Aldalalah, Osamah Ahmad; Fong, Soon Fook

2010-01-01

The purpose of this study was to investigate the effects of modality and redundancy principles on the attitude and learning of music theory among primary pupils of different aptitudes in Jordan. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The…

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

Edmodo social learning network for elementary school mathematics learning

Science.gov (United States)

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.

Entropy Learning in Neural Network

Directory of Open Access Journals (Sweden)

Geok See Ng

2017-12-01

Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

Science.gov (United States)

Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

2016-12-01

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

The Application of S7-400H redundant PLC in I and C system for waterworks in nuclear power plant

International Nuclear Information System (INIS)

Pang Yuxiang

2013-01-01

This paper introduces Siemens S7-400H redundant PLC which is employed to implement monitor and control systems for waterworks in nuclear power plant. It focuses on the configuration and realization of the redundant system. The safety and reliability of the system is improved by using redundant CPU, network and server. (authors)

Quantitative learning strategies based on word networks

Science.gov (United States)

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.

Distributed Extreme Learning Machine for Nonlinear Learning over Network

Directory of Open Access Journals (Sweden)

Songyan Huang

2015-02-01

Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

Language Choice & Global Learning Networks

Directory of Open Access Journals (Sweden)

Dennis Sayers

1995-05-01

Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

Learning Networks Distributed Environment

NARCIS (Netherlands)

Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

2005-01-01

Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

CERN Document Server

Hu, Fei

2012-01-01

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available 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.

Learning and coding in biological neural networks

Science.gov (United States)

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

Collective Learning in Games through Social Networks

NARCIS (Netherlands)

Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

2015-01-01

This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Changing Conditions for Networked Learning?

DEFF Research Database (Denmark)

Ryberg, Thomas

2011-01-01

in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective...... of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal...

Learning-parameter adjustment in neural networks

Science.gov (United States)

Heskes, Tom M.; Kappen, Bert

1992-06-01

We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

Researching Design, Experience and Practice of Networked Learning

DEFF Research Database (Denmark)

Hodgson, Vivien; de Laat, Maarten; McConnell, David

2014-01-01

and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction...

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

NARCIS (Netherlands)

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

Cooperative Learning for Distributed In-Network Traffic Classification

Science.gov (United States)

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.

Comparative efficacy and safety of different circumcisions for patients with redundant prepuce or phimosis: A network meta-analysis.

Science.gov (United States)

Huang, Chuiguo; Song, Pan; Xu, Changbao; Wang, Ruofan; Wei, Lei; Zhao, Xinghua

2017-07-01

Phimosis and redundant prepuce are defined as the inability of the foreskin to be retracted behind the glans penis in uncircumcised males. To synthesize the evidence and provide the hierarchies of different circumcisions for phimosis and redundant prepuce, we performed an overall network meta-analysis (NMA) based on their comparative efficacy and safety. Electronic databases including PubMed, Embase, Wan Fang, VIP, CNKI and CBM database were researched from randomized controlled trials (RCTs) for redundant prepuce or phimosis. We conducted the direct and indirect comparisons by aggregate data drug information system (ADDIS) software. Moreover, consistency models were applied to assess the differences among the male circumcision practices, and the ranks based on probabilities of intervention for the different endpoints were performed. Node-splitting analysis was used to test inconsistency. Eighteen RCTs were included with 6179 participants. Compared with the conventional circumcision(CC), two new styles of circumcisions, the disposable circumcision suture device(DCSD) and Shang Ring circumcision(SRC), provided significantly shorter operation time[DCSD: standardized mean difference (SMD) = -20.60, 95% credible interval(CI) (-23.38, -17.82); SRC: SMD = -19.16, 95%CI (-21.86, -16.52)], shorter wound healing time [DCSD:SMD = -4.19, 95%CI (-8.24,-0.04); SRC: SMD = 4.55, 95%CI (1.62, 7.57); ] and better postoperative penile appearance [DCSD: odds ratios odds ratios (OR) = 11.42, 95%CI (3.60, 37.68); SRC: OR = 3.85,95%CI (1.29, 12.79)]. Additionally, DCSD showed a lower adverse events rate than other two treatments. However, no significant difference was shown in all surgeries for 24 h postoperative pain score. Node-splitting analysis showed that no significant inconsistency was existed (P > 0.05). Based on the results of NMA, DCSD may be a most effective and safest choice for phimosis and redundant prepuce. DCSD has the advantages of a shorter operation

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

A Team Formation and Project-based Learning Support Service for Social Learning Networks

NARCIS (Netherlands)

Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

2014-01-01

The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

Contingent factors affecting network learning

OpenAIRE

Peters, Linda D.; Pressey, Andrew D.; Johnston, Wesley J.

2016-01-01

To increase understanding of the impact of individuals on organizational learning processes, this paper explores the impact of individual cognition and action on the absorptive capacity process of the wider network. In particular this study shows how contingent factors such as social integration mechanisms and power relationships influence how network members engage in, and benefit from, learning. The use of cognitive consistency and sensemaking theory enables examination of how these conting...

Redundancy Effect on Retention of Vocabulary Words Using Multimedia Presentation

Science.gov (United States)

Samur, Yavuz

2012-01-01

This study was designed to examine the effect of the redundancy principle in a multimedia presentation constructed for foreign language vocabulary learning on undergraduate students' retention. The underlying hypothesis of this study is that when the students are exposed to the material in multiple ways through animation, concurrent narration,…

Developmental changes in children’s processing of redundant modifiers in definite object descriptions

Directory of Open Access Journals (Sweden)

Ruud Koolen

2016-12-01

Full Text Available This paper investigates developmental changes in children’s processing of redundant information in definite object descriptions. In two experiments, children of two age groups (six or seven, and nine or ten years old were presented with pictures of sweets. In the first experiment (pairwise comparison, two identical sweets were shown, and one of these was described with a redundant modifier. After the description, the children had to indicate the sweet they preferred most in a forced-choice task. In the second experiment (graded rating, only one sweet was shown, which was described with a redundant color modifier in half of the cases (e.g., the blue sweet and in the other half of the cases simply as the sweet. This time, the children were asked to indicate on a 5-point rating scale to what extent they liked the sweets. In both experiments, the results showed that the younger children had a preference for the sweets described with redundant information, while redundant information did not have an effect on the preferences for the older children. These results imply that children are learning to distinguish between situations in which redundant information carries an implicature and situations in which this is not the case.

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

OpenAIRE

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

Learning and structure of neuronal networks

Indian Academy of Sciences (India)

We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

How Redundant Are Redundant Color Adjectives? An Efficiency-Based Analysis of Color Overspecification

OpenAIRE

Rubio-Fern?ndez, Paula

2016-01-01

Color adjectives tend to be used redundantly in referential communication. I propose that redundant color adjectives are often intended to exploit a color contrast in the visual context and hence facilitate object identification, despite not being necessary to establish unique reference. Two language-production experiments investigated two types of factors that may affect the use of redundant color adjectives: factors related to the efficiency of color in the visual context and factors relate...

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Industrial plant electrical systems: Simplicity, reliability, cost savings, redundancies

International Nuclear Information System (INIS)

Silvestri, A.; Tommazzolli, F.; Pavia Univ.

1992-01-01

This article represents a compact but complete design and construction manual for industrial plant electrical systems. It is to be used by design engineers having prior knowledge of local power supply routes and voltages and regards principally the optimum choice of internal distribution systems which can be radial or single, double ringed or with various network configurations, and with single or multiple supplies, and many or few redundancies. After giving guidelines on the choosing of these options, the manual deals with problematics relevant to suitable cable sizing. A cost benefit benefit analysis method is suggested for the choice of the number of redundancies. Recommendations are given for the choice of transformers, motorized equipment, switch boards and circuit breakers. Reference is made to Italian electrical safety and building codes

The Redundancy Effect on Retention and Transfer for Individuals with High Symptoms of ADHD

Science.gov (United States)

Brown, Victoria; Lewis, David; Toussaint, Mario

2016-01-01

The multimedia elements of text and audio need to be carefully integrated together to maximize the impact of those elements for learning in a multimedia environment. Redundancy information presented through audio and visual channels can inhibit learning for individuals diagnosed with ADHD, who may experience challenges in the processing of…

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

Directory of Open Access Journals (Sweden)

Mohd Ishak Bin Ismail

2016-02-01

Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

Science.gov (United States)

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

The Use Of Social Networking Sites For Learning In Institutions Of Higher Learning

Directory of Open Access Journals (Sweden)

Mange Gladys Nkatha

2015-08-01

Full Text Available Abstract Institutions of higher learning are facing greater challenges to change and subjected to various transformations in the surrounding environment including technology. These challenge and motivate them to explore new ways to improve their teaching approaches. This study sought to investigate the use of social networking site in institutions of higher learning. To this end two objectives were formulated 1 to investigate the current state of the use of social networking sites by the students 2 investigate how social networking sites can be used to promote authentic learning in institutions of higher learning. The study adopted exploratory approach using descriptive survey design where a sample of 10 67 students were picked from Jomo Kenyatta University of Agriculture and Technology JKUAT main campus. The findings indicate the use of social networking sites is a viable option as the students are not only members of social networking sites but also that majority have access to the requisite technological devices. Additionally recommendations for ensuring authentic learning were presented. The researcher recommends the exploration of the leveraging of the existing social networking sites for learning in conjunction with key stakeholders.

Ecological network analysis for a virtual water network.

Science.gov (United States)

Fang, Delin; Chen, Bin

2015-06-02

The notions of virtual water flows provide important indicators to manifest the water consumption and allocation between different sectors via product transactions. However, the configuration of virtual water network (VWN) still needs further investigation to identify the water interdependency among different sectors as well as the network efficiency and stability in a socio-economic system. Ecological network analysis is chosen as a useful tool to examine the structure and function of VWN and the interactions among its sectors. A balance analysis of efficiency and redundancy is also conducted to describe the robustness (RVWN) of VWN. Then, network control analysis and network utility analysis are performed to investigate the dominant sectors and pathways for virtual water circulation and the mutual relationships between pairwise sectors. A case study of the Heihe River Basin in China shows that the balance between efficiency and redundancy is situated on the left side of the robustness curve with less efficiency and higher redundancy. The forestation, herding and fishing sectors and industrial sectors are found to be the main controllers. The network tends to be more mutualistic and synergic, though some competitive relationships that weaken the virtual water circulation still exist.

Structure of Small World Innovation Network and Learning Performance

Directory of Open Access Journals (Sweden)

Shuang Song

2014-01-01

Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

Pigeons learn stimulus identity and stimulus relations when both serve as redundant, relevant cues during same-different discrimination training.

Science.gov (United States)

Gibson, Brett M; Wasserman, Edward A

2003-01-01

The authors taught pigeons to discriminate displays of 16 identical items from displays of 16 nonidentical items. Unlike most same-different discrimination studies--where only stimulus relations could serve a discriminative function--both the identity of the items and the relations among the items were discriminative features of the displays. The pigeons learned about both stimulus identity and stimulus relations when these 2 sources of information served as redundant, relevant cues. In tests of associative competition, identity cues exerted greater stimulus control than relational cues. These results suggest that the pigeon can respond to both specific stimuli and general relations in the environment.

Mutual information and redundancy in spontaneous communication between cortical neurons.

Science.gov (United States)

Szczepanski, J; Arnold, M; Wajnryb, E; Amigó, J M; Sanchez-Vives, M V

2011-03-01

An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

Learning and Model-checking Networks of I/O Automata

DEFF Research Database (Denmark)

Mao, Hua; Jaeger, Manfred

2012-01-01

We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

Interconnecting Networks of Practice for Professional Learning

Directory of Open Access Journals (Sweden)

Julie Mackey

2011-03-01

Full Text Available The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers create their own networks of practice as they blend online and offline interactions with fellow learners and workplace colleagues. Teachers’ perspectives of their professional learning activities challenge the way universities design formal online learning communities and highlight the potential for networked learning in the zones and intersections between professional practice and study.The article extends the concepts of Lave and Wenger’s (1991 communities of practice social theory of learning by considering the role participants play in determining their engagement and connections in and across boundaries between online learning communities and professional practice. It provides insights into the applicability of connectivist concepts for developing online pedagogies to promote socially networked learning and for emphasising the role of the learner in defining their learning pathways.

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

Science.gov (United States)

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…

Co-Operative Learning and Development Networks.

Science.gov (United States)

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…

Networks and learning in game theory

NARCIS (Netherlands)

Kets, W.

2008-01-01

This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.

An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies

International Nuclear Information System (INIS)

Chambari, Amirhossain; Najafi, Amir Abbas; Rahmati, Seyed Habib A.; Karimi, Aida

2013-01-01

The redundancy allocation problem (RAP) is an important reliability optimization problem. This paper studies a specific RAP in which redundancy strategies are chosen. To do so, the choice of the redundancy strategies among active and cold standby is considered as decision variables. The goal is to select the redundancy strategy, component, and redundancy level for each subsystem such that the system reliability is maximized. Since RAP is a NP-hard problem, we propose an efficient simulated annealing algorithm (SA) to solve it. In addition, to evaluating the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. The results of the performance analysis show a relatively satisfactory efficiency of the proposed SA algorithm

Learning as Issue Framing in Agricultural Innovation Networks

Science.gov (United States)

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…

A Decomposition Algorithm for Learning Bayesian Network Structures from Data

DEFF Research Database (Denmark)

Zeng, Yifeng; Cordero Hernandez, Jorge

2008-01-01

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

Redundancy of Redundancy in Justifications of Verdicts of Polish The Constitutional Tribuna

Directory of Open Access Journals (Sweden)

Jan Winczorek

2016-09-01

Full Text Available The results of an empirical study of 150 justifications of verdicts of the Polish Constitutional Tribunal (CT are discussed. CT justifies its decisions mostly on authoritative references to previous decisions and other doxa- type arguments. It thus does not convince the audience of a decision's validity, but rather documents it. Further, the methodology changes depending on features of the case. The results are analysed using a conceptual    framework    of sociological systems theory. It is shown that CT's justification methodology ignores the redundancy (excess of references and dependencies of the legal system, finding redundancy redundant. This is a risky strategy of decision- making, enabling political influence.

Computational and experimental analysis of redundancy in the central metabolism of Geobacter sulfurreducens.

Directory of Open Access Journals (Sweden)

Daniel Segura

2008-02-01

Full Text Available Previous model-based analysis of the metabolic network of Geobacter sulfurreducens suggested the existence of several redundant pathways. Here, we identified eight sets of redundant pathways that included redundancy for the assimilation of acetate, and for the conversion of pyruvate into acetyl-CoA. These equivalent pathways and two other sub-optimal pathways were studied using 5 single-gene deletion mutants in those pathways for the evaluation of the predictive capacity of the model. The growth phenotypes of these mutants were studied under 12 different conditions of electron donor and acceptor availability. The comparison of the model predictions with the resulting experimental phenotypes indicated that pyruvate ferredoxin oxidoreductase is the only activity able to convert pyruvate into acetyl-CoA. However, the results and the modeling showed that the two acetate activation pathways present are not only active, but needed due to the additional role of the acetyl-CoA transferase in the TCA cycle, probably reflecting the adaptation of these bacteria to acetate utilization. In other cases, the data reconciliation suggested additional capacity constraints that were confirmed with biochemical assays. The results demonstrate the need to experimentally verify the activity of key enzymes when developing in silico models of microbial physiology based on sequence-based reconstruction of metabolic networks.

Learning Bayesian Networks with Incomplete Data by Augmentation

OpenAIRE

Adel, Tameem; de Campos, Cassio P.

2016-01-01

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

OpenAIRE

Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

2016-01-01

Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

How redundant are redundant color adjectives? An efficiency-based analysis of color overspecification

Directory of Open Access Journals (Sweden)

Paula eRubio-Fernández

2016-02-01

Full Text Available Color adjectives tend to be used redundantly in referential communication. I propose that redundant color adjectives are often intended to exploit a color contrast in the visual context and hence facilitate object identification, despite not being necessary to establish unique reference. Two language-production experiments investigated two types of factors that may affect the use of redundant color adjectives: factors related to the efficiency of color in the visual context and factors related to the semantic category of the noun. The results of Experiment 1 confirmed that people produce redundant color adjectives when color may facilitate object recognition; e.g., they do so more often in polychrome displays than in monochrome displays, and more often in English (pre-nominal position than in Spanish (post-nominal position. Redundant color adjectives are also used when color is a central property of the object category; e.g., people referred to the color of clothes more often than to the color of geometrical figures (Experiment 1, and they overspecified atypical colors more often than variable and stereotypical colors (Experiment 2. These results are relevant for pragmatic models of referential communication based on Gricean pragmatics and informativeness. An alternative analysis is proposed, which focuses on the efficiency and pertinence of color in a given referential situation.

Learning dynamic Bayesian networks with mixed variables

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Science.gov (United States)

Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

2016-01-01

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

Identifying Gatekeepers in Online Learning Networks

Science.gov (United States)

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…

Developing a New HSR Switching Node (SwitchBox for Improving Traffic Performance in HSR Networks

Directory of Open Access Journals (Sweden)

Nguyen Xuan Tien

2016-01-01

Full Text Available High availability is crucial for industrial Ethernet networks as well as Ethernet-based control systems such as automation networks and substation automation systems (SAS. Since standard Ethernet does not support fault tolerance capability, the high availability of Ethernet networks can be increased by using redundancy protocols. Various redundancy protocols for Ethernet networks have been developed and standardized, such as rapid spanning tree protocol (RSTP, media redundancy protocol (MRP, parallel redundancy protocol (PRP, high-availability seamless redundancy (HSR and others. RSTP and MRP have switchover delay drawbacks. PRP provides zero recovery time, but requires a duplicate network infrastructure. HSR operation is similar to PRP, but HSR uses a single network. However, the standard HSR protocol is mainly applied to ring-based topologies and generates excessively unnecessary redundant traffic in the network. In this paper, we develop a new switching node for the HSR protocol, called SwitchBox, which is used in HSR networks in order to support any network topology and significantly reduce redundant network traffic, including unicast, multicast and broadcast traffic, compared with standard HSR. By using the SwitchBox, HSR not only provides seamless communications with zero switchover time in case of failure, but it is also easily applied to any network topology and significantly reduces unnecessary redundant traffic in HSR networks.

Robust Learning of Fixed-Structure Bayesian Networks

OpenAIRE

Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

2016-01-01

We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

Statistical and machine learning approaches for network analysis

CERN Document Server

Dehmer, Matthias

2012-01-01

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

Design of a Networked Learning Master Environment for Professionals

DEFF Research Database (Denmark)

Dirckinck-Holmfeld, Lone

2010-01-01

The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how these pri......The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how...

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

Science.gov (United States)

Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields

Language as an information system: redundancy and optimization

Directory of Open Access Journals (Sweden)

Irina Mikhaylovna Nekipelova

2015-11-01

Full Text Available The paper is devoted to research of the language system as an information system. The distinguishing feature of any natural living language system is redundant of elements of its structure. Redundancy, broken terms of universality peculiar to artificial information systems, makes language mobile in time and in space. It should be marked out informational redundancy of two types: language redundancy, when information overlay of language units within the system occurs and speech redundancy when condense of information into syntagmatic level occurs. Language redundancy is potential and speech redundancy is actual. In general, it should be noted that the language redundancy is necessary for language: complicating the relationships between language units, language redundancy creates in language situation of choice, leading to a disorder of language system, increasing of entropy and, as a result, the appearing of the information that can be accepted or cannot be by language system. Language redundancy is one of the reasons for growth of information in language. In addition, the information redundancy in language is one of the factors of language system development.

Learning Latent Structure in Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

Thermodynamic efficiency of learning a rule in neural networks

Science.gov (United States)

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.

The Public Collaboration Lab—Infrastructuring Redundancy with Communities-in-Place

Directory of Open Access Journals (Sweden)

Adam Thorpe

Full Text Available In this article we share an example of challenge-driven learning in design education and consider the contribution of such approaches to the weaving of communities-in-place. We describe the research and practice of the Public Collaboration Lab (PCL, a prototype public social innovation lab developed and tested via a collaborative action research partnership between a London borough council and an art and design university. We make the case that this collaboration is an effective means of bringing capacity in design to public service innovation, granting the redundancy of resources necessary for the experimentation, reflection, and learning that leads to innovation—particularly at a time of financial austerity. We summarize three collaborative design experiments delivered by local government officers working with student designers and residents supported by design researchers and tutors. We identify particular qualities of participatory and collaborative design that foster the construction of meaningful connections among participants in the design process—connections that have the potential to catalyze or strengthen the relationships, experiences, and understandings that contribute to enrich communities-in-place, and infrastructure community resilience in the process. Keywords: Participatory design, Public social innovation, Redundancy, Infrastructuring, Local government

The Effects of Race Conditions When Implementing Single-Source Redundant Clock Trees in Triple Modular Redundant Synchronous Architectures

Science.gov (United States)

Berg, Melanie D.; Kim, Hak S.; Phan, Anthony M.; Seidleck, Christina M.; Label, Kenneth A.; Pellish, Jonathan A.; Campola, Michael J.

2016-01-01

We present the challenges that arise when using redundant clock domains due to their time-skew. Radiation data show that a singular clock domain provides an improved triple modular redundant (TMR) scheme over redundant clocks.

Teachers’ motives for learning in networks : costs, rewards and community interest

NARCIS (Netherlands)

van den Beemt, A.A.J.; Ketelaar, E.; Diepstraten, I.; de Laat, M.

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

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Evolving autonomous learning in cognitive networks.

Science.gov (United States)

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Efficient learning strategy of Chinese characters based on network approach.

Directory of Open Access Journals (Sweden)

Xiaoyong Yan

Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

Tongue Images Classification Based on Constrained High Dispersal Network

Directory of Open Access Journals (Sweden)

Dan Meng

2017-01-01

Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming; Zhang, Jian

2009-01-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

A Network of Local and Redundant Gene Regulation Governs Arabidopsis Seed Maturation

Science.gov (United States)

To, Alexandra; Valon, Christiane; Savino, Gil; Guilleminot, Jocelyne; Devic, Martine; Giraudat, Jérôme; Parcy, François

2006-01-01

In Arabidopsis thaliana, four major regulators (ABSCISIC ACID INSENSITIVE3 [ABI3], FUSCA3 [FUS3], LEAFY COTYLEDON1 [LEC1], and LEC2) control most aspects of seed maturation, such as accumulation of storage compounds, cotyledon identity, acquisition of desiccation tolerance, and dormancy. The molecular basis for complex genetic interactions among these regulators is poorly understood. By analyzing ABI3 and FUS3 expression in various single, double, and triple maturation mutants, we have identified multiple regulatory links among all four genes. We found that one of the major roles of LEC2 was to upregulate FUS3 and ABI3. The lec2 mutation is responsible for a dramatic decrease in ABI3 and FUS3 expression, and most lec2 phenotypes can be rescued by ABI3 or FUS3 constitutive expression. In addition, ABI3 and FUS3 positively regulate themselves and each other, thereby forming feedback loops essential for their sustained and uniform expression in the embryo. Finally, LEC1 also positively regulates ABI3 and FUS3 in the cotyledons. Most of the genetic controls discovered were found to be local and redundant, explaining why they had previously been overlooked. This works establishes a genetic framework for seed maturation, organizing the key regulators of this process into a hierarchical network. In addition, it offers a molecular explanation for the puzzling variable features of lec2 mutant embryos. PMID:16731585

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

. Redundancy and blocking in the spatial domain: A connectionist model

Directory of Open Access Journals (Sweden)

I. P. L. Mc Laren

2002-01-01

Full Text Available How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren & Mackintosh, 1997 and cue redundancy (O’Keefe and Conway, 1978 be reconciled within the framework provided by an error-correcting, connectionist account of spatial navigation? I show that an implementation of McLaren’s (1995 better beta model can serve this purpose, and examine some of the implications for spatial learning and memory.

Machine Learning Topological Invariants with Neural Networks

Science.gov (United States)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

Functionality for learning networks: lessons learned from social web applications

NARCIS (Netherlands)

Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

2007-01-01

Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

Social networks and performance in distributed learning communities

OpenAIRE

Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

On-line learning in radial basis functions networks

OpenAIRE

Freeman, Jason; Saad, David

1997-01-01

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Adaptive Learning in Weighted Network Games

NARCIS (Netherlands)

Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

2017-01-01

This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

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

Science.gov (United States)

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.

Networks of Learning

Science.gov (United States)

Bettencourt, Luis; Kaiser, David

2004-03-01

Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Evolution of individual versus social learning on social networks.

Science.gov (United States)

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.

Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection

Directory of Open Access Journals (Sweden)

Daren Yu

2011-08-01

Full Text Available Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with qmin-Redundancy-Max-Relevanceq, qMax-Dependencyq and min-Redundancy-Max-Dependencyq algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Learning oncogenetic networks by reducing to mixed integer linear programming.

Science.gov (United States)

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.

Software engineering : redundancy is key

NARCIS (Netherlands)

Brand, van den M.G.J.; Groote, J.F.

2015-01-01

Software engineers are humans and so they make lots of mistakes. Typically 1 out of 10 to 100 tasks go wrong. The only way to avoid these mistakes is to introduce redundancy in the software engineering process. This article is a plea to consciously introduce several levels of redundancy for each

Nonlinear Redundancy Analysis. Research Report 88-1.

Science.gov (United States)

van der Burg, Eeke; de Leeuw, Jan

A non-linear version of redundancy analysis is introduced. The technique is called REDUNDALS. It is implemented within the computer program for canonical correlation analysis called CANALS. The REDUNDALS algorithm is of an alternating least square (ALS) type. The technique is defined as minimization of a squared distance between criterion…

The Mobile Learning Network: Getting Serious about Games Technologies for Learning

Science.gov (United States)

Petley, Rebecca; Parker, Guy; Attewell, Jill

2011-01-01

The Mobile Learning Network currently in its third year, is a unique collaborative initiative encouraging and enabling the introduction of mobile learning in English post-14 education. The programme, funded jointly by the Learning and Skills Council and participating colleges and schools and supported by LSN has involved nearly 40,000 learners and…

Learning drifting concepts with neural networks

NARCIS (Netherlands)

Biehl, Michael; Schwarze, Holm

1993-01-01

The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Science.gov (United States)

Wenhui, Ma; Yu, Wang

2017-06-01

Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

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

Science.gov (United States)

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.

How to Trigger Emergence and Self-Organisation in Learning Networks

Science.gov (United States)

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.

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

Science.gov (United States)

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…

A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy

International Nuclear Information System (INIS)

Gholinezhad, Hadi; Zeinal Hamadani, Ali

2017-01-01

This paper develops a new model for redundancy allocation problem. In this paper, like many recent papers, the choice of the redundancy strategy is considered as a decision variable. But, in our model each subsystem can exploit both active and cold-standby strategies simultaneously. Moreover, the model allows for component mixing such that components of different types may be used in each subsystem. The problem, therefore, boils down to determining the types of components, redundancy levels, and number of active and cold-standby units of each type for each subsystem to maximize system reliability by considering such constraints as available budget, weight, and space. Since RAP belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed for solving the problem. Finally, the performance of the proposed algorithm is evaluated by applying it to a well-known test problem from the literature with relatively satisfactory results. - Highlights: • A new model for the redundancy allocation problem in series–parallel systems is proposed. • The redundancy strategy of each subsystem is considered as a decision variable and can be active, cold-standby or mixed. • Component mixing is allowed, in other words components of any subsystem can be non-identical. • A genetic algorithm is developed for solving the problem. • Computational experiments demonstrate that the new model leads to interesting results.

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Directory of Open Access Journals (Sweden)

Wenhui Ma

2017-06-01

Full Text Available Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

Evaluation of software based redundancy algorithms for the EOS storage system at CERN

International Nuclear Information System (INIS)

Peters, Andreas-Joachim; Sindrilaru, Elvin Alin; Zigann, Philipp

2012-01-01

EOS is a new disk based storage system used in production at CERN since autumn 2011. It is implemented using the plug-in architecture of the XRootD software framework and allows remote file access via XRootD protocol or POSIX-like file access via FUSE mounting. EOS was designed to fulfill specific requirements of disk storage scalability and IO scheduling performance for LHC analysis use cases. This is achieved by following a strategy of decoupling disk and tape storage as individual storage systems. A key point of the EOS design is to provide high availability and redundancy of files via a software implementation which uses disk-only storage systems without hardware RAID arrays. All this is aimed at reducing the overall cost of the system and also simplifying the operational procedures. This paper presents the advantages and disadvantages of redundancy by hardware (most classical storage installations) in comparison to redundancy by software. The latter is implemented in the EOS system and achieves its goal by spawning data and parity stripes via remote file access over nodes. The gain in redundancy and reliability comes with a trade-off in the following areas: • Increased complexity of the network connectivity • CPU intensive parity computations during file creation and recovery • Performance loss through remote disk coupling An evaluation and performance figures of several redundancy algorithms are presented for dual parity RAID and Reed-Solomon codecs. Moreover, the characteristics and applicability of these algorithms are discussed in the context of reliable data storage systems.

Social networks as ICT collaborative and supportive learning media ...

African Journals Online (AJOL)

... ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social network are highlighted among which Facebook, ...

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

Science.gov (United States)

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…

Exploring Practice-Research Networks for Critical Professional Learning

Science.gov (United States)

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…

Theoretical framework on selected core issues on conditions for productive learning in networked learning environments

DEFF Research Database (Denmark)

Dirckinck-Holmfeld, Lone; Svendsen, Brian Møller; Ponti, Marisa

The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments.......The report documents and summarises the elements and dimensions that have been identified to describe and analyse the case studies collected in the Kaleidoscope Jointly Executed Integrating Research Project (JEIRP) on Conditions for productive learning in network learning environments....

Logarithmic learning for generalized classifier neural network.

Science.gov (United States)

Ozyildirim, Buse Melis; Avci, Mutlu

2014-12-01

Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards a Social Networks Model for Online Learning & Performance

Science.gov (United States)

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…

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

Science.gov (United States)

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Network anomaly detection a machine learning perspective

CERN Document Server

Bhattacharyya, Dhruba Kumar

2013-01-01

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Social Networking Sites and Language Learning

Science.gov (United States)

Brick, Billy

2011-01-01

This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site (SNS) and asked to learn a…

Availability improvement of layer 2 seamless networks using OpenFlow.

Science.gov (United States)

Molina, Elias; Jacob, Eduardo; Matias, Jon; Moreira, Naiara; Astarloa, Armando

2015-01-01

The network robustness and reliability are strongly influenced by the implementation of redundancy and its ability of reacting to changes. In situations where packet loss or maximum latency requirements are critical, replication of resources and information may become the optimal technique. To this end, the IEC 62439-3 Parallel Redundancy Protocol (PRP) provides seamless recovery in layer 2 networks by delegating the redundancy management to the end-nodes. In this paper, we present a combination of the Software-Defined Networking (SDN) approach and PRP topologies to establish a higher level of redundancy and thereby, through several active paths provisioned via the OpenFlow protocol, the global reliability is increased, as well as data flows are managed efficiently. Hence, the experiments with multiple failure scenarios, which have been run over the Mininet network emulator, show the improvement in the availability and responsiveness over other traditional technologies based on a single active path.

Redundancy in Nigerian Business Organizations: Alternatives ...

African Journals Online (AJOL)

This theoretical discourse examined the incidence of work redundancy in Nigerian organizations as to offer alternative options. Certainly, some redundancy exercises may be necessary for the survival of the organizations but certain variables may influence employees' reactions to the exercises and thus influence the ...

Learning and forgetting on asymmetric, diluted neural networks

International Nuclear Information System (INIS)

Derrida, B.; Nadal, J.P.

1987-01-01

It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks

Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

NARCIS (Netherlands)

Koper, Rob; Sloep, Peter

2003-01-01

Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit

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

Science.gov (United States)

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.

A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

NARCIS (Netherlands)

De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

2008-01-01

De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

Stochastic sensitivity analysis and Langevin simulation for neural network learning

International Nuclear Information System (INIS)

Koda, Masato

1997-01-01

A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method

On the performance of hybrid ARQ with incremental redundancy over amplify-and-forward dual-hop relay networks

KAUST Repository

Hadjtaieb, Amir

2014-09-01

In this paper, we consider a three node relay network comprising a source, a relay, and a destination. The source transmits the message to the destination using hybrid automatic repeat request (HARQ) with incremental redundancy (IR). The relay overhears the transmitted message, amplifies it using a variable gain amplifier, and then forwards the message to the destination. This latter combines both the source and the relay message and tries to decode the information. In case of decoding failure, the destination sends a negative acknowledgement. A new replica of the message containing new parity bits is then transmitted in the subsequent HARQ round. This process continues until successful decoding occurs at the destination or a maximum number M of rounds is reached. We study the performance of HARQ-IR over the considered relay channel from an information theoretic perspective. We derive for instance exact expressions and bounds for the information outage probability, the average number of transmissions, and the average transmission rate. The derived exact expressions are validated by Monte Carlo simulations.

Reinforcement learning account of network reciprocity.

Science.gov (United States)

Ezaki, Takahiro; Masuda, Naoki

2017-01-01

Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

Boltzmann learning of parameters in cellular neural networks

DEFF Research Database (Denmark)

Hansen, Lars Kai

1992-01-01

The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...

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

Science.gov (United States)

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

2015-04-15

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

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Quantum redundancies and local realism

International Nuclear Information System (INIS)

Horodecki, R.; Horodecki, P.

1994-01-01

The basic properties of quantum redundancies are presented. The previous definitions of the informationally coherent quantum (ICQ) system are generalized in terms of the redundancies. The ICQ systems are also considered in the context of local realism in terms of the information integrity factor η. The classical region η≤qslant[1]/[2] for the two classes of mixed, nonfactorizable states admitting the local hidden variable model is found. ((orig.))

Collaborative Supervised Learning for Sensor Networks

Science.gov (United States)

Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

2011-01-01

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

Czech Academy of Sciences Publication Activity Database

Neruda, Roman; Vidnerová, Petra

2009-01-01

Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

Learning State Space Dynamics in Recurrent Networks

Science.gov (United States)

Simard, Patrice Yvon

Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Science.gov (United States)

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Redundancy in Nigerian Business Organizations: Alternatives (Pp ...

African Journals Online (AJOL)

FIRST LADY

Redundancy in Nigerian Business Organizations: Alternatives (Pp. ... When business downturns ... The galloping pace of information technologies is a harbinger of profound ... Redundant staff in public departments can also be retained as.

Redundant correlation effect on personalized recommendation

Science.gov (United States)

Qiu, Tian; Han, Teng-Yue; Zhong, Li-Xin; Zhang, Zi-Ke; Chen, Guang

2014-02-01

The high-order redundant correlation effect is investigated for a hybrid algorithm of heat conduction and mass diffusion (HHM), through both heat conduction biased (HCB) and mass diffusion biased (MDB) correlation redundancy elimination processes. The HCB and MDB algorithms do not introduce any additional tunable parameters, but keep the simple character of the original HHM. Based on two empirical datasets, the Netflix and MovieLens, the HCB and MDB are found to show better recommendation accuracy for both the overall objects and the cold objects than the HHM algorithm. Our work suggests that properly eliminating the high-order redundant correlations can provide a simple and effective approach to accurate recommendation.

Problems in the Deployment of Learning Networks In Small Organizations

NARCIS (Netherlands)

Shankle, Dean E.; Shankle, Jeremy P.

2006-01-01

Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

A Self-Organizing Incremental Neural Network based on local distribution learning.

Science.gov (United States)

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.

A model of primate visual cortex based on category-specific redundancies in natural images

Science.gov (United States)

Malmir, Mohsen; Shiry Ghidary, S.

2010-12-01

Neurophysiological and computational studies have proposed that properties of natural images have a prominent role in shaping selectivity of neurons in the visual cortex. An important property of natural images that has been studied extensively is the inherent redundancy in these images. In this paper, the concept of category-specific redundancies is introduced to describe the complex pattern of dependencies between responses of linear filters to natural images. It is proposed that structural similarities between images of different object categories result in dependencies between responses of linear filters in different spatial scales. It is also proposed that the brain gradually removes these dependencies in different areas of the ventral visual hierarchy to provide a more efficient representation of its sensory input. The authors proposed a model to remove these redundancies and trained it with a set of natural images using general learning rules that are developed to remove dependencies between responses of neighbouring neurons. Results of experiments demonstrate the close resemblance of neuronal selectivity between different layers of the model and their corresponding visual areas.

Parameter diagnostics of phases and phase transition learning by neural networks

Science.gov (United States)

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.

Reinforcement learning account of network reciprocity.

Directory of Open Access Journals (Sweden)

Takahiro Ezaki

Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

N + 1 redundancy on ATCA instrumentation for Nuclear Fusion

Energy Technology Data Exchange (ETDEWEB)

Correia, Miguel, E-mail: miguelfc@ipfn.ist.utl.pt [Associação EURATOM/IST, Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico – Universidade Técnica de Lisboa, Lisboa (Portugal); Sousa, Jorge; Rodrigues, António P.; Batista, António J.N.; Combo, Álvaro; Carvalho, Bernardo B.; Santos, Bruno; Carvalho, Paulo F.; Gonçalves, Bruno [Associação EURATOM/IST, Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico – Universidade Técnica de Lisboa, Lisboa (Portugal); Correia, Carlos M.B.A. [Centro de Instrumentação, Departamento de Física, Universidade de Coimbra, Coimbra (Portugal); Varandas, Carlos A.F. [Associação EURATOM/IST, Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico – Universidade Técnica de Lisboa, Lisboa (Portugal)

2013-10-15

Highlights: ► In Nuclear Fusion, demanding security and high-availability requirements call for redundancy to be available. ► ATCA standard features desirable redundancy features for Fusion instrumentation. ► The developed control and data acquisition hardware modules support additional redundancy schemes. ► Implementation of N + 1 redundancy of host processor and I/O data modules. -- Abstract: The role of redundancy on control and data acquisition systems has gained a significant importance in the case of Nuclear Fusion, as demanding security and high-availability requirements call for redundancy to be available. IPFN's control and data acquisition system hardware is based on an Advanced Telecommunications Computing Architecture (ATCA) set of I/O (DAC/ADC endpoints) and data/timing switch modules, which handle data and timing from all I/O endpoints. Modules communicate through Peripheral Component Interconnect Express (PCIe), established over the ATCA backplane and controlled by one or more external hosts. The developed hardware modules were designed to take advantage of ATCA specification's redundancy features, namely at the hardware management level, including support of: (i) multiple host operation with N + 1 redundancy – in which a designated failover host takes over data previously assigned to a suddenly malfunctioning host and (ii) N + 1 redundancy of I/O and data/timing switch modules. This paper briefly describes IPFN's control and data acquisition system, which is being developed for ITER fast plant system controller (FPSC), and analyses the hardware implementation of its supported redundancy features.

Timing control by redundant inhibitory neuronal circuits

International Nuclear Information System (INIS)

Tristan, I.; Rulkov, N. F.; Huerta, R.; Rabinovich, M.

2014-01-01

Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model

Timing control by redundant inhibitory neuronal circuits

Energy Technology Data Exchange (ETDEWEB)

Tristan, I., E-mail: itristan@ucsd.edu; Rulkov, N. F.; Huerta, R.; Rabinovich, M. [BioCircuits Institute, University of California, San Diego, La Jolla, California 92093-0402 (United States)

2014-03-15

Rhythms and timing control of sequential activity in the brain is fundamental to cognition and behavior. Although experimental and theoretical studies support the understanding that neuronal circuits are intrinsically capable of generating different time intervals, the dynamical origin of the phenomenon of functionally dependent timing control is still unclear. Here, we consider a new mechanism that is related to the multi-neuronal cooperative dynamics in inhibitory brain motifs consisting of a few clusters. It is shown that redundancy and diversity of neurons within each cluster enhances the sensitivity of the timing control with the level of neuronal excitation of the whole network. The generality of the mechanism is shown to work on two different neuronal models: a conductance-based model and a map-based model.

Network Resiliency Implementation in the ATLAS TDAQ System

CERN Document Server

Stancu, S N; The ATLAS collaboration; Batraneanu, S M; Ballestrero, S; Caramarcu, C; Martin, B; Savu, D O; Sjoen, R V; Valsan, L

2010-01-01

The ATLAS TDAQ (Trigger and Data Acquisition) system performs the real-time selection of events produced by the detector. For this purpose approximately 2000 computers are deployed and interconnected through various high speed networks, whose architecture has already been described. This article focuses on the implementation and validation of network connectivity resiliency (previously presented at a conceptual level). Redundancy and eventually load balancing are achieved through the synergy of various protocols: 802.3ad link aggregation, OSPF (Open Shortest Path First), VRRP (Virtual Router Redundancy Protocol), MST (Multiple Spanning Trees). An innovative method for cost-effective redundant connectivity of high-throughput high-availability servers is presented. Furthermore, real-life examples showing how redundancy works, and more importantly how it might fail despite careful planning are presented.

Intuitive theories of information: beliefs about the value of redundancy.

Science.gov (United States)

Soll, J B

1999-03-01

In many situations, quantity estimates from multiple experts or diagnostic instruments must be collected and combined. Normatively, and all else equal, one should value information sources that are nonredundant, in the sense that correlation in forecast errors should be minimized. Past research on the preference for redundancy has been inconclusive. While some studies have suggested that people correctly place higher value on uncorrelated inputs when collecting estimates, others have shown that people either ignore correlation or, in some cases, even prefer it. The present experiments show that the preference for redundancy depends on one's intuitive theory of information. The most common intuitive theory identified is the Error Tradeoff Model (ETM), which explicitly distinguishes between measurement error and bias. According to ETM, measurement error can only be averaged out by consulting the same source multiple times (normatively false), and bias can only be averaged out by consulting different sources (normatively true). As a result, ETM leads people to prefer redundant estimates when the ratio of measurement error to bias is relatively high. Other participants favored different theories. Some adopted the normative model, while others were reluctant to mathematically average estimates from different sources in any circumstance. In a post hoc analysis, science majors were more likely than others to subscribe to the normative model. While tentative, this result lends insight into how intuitive theories might develop and also has potential ramifications for how statistical concepts such as correlation might best be learned and internalized. Copyright 1999 Academic Press.

The Relationships Between Policy, Boundaries and Research in Networked Learning

DEFF Research Database (Denmark)

Ryberg, Thomas; Sinclair, Christine

2016-01-01

the books that include a selection of reworked and peer-reviewed papers from the conference. The 2014 Networked Learning Conference which was held in Edinburgh was characterised by animated dialogue on emergent influences affecting networked teaching and learning building on work established in earlier...

Increasing The Dexterity Of Redundant Robots

Science.gov (United States)

Seraji, Homayoun

1990-01-01

Redundant coordinates used to define additional tasks. Configuration control emerging as effective way to control motions of robot having more degrees of freedom than necessary to define trajectory of end effector and/or of object to be manipulated. Extra or redundant degrees of freedom used to give robot humanlike dexterity and versatility.

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

Science.gov (United States)

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,…

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

Science.gov (United States)

Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

2016-09-14

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

The TENCompetence Infrastructure: A Learning Network Implementation

Science.gov (United States)

Vogten, Hubert; Martens, Harrie; Lemmers, Ruud

The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.

Redundant interferometric calibration as a complex optimization problem

Science.gov (United States)

Grobler, T. L.; Bernardi, G.; Kenyon, J. S.; Parsons, A. R.; Smirnov, O. M.

2018-05-01

Observations of the redshifted 21 cm line from the epoch of reionization have recently motivated the construction of low-frequency radio arrays with highly redundant configurations. These configurations provide an alternative calibration strategy - `redundant calibration' - and boost sensitivity on specific spatial scales. In this paper, we formulate calibration of redundant interferometric arrays as a complex optimization problem. We solve this optimization problem via the Levenberg-Marquardt algorithm. This calibration approach is more robust to initial conditions than current algorithms and, by leveraging an approximate matrix inversion, allows for further optimization and an efficient implementation (`redundant STEFCAL'). We also investigated using the preconditioned conjugate gradient method as an alternative to the approximate matrix inverse, but found that its computational performance is not competitive with respect to `redundant STEFCAL'. The efficient implementation of this new algorithm is made publicly available.

A theoretical design for learning model addressing the networked society

DEFF Research Database (Denmark)

Levinsen, Karin; Nielsen, Janni; Sørensen, Birgitte Holm

2010-01-01

The transition from the industrial to the networked society produces contradictions that challenges the educational system and force it to adapt to new conditions. In a Danish virtual Master in Information and Communication Technologies and Learning (MIL) these contradictions appear as a field of...... which enables students to develop Networked Society competencies and maintain progression in the learning process also during the online periods. Additionally we suggest that our model contributes to the innovation of a networked society's design for learning....... is continuously decreasing. We teach for deep learning but are confronted by students' cost-benefit strategies when they navigate through the study programme under time pressure. To meet these challenges a Design for Learning Model has been developed. The aim is to provide a scaffold that ensures students......' acquisition of the subject matter within a time limit and at a learning quality that support their deep learning process during a subsequent period of on-line study work. In the process of moving from theory to application the model passes through three stages: 1) Conceptual modelling; 2) Orchestration, and 3...

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

Science.gov (United States)

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.

Validating module network learning algorithms using simulated data.

Science.gov (United States)

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

2007-05-03

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

Gamification of learning deactivates the Default Mode Network

Directory of Open Access Journals (Sweden)

Paul Alexander Howard-Jones

2016-01-01

Full Text Available We hypothesised that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN and deactivation of Default Mode Network (DMN regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer, Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards. DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

Gamification of Learning Deactivates the Default Mode Network.

Science.gov (United States)

Howard-Jones, Paul A; Jay, Tim; Mason, Alice; Jones, Harvey

2015-01-01

We hypothesized that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN) and deactivation of default mode network (DMN) regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer), Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points) and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards). DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

Learning, memory, and the role of neural network architecture.

Directory of Open Access Journals (Sweden)

Ann M Hermundstad

2011-06-01

Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Optimal redundant systems for works with random processing time

International Nuclear Information System (INIS)

Chen, M.; Nakagawa, T.

2013-01-01

This paper studies the optimal redundant policies for a manufacturing system processing jobs with random working times. The redundant units of the parallel systems and standby systems are subject to stochastic failures during the continuous production process. First, a job consisting of only one work is considered for both redundant systems and the expected cost functions are obtained. Next, each redundant system with a random number of units is assumed for a single work. The expected cost functions and the optimal expected numbers of units are derived for redundant systems. Subsequently, the production processes of N tandem works are introduced for parallel and standby systems, and the expected cost functions are also summarized. Finally, the number of works is estimated by a Poisson distribution for the parallel and standby systems. Numerical examples are given to demonstrate the optimization problems of redundant systems

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Science.gov (United States)

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Hasan A. A. Al-Rawi

2014-01-01

Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.

A Newton-type neural network learning algorithm

International Nuclear Information System (INIS)

Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.

1993-01-01

First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab

Learning in innovation networks: Some simulation experiments

Science.gov (United States)

Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

2007-05-01

According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

Impact of censoring on learning Bayesian networks in survival modelling.

Science.gov (United States)

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

Reliability Analysis Multiple Redundancy Controller for Nuclear Safety Systems

International Nuclear Information System (INIS)

Son, Gwangseop; Kim, Donghoon; Son, Choulwoong

2013-01-01

This controller is configured for multiple modular redundancy (MMR) composed of dual modular redundancy (DMR) and triple modular redundancy (TMR). The architecture of MRC is briefly described, and the Markov model is developed. Based on the model, the reliability and Mean Time To Failure (MTTF) are analyzed. In this paper, the architecture of MRC for nuclear safety systems is described. The MRC is configured for multiple modular redundancy (MMR) composed of dual modular redundancy (DMR) and triple modular redundancy (TMR). Markov models for MRC architecture was developed, and then the reliability was analyzed by using the model. From the reliability analyses for the MRC, it is obtained that the failure rate of each module in the MRC should be less than 2 Χ 10 -4 /hour and the MTTF average increase rate depending on FCF increment, i. e. ΔMTTF/ΔFCF, is 4 months/0.1

Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

NARCIS (Netherlands)

Rajagopal, Kamakshi

2016-01-01

This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through

Using Epistemic Network Analysis to understand core topics as planned learning objectives

DEFF Research Database (Denmark)

Allsopp, Benjamin Brink; Dreyøe, Jonas; Misfeldt, Morten

Epistemic Network Analysis is a tool developed by the epistemic games group at the University of Wisconsin Madison for tracking the relations between concepts in students discourse (Shaffer 2017). In our current work we are applying this tool to learning objectives in teachers digital preparation....... The danish mathematics curriculum is organised in six competencies and three topics. In the recently implemented learning platforms teacher choose which of the mathematical competencies that serves as objective for a specific lesson or teaching sequence. Hence learning objectives for lessons and teaching...... sequences are defining a network of competencies, where two competencies are closely related of they often are part of the same learning objective or teaching sequence. We are currently using Epistemic Network Analysis to study these networks. In the poster we will include examples of different networks...

Learning-induced pattern classification in a chaotic neural network

International Nuclear Information System (INIS)

Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

2012-01-01

In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

OpenAIRE

Tripathy, Manoj

2012-01-01

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...

Learning of N-layers neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2005-01-01

Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

Science.gov (United States)

Gardner, Brian; Sporea, Ioana; Grüning, André

2015-12-01

Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

Learning the Structure of Bayesian Network from Small Amount of Data

Directory of Open Access Journals (Sweden)

Bogdan COCU

2009-12-01

Full Text Available Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.

The Practices of Student Network as Cooperative Learning in Ethiopia

Science.gov (United States)

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,…

"Getting Practical" and the National Network of Science Learning Centres

Science.gov (United States)

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…

Mimicking Nature´s way of organizing in industry: a network learning perspective

DEFF Research Database (Denmark)

Ulhøi, John Parm; Madsen, Henning

to reconsider organisational learning as being both an internal as well as an external phenomenon. By bringing network learning into an existing interorganisational setting (such as industrial ecology) new potentials for increased learning emerge for the participating companies. The concept of network learning...

Radiation-Tolerance Assessment of a Redundant Wireless Device

Science.gov (United States)

Huang, Q.; Jiang, J.

2018-01-01

This paper presents a method to evaluate radiation-tolerance without physical tests for a commercial off-the-shelf (COTS)-based monitoring device for high level radiation fields, such as those found in post-accident conditions in a nuclear power plant (NPP). This paper specifically describes the analysis of radiation environment in a severe accident, radiation damages in electronics, and the redundant solution used to prolong the life of the system, as well as the evaluation method for radiation protection and the analysis method of system reliability. As a case study, a wireless monitoring device with redundant and diversified channels is evaluated by using the developed method. The study results and system assessment data show that, under the given radiation condition, performance of the redundant device is more reliable and more robust than those non-redundant devices. The developed redundant wireless monitoring device is therefore able to apply in those conditions (up to 10 M Rad (Si)) during a severe accident in a NPP.

Finite time convergent learning law for continuous neural networks.

Science.gov (United States)

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Birth and Death of Redundancy in Decoherence and Quantum Darwinism

Science.gov (United States)

Riedel, Charles; Zurek, Wojciech; Zwolak, Michael

2012-02-01

Understanding the quantum-classical transition and the identification of a preferred classical domain through quantum Darwinism is based on recognizing high-redundancy states as both ubiquitous and exceptional. They are produced ubiquitously during decoherence, as has been demonstrated by the recent identification of very general conditions under which high-redundancy states develop. They are exceptional in that high-redundancy states occupy a very narrow corner of the global Hilbert space; states selected at random are overwelming likely to exhibit zero redundancy. In this letter, we examine the conditions and time scales for the transition from high-redundancy states to zero-redundancy states in many-body dynamics. We identify sufficient condition for the development of redundancy from product states and show that the destruction of redundancy can be accomplished even with highly constrained interactions.

Biologically-inspired On-chip Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

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

Science.gov (United States)

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…

Dialogue, Language and Identity: Critical Issues for Networked Management Learning

Science.gov (United States)

Ferreday, Debra; Hodgson, Vivien; Jones, Chris

2006-01-01

This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…

Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

Science.gov (United States)

Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

2014-07-01

Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

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

Science.gov (United States)

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

2017-03-01

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

Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series-parallel systems

International Nuclear Information System (INIS)

Nahas, Nabil; Nourelfath, Mustapha; Ait-Kadi, Daoud

2007-01-01

The redundancy allocation problem (RAP) is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. This paper presents an efficient algorithm to solve this reliability optimization problem. The idea of a heuristic approach design is inspired from the ant colony meta-heuristic optimization method and the degraded ceiling local search technique. Our hybridization of the ant colony meta-heuristic with the degraded ceiling performs well and is competitive with the best-known heuristics for redundancy allocation. Numerical results for the 33 test problems from previous research are reported and compared. The solutions found by our approach are all better than or are in par with the well-known best solutions

Redundancy Maintenance and Garbage Collection Strategies in Peer-to-Peer Storage Systems

Science.gov (United States)

Liu, Xin; Datta, Anwitaman

Maintaining redundancy in P2P storage systems is essential for reliability guarantees. Numerous P2P storage system maintenance algorithms have been proposed in the last years, each supposedly improving upon the previous approaches. We perform a systematic comparative study of the various strategies taking also into account the influence of different garbage collection mechanisms, an issue not studied so far. Our experiments show that while some strategies generally perform better than some others, there is no universally best strategy, and their relative superiority depends on various other design choices as well as the specific evaluation criterion. Our results can be used by P2P storage systems designers to make prudent design decisions, and our exploration of the various evaluation metrics also provides a more comprehensive framework to compare algorithms for P2P storage systems. While there are numerous network simulators specifically developed even to simulate peer-to-peer networks, there existed no P2P storage simulators - a byproduct of this work is a generic modular P2P storage system simulator which we provide as open-source. Different redundancy, maintenance, placement, garbage-collection policies, churn scenarios can be easily integrated to the simulator to try out new schemes in future, and provides a common framework to compare (future) p2p storage systems designs - something which has not been possible so far.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

Science.gov (United States)

Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

2016-01-01

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

Science.gov (United States)

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Network Resiliency Implementation in the ATLAS TDAQ System

CERN Document Server

Stancu, S N; The ATLAS collaboration

2010-01-01

The ATLAS TDAQ system performs the real time selection of events produced by the detector. For this purpose approximately 2000 computers are deployed and interconnected through various high speed networks, whose architecture has already been described. This article focuses on the implementation and validation of network connectivity resiliency (previously presented at a conceptual level). Redundancy and eventually load balancing are achieved through the synergy of various protocols: 802.3ad link aggregation, OSPF, VRRP, MSTP. An innovative method for cost efficient redundant connectivity of high-throughput high-availability servers is presented. Furthermore, real life examples showing how redundancy works, and more importantly how it might fail despite careful planning are presented.

Network Enabled - Unresolved Residual Analysis and Learning (NEURAL)

Science.gov (United States)

Temple, D.; Poole, M.; Camp, M.

Since the advent of modern computational capacity, machine learning algorithms and techniques have served as a method through which to solve numerous challenging problems. However, for machine learning methods to be effective and robust, sufficient data sets must be available; specifically, in the space domain, these are generally difficult to acquire. Rapidly evolving commercial space-situational awareness companies boast the capability to collect hundreds of thousands nightly observations of resident space objects (RSOs) using a ground-based optical sensor network. This provides the ability to maintain custody of and characterize thousands of objects persistently. With this information available, novel deep learning techniques can be implemented. The technique discussed in this paper utilizes deep learning to make distinctions between nightly data collects with and without maneuvers. Implementation of these techniques will allow the data collected from optical ground-based networks to enable well informed and timely the space domain decision making.

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

Science.gov (United States)

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.

Supervised dictionary learning for inferring concurrent brain networks.

Science.gov (United States)

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Selective Redundancy Removal: A Framework for Data Hiding

Directory of Open Access Journals (Sweden)

Ugo Fiore

2010-02-01

Full Text Available Data hiding techniques have so far concentrated on adding or modifying irrelevant information in order to hide a message. However, files in widespread use, such as HTML documents, usually exhibit high redundancy levels, caused by code-generation programs. Such redundancy may be removed by means of optimization software. Redundancy removal, if applied selectively, enables information hiding. This work introduces Selective Redundancy Removal (SRR as a framework for hiding data. An example application of the framework is given in terms of hiding information in HTML documents. Non-uniformity across documents may raise alarms. Nevertheless, selective application of optimization techniques might be due to the legitimate use of optimization software not supporting all the optimization methods, or configured to not use all of them.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

Celebrating the Tenth Networked Learning Conference: Looking Back and Moving Forward

DEFF Research Database (Denmark)

de Laat, Maarten; Ryberg, Thomas

2018-01-01

conferences with the aim to describe some general trends and developments in networked learning research as they emerge and fade out over the years. In order to do so the authors use the proceedings of each networked learning conference (from 1998 till 2016) as a compiled dataset. This dataset forms a text...... corpus that has been analysed with Voyant tools (Sinclair and Rockwell 2016) specifically designed for analysing digital texts. Voyant tools are used to generate a set of word clouds (Cirrus) in order to visualise networked learning research-related terms that feature most frequently in each set...

Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

Science.gov (United States)

Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen

2016-01-01

This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the "ICT Literacy--Learning in digital networks" learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable…

Reliability optimization of a redundant system with failure dependencies

Energy Technology Data Exchange (ETDEWEB)

Yu Haiyang [Institute Charles Delaunay (ICD, FRE CNRS 2848), Troyes University of Technology, Rue Marie Curie, BP 2060, 10010 Troyes (France)]. E-mail: Haiyang.YU@utt.fr; Chu Chengbin [Institute Charles Delaunay (ICD, FRE CNRS 2848), Troyes University of Technology, Rue Marie Curie, BP 2060, 10010 Troyes (France); Management School, Hefei University of Technology, 193 Tunxi Road, Hefei (China); Chatelet, Eric [Institute Charles Delaunay (ICD, FRE CNRS 2848), Troyes University of Technology, Rue Marie Curie, BP 2060, 10010 Troyes (France); Yalaoui, Farouk [Institute Charles Delaunay (ICD, FRE CNRS 2848), Troyes University of Technology, Rue Marie Curie, BP 2060, 10010 Troyes (France)

2007-12-15

In a multi-component system, the failure of one component can reduce the system reliability in two aspects: loss of the reliability contribution of this failed component, and the reconfiguration of the system, e.g., the redistribution of the system loading. The system reconfiguration can be triggered by the component failures as well as by adding redundancies. Hence, dependency is essential for the design of a multi-component system. In this paper, we study the design of a redundant system with the consideration of a specific kind of failure dependency, i.e., the redundant dependency. The dependence function is introduced to quantify the redundant dependency. With the dependence function, the redundant dependencies are further classified as independence, weak, linear, and strong dependencies. In addition, this classification is useful in that it facilitates the optimization resolution of the system design. Finally, an example is presented to illustrate the concept of redundant dependency and its application in system design. This paper thus conveys the significance of failure dependencies in the reliability optimization of systems.

Reliability optimization of a redundant system with failure dependencies

International Nuclear Information System (INIS)

Yu Haiyang; Chu Chengbin; Chatelet, Eric; Yalaoui, Farouk

2007-01-01

In a multi-component system, the failure of one component can reduce the system reliability in two aspects: loss of the reliability contribution of this failed component, and the reconfiguration of the system, e.g., the redistribution of the system loading. The system reconfiguration can be triggered by the component failures as well as by adding redundancies. Hence, dependency is essential for the design of a multi-component system. In this paper, we study the design of a redundant system with the consideration of a specific kind of failure dependency, i.e., the redundant dependency. The dependence function is introduced to quantify the redundant dependency. With the dependence function, the redundant dependencies are further classified as independence, weak, linear, and strong dependencies. In addition, this classification is useful in that it facilitates the optimization resolution of the system design. Finally, an example is presented to illustrate the concept of redundant dependency and its application in system design. This paper thus conveys the significance of failure dependencies in the reliability optimization of systems

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

Science.gov (United States)

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

SUSTAIN: a network model of category learning.

Science.gov (United States)

Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

2004-04-01

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Exploiting Redundancy in an OFDM SDR Receiver

Directory of Open Access Journals (Sweden)

Tomas Palenik

2009-01-01

Full Text Available Common OFDM system contains redundancy necessary to mitigate interblock interference and allows computationally effective single-tap frequency domain equalization in receiver. Assuming the system implements an outer error correcting code and channel state information is available in the receiver, we show that it is possible to understand the cyclic prefix insertion as a weak inner ECC encoding and exploit the introduced redundancy to slightly improve error performance of such a system. In this paper, an easy way to implement modification to an existing SDR OFDM receiver is presented. This modification enables the utilization of prefix redundancy, while preserving full compatibility with existing OFDM-based communication standards.

Artificial neuron-glia networks learning approach based on cooperative coevolution.

Science.gov (United States)

Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

2015-06-01

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

A smart-pixel holographic competitive learning network

Science.gov (United States)

Slagle, Timothy Michael

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

The heuristic value of redundancy models of aging.

Science.gov (United States)

Boonekamp, Jelle J; Briga, Michael; Verhulst, Simon

2015-11-01

Molecular studies of aging aim to unravel the cause(s) of aging bottom-up, but linking these mechanisms to organismal level processes remains a challenge. We propose that complementary top-down data-directed modelling of organismal level empirical findings may contribute to developing these links. To this end, we explore the heuristic value of redundancy models of aging to develop a deeper insight into the mechanisms causing variation in senescence and lifespan. We start by showing (i) how different redundancy model parameters affect projected aging and mortality, and (ii) how variation in redundancy model parameters relates to variation in parameters of the Gompertz equation. Lifestyle changes or medical interventions during life can modify mortality rate, and we investigate (iii) how interventions that change specific redundancy parameters within the model affect subsequent mortality and actuarial senescence. Lastly, as an example of data-directed modelling and the insights that can be gained from this, (iv) we fit a redundancy model to mortality patterns observed by Mair et al. (2003; Science 301: 1731-1733) in Drosophila that were subjected to dietary restriction and temperature manipulations. Mair et al. found that dietary restriction instantaneously reduced mortality rate without affecting aging, while temperature manipulations had more transient effects on mortality rate and did affect aging. We show that after adjusting model parameters the redundancy model describes both effects well, and a comparison of the parameter values yields a deeper insight in the mechanisms causing these contrasting effects. We see replacement of the redundancy model parameters by more detailed sub-models of these parameters as a next step in linking demographic patterns to underlying molecular mechanisms. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning in Networks for Sustainable Development

NARCIS (Netherlands)

Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

2010-01-01

The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

Redundancy and Reliability for an HPC Data Centre

OpenAIRE

Erhan Yılmaz

2012-01-01

Defining a level of redundancy is a strategic question when planning a new data centre, as it will directly impact the entire design of the building as well as the construction and operational costs. It will also affect how to integrate future extension plans into the design. Redundancy is also a key strategic issue when upgrading or retrofitting an existing facility. Redundancy is a central strategic question to any business that relies on data centres for its operation. In th...

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Science.gov (United States)

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

A hybrid Jaya algorithm for reliability-redundancy allocation problems

Science.gov (United States)

Ghavidel, Sahand; Azizivahed, Ali; Li, Li

2018-04-01

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

Optimize the Power Consumption and SNR of the 3D Photonic High-Radix Switch Architecture Based on Extra Channels and Redundant Rings

Directory of Open Access Journals (Sweden)

Jie Jian

2018-01-01

Full Text Available The demand from exascale computing has made the design of high-radix switch chips an attractive and challenging research field in EHPC (exascale high-performance computing. The static power, due to the thermal sensitivity and process variation of the microresonator rings, and the cross talk noise of the optical network become the main bottlenecks of the network’s scalability. This paper proposes the analyze model of the trimming power, process variation power, and signal-to-noise ratio (SNR for the Graphein-based high-radix optical switch networks and uses the extra channels and the redundant rings to decrease the trimming power and the process variation power. The paper also explores the SNR under different configurations. The simulation result shows that when using 8 extra channels in the 64×64 crossbar optical network, the trimming power reduces almost 80% and the process variation power decreases 65% by adding 16 redundant rings in the 64×64 crossbar optical network. All of these schemes have little influence on the SNR. Meanwhile, the greater channel spacing has great advantages to decrease the static power and increase the SNR of the optical network.

Factors that influence cooperation in networks for innovation and learning

NARCIS (Netherlands)

Sie, Rory; Bitter-Rijpkema, Marlies; Stoyanov, Slavi; Sloep, Peter

2018-01-01

Networked cooperation fails if the available partnerships remain opaque. A literature review and Delphi study uncovered the elements of a fruitful partnership. They relate to personality, diversity, cooperation, and management. Innovation networks and learning networks share the same cooperative

Nuclear power plant monitoring using real-time learning neural network

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

1994-01-01

In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

Implementation of a Framework for Collaborative Social Networks in E-Learning

Science.gov (United States)

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…

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

Science.gov (United States)

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

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

Science.gov (United States)

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.

Social Networks and Performance in Distributed Learning Communities

Science.gov (United States)

Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

DEFF Research Database (Denmark)

Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

2017-01-01

it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...... associations quickly and over long periods of time. The results in this paper demonstrate that the ENTM is able to perform one-shot learning in reinforcement learning tasks without catastrophic forgetting of previously stored associations. Additionally, we introduce a new ENTM default jump mechanism that makes...

Motion compensation via redundant-wavelet multihypothesis.

Science.gov (United States)

Fowler, James E; Cui, Suxia; Wang, Yonghui

2006-10-01

Multihypothesis motion compensation has been widely used in video coding with previous attention focused on techniques employing predictions that are diverse spatially or temporally. In this paper, the multihypothesis concept is extended into the transform domain by using a redundant wavelet transform to produce multiple predictions that are diverse in transform phase. The corresponding multiple-phase inverse transform implicitly combines the phase-diverse predictions into a single spatial-domain prediction for motion compensation. The performance advantage of this redundant-wavelet-multihypothesis approach is investigated analytically, invoking the fact that the multiple-phase inverse involves a projection that significantly reduces the power of a dense-motion residual modeled as additive noise. The analysis shows that redundant-wavelet multihypothesis is capable of up to a 7-dB reduction in prediction-residual variance over an equivalent single-phase, single-hypothesis approach. Experimental results substantiate the performance advantage for a block-based implementation.

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

Science.gov (United States)

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…

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

Science.gov (United States)

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…

Detection of sensor failures in nuclear plants using analytic redundancy

International Nuclear Information System (INIS)

Kitamura, M.

1980-01-01

A method for on-line, nonperturbative detection and identification of sensor failures in nuclear power plants was studied to determine its feasibility. This method is called analytic redundancy, or functional redundancy. Sensor failure has traditionally been detected by comparing multiple signals from redundant sensors, such as in two-out-of-three logic. In analytic redundancy, with the help of an assumed model of the physical system, the signals from a set of sensors are processed to reproduce the signals from all system sensors

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

Science.gov (United States)

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

Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

OpenAIRE

Rajagopal, Kamakshi

2016-01-01

This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through which they reassess their held thoughts and make sense of their experiences together with others.

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

Science.gov (United States)

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

2013-09-01

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

Enhancing Formal E-Learning with Edutainment on Social Networks

Science.gov (United States)

Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

2015-01-01

This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

OpenAIRE

Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

2017-01-01

Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

Science.gov (United States)

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Using Social Networks to Enhance Teaching and Learning Experiences in Higher Learning Institutions

Science.gov (United States)

Balakrishnan, Vimala

2014-01-01

The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push-Pull-Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study.…

Reliability of redundant structures of nuclear reactor protection systems

International Nuclear Information System (INIS)

Vojnovic, B.

1983-01-01

In this paper, reliability of various redundant structures of PWR protection systems has been analysed. Structures of reactor tip systems as well as the systems for activation of safety devices have been presented. In all those systems redundancy is achieved by means of so called majority voting logic ('r out of n' structures). Different redundant devices have been compared, concerning probability of occurrence of safe as well as unsafe failures. (author)

Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments.

Science.gov (United States)

Fang, Shih-Hau; Lin, Tsung-Nan

2008-11-01

This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

Signal validation in nuclear power plants using redundant measurements

International Nuclear Information System (INIS)

Glockler, O.; Upadhyaya, B.R.; Morgenstern, V.M.

1989-01-01

This paper discusses the basic principles of a multivariable signal validation software system utilizing redundant sensor readings of process variables in nuclear power plants (NPPs). The technique has been tested in numerical experiments, and was applied to actual data from a pressurized water reactor (PWR). The simultaneous checking within one redundant measurement set, and the cross-checking among redundant measurement sets of dissimilar process variables, results in an algorithm capable of detecting and isolating bias-type errors. A case in point occurs when a majority of the direct redundant measurements of more than one process variable has failed simultaneously by a common-mode or correlated failures can be detected by the developed approach. 5 refs

Converging Redundant Sensor Network Information for Improved Building Control

Energy Technology Data Exchange (ETDEWEB)

Dale Tiller; D. Phil; Gregor Henze; Xin Guo

2007-09-30

This project investigated the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point could improve system performance. Phase I of the project focused on instrumentation and data collection. During the initial project phase, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Phase II of the project demonstrated that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. This phase also established that analysis algorithms could be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications. In Phase III of the project, the sensor network from Phase I was complemented by a control strategy developed based on the results from the first two project phases: this controller was implemented in a small sample of work areas, and applied to lighting control. Two additional technologies were developed in the course of completing the project. A prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy was designed and implemented. A new capability that enables occupancy sensors in a sensor network to dynamically set the 'time delay' interval based on ongoing occupant behavior in the space was also designed and implemented.

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

Science.gov (United States)

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…

Verification of failover effects from distributed control system communication networks in digitalized nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Min, Moon Gi; Lee, Jae Ki; Lee, Kwang Hyun; Lee, Dong Il; Lim, Hee Taek [Korea Hydro and Nuclear Power Co., Ltd, Daejeon (Korea, Republic of)

2017-08-15

Distributed Control System (DCS) communication networks, which use Fast Ethernet with redundant networks for the transmission of information, have been installed in digitalized nuclear power plants. Normally, failover tests are performed to verify the reliability of redundant networks during design and manufacturing phases; however, systematic integrity tests of DCS networks cannot be fully performed during these phases because all relevant equipment is not installed completely during these two phases. In additions, practical verification tests are insufficient, and there is a need to test the actual failover function of DCS redundant networks in the target environment. The purpose of this study is to verify that the failover functions works correctly in certain abnormal conditions during installation and commissioning phase and identify the influence of network failover on the entire DCS. To quantify the effects of network failover in the DCS, the packets (Protocol Data Units) must be collected and resource usage of the system has to be monitored and analyzed. This study introduces the use of a new methodology for verification of DCS network failover during the installation and commissioning phases. This study is expected to provide insight into verification methodology and the failover effects from DCS redundant networks. It also provides test results of network performance from DCS network failover in digitalized domestic nuclear power plants (NPPs)

The APS control system network upgrade

International Nuclear Information System (INIS)

Sidorowicz, K. v.; Leibfritz, D.; McDowell, W. P.

1999-01-01

When it was installed,the Advanced Photon Source (APS) control system network was at the state-of-the-art. Different aspects of the system have been reported at previous meetings [1,2]. As loads on the controls network have increased due to newer and faster workstations and front-end computers, we have found performance of the system declining and have implemented an upgraded network. There have been dramatic advances in networking hardware in the last several years. The upgraded APS controls network replaces the original FDDI backbone and shared Ethernet hubs with redundant gigabit uplinks and fully switched 10/100 Ethernet switches with backplane fabrics in excess of 20 Gbits/s (Gbps). The central collapsed backbone FDDI concentrator has been replaced with a Gigabit Ethernet switch with greater than 30 Gbps backplane fabric. Full redundancy of the system has been maintained. This paper will discuss this upgrade and include performance data and performance comparisons with the original network

Networked learning in, for, and with the world

DEFF Research Database (Denmark)

Nørgård, Rikke Toft; Mor, Yishay; Bengtsen, Søren Smedegaard

2018-01-01

With the so-called ‘Mode 3’ university as overarching framework (Barnett, 2004; Bengtsen & Nørgård, 2016; Barnett & Bengtsen, 2017; Nørgård, Olesen & Toft-Nielsen, 2018) this chapter considers how traditional forms of and formats for teaching and learning within higher education can be rethought,......’ in higher education. In the following sections, we will describe these transformations of university being, before considering some of the new challenges, opportunities, and potentials of teaching and learning in and through hybrid networks in the Mode 3 institution......., opportunities, and potentials to the teaching and learning that takes place at the university. Through history, and across different present national contexts and cultures, the ‘being’ of the university and its livelihood and mandate has changed (Wright, 2016; Barnett, 2018). Through these transformations where......, reconfigured, and redesigned in order to facilitate valuable, meaningful and relevant hybrid networked learning in, for, and with the world. What it means to ‘be’ a university is changing and the university is a ‘being’ that in itself is changing (Barnett, 2011), something also offering challenges...

Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning

Directory of Open Access Journals (Sweden)

Guangyi Liu

2014-01-01

Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

RELATION BETWEEN COOPERATION AND ORGANIZATIONAL LEARNING WITH THE COMPETITIVENESS IN AN INTERORGANIZATIONAL NETWORK

Directory of Open Access Journals (Sweden)

Paulo Cesar Zonta

2015-05-01

Full Text Available The study analyzed the relationship between cooperation and organizational learning with competitiveness in a small and medium enterprises (SME network, with business of the groups of the Commercial and Industrial Association of Chapecó (ACIC. The methodology used was quantitative, with the factorial analysis. Currently, ACIC has 14 groups and 236 SME´s nucleated, developing joint activities of economic and social sustainability in Chapecó. The theoretical study raised concepts already endorsed by the scientific community on interorganizational networks, competitiveness, cooperation and organizational learning. The results demonstrated that indicators related to cooperation and learning in horizontal networks are characterized as antecedents of competitiveness in organizational networks, and that there is a positive correlation between the constructs cooperation and organizational learning with competitiveness construct. The study confirms the belief that small businesses associated in networks can increase their competitiveness, thus contributing to regional development.

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

Science.gov (United States)

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…

Social Networks: Rational Learning and Information Aggregation

Science.gov (United States)

2009-09-01

predecessor, Gale and Kariv (2003) who generalize the payoff equalization result of Bala and Goyal (1998) in connected social networks (discussed below...requires more notation. Using Bayes’ Rule and the assumption of equal priors on the state θ, we have that the social belief given by observing... Social Networks: Rational Learning and Information Aggregation by Ilan Lobel B.Sc., Pontif́ıcia Universidade Católica do Rio de Janeiro (2004

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

2015-01-01

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

Stochastic Online Learning in Dynamic Networks under Unknown Models

Science.gov (United States)

2016-08-02

The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

Science.gov (United States)

Noor, Ahmed K. (Compiler)

2003-01-01

An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

Language Learning through Social Networks: Perceptions and Reality

Science.gov (United States)

Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert

2016-01-01

Language Learning Social Network Sites (LLSNSs) have attracted millions of users around the world. However, little is known about how people participate in these sites and what they learn from them. This study investigated learners' attitudes, usage, and progress in a major LLSNS through a survey of 4,174 as well as 20 individual case studies. The…

Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

Science.gov (United States)

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

2017-01-01

Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Directory of Open Access Journals (Sweden)

Zou Xiaotao

2009-01-01

Full Text Available Abstract A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Directory of Open Access Journals (Sweden)

Xiaotao Zou

2009-01-01

Full Text Available A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

Mobility and Position Error Analysis of a Complex Planar Mechanism with Redundant Constraints

Science.gov (United States)

Sun, Qipeng; Li, Gangyan

2018-03-01

Nowadays mechanisms with redundant constraints have been created and attracted much attention for their merits. The mechanism of the redundant constraints in a mechanical system is analyzed in this paper. A analysis method of Planar Linkage with a repetitive structure is proposed to get the number and type of constraints. According to the difference of applications and constraint characteristics, the redundant constraints are divided into the theoretical planar redundant constraints and the space-planar redundant constraints. And the calculation formula for the number of redundant constraints and type of judging method are carried out. And a complex mechanism with redundant constraints is analyzed of the influence about redundant constraints on mechanical performance. With the combination of theoretical derivation and simulation research, a mechanism analysis method is put forward about the position error of complex mechanism with redundant constraints. It points out the direction on how to eliminate or reduce the influence of redundant constraints.

Analog memristive synapse in spiking networks implementing unsupervised learning

Directory of Open Access Journals (Sweden)

Erika Covi

2016-10-01

Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

Science.gov (United States)

Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

2016-01-01

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring

NARCIS (Netherlands)

Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter

2009-01-01

Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning

Informal Learning and Identity Formation in Online Social Networks

Science.gov (United States)

Greenhow, Christine; Robelia, Beth

2009-01-01

All students today are increasingly expected to develop technological fluency, digital citizenship, and other twenty-first century competencies despite wide variability in the quality of learning opportunities schools provide. Social network sites (SNSs) available via the internet may provide promising contexts for learning to supplement…

Interaction control of a redundant mobile manipulator

International Nuclear Information System (INIS)

Chung, J.H.; Velinsky, S.A.; Hess, R.A.

1998-01-01

This paper discusses the modeling and control of a spatial mobile manipulator that consists of a robotic manipulator mounted on a wheeled mobile platform. The Lagrange-d'Alembert formulation is used to obtain a concise description of the dynamics of the system, which is subject to nonholonomic constraints. The complexity of the model is increased by introducing kinematic redundancy, which is created when a multilinked manipulator is used. The kinematic redundancy is resolved by decomposing the mobile manipulator into two subsystems: the mobile platform and the manipulator. The redundancy resolution scheme employs a nonlinear interaction-control algorithm, which is developed and applied to coordinate the two subsystems' controllers. The subsystem controllers are independently designed, based on each subsystem's dynamic characteristics. Simulation results show the promise of the developed algorithm

Adaptive competitive learning neural networks

Directory of Open Access Journals (Sweden)

Ahmed R. Abas

2013-11-01

Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

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

Science.gov (United States)

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

Application study of EPICS-based redundant method for reactor control system

International Nuclear Information System (INIS)

Zhang Ning; Han Lifeng; Chen Yongzhong; Guo Bing; Yin Congcong

2013-01-01

In the reactor control system prototype development of TMSR (Thorium Molten Salt Reactor system, CAS) project, EPICS (Experimental Physics and Industrial Control System) is adopted as Instrument and Control software platform. For the aim of IOC (Input/Output Controller) redundancy and data synchronization of the system, the EPICS-based RMT (Redundancy Monitor Task ) software package and its data-synchronization component CCE (Continuous Control Executive) were introduced. By the development of related IOC driver, redundant switch-over control of server IOC was implemented. The method of redundancy implementation using RMT in server and redundancy performance test for power control system are discussed in this paper. (authors)

Joint optimization of redundancy level and spare part inventories

International Nuclear Information System (INIS)

Sleptchenko, Andrei; Heijden, Matthieu van der

2016-01-01

We consider a “k-out-of-N” system with different standby modes. Each of the N components consists of multiple part types. Upon failure, a component can be repaired within a certain time by switching the failed part by a spare, if available. We develop both an exact and a fast approximate analysis to compute the system availability. Next, we jointly optimize the component redundancy level with the inventories of the various spare parts. We find that our approximations are very accurate and suitable for large systems. We apply our model to a case study at a public organization in Qatar, and find that we can improve the availability-to-cost ratio by reducing the redundancy level and increasing the spare part inventories. In general, high redundancy levels appear to be useful only when components are relatively cheap and part replacement times are high. - Highlights: • We analyze a redundant system (k-out-of-N) with multiple parts and spares. • We jointly optimize the redundancy level and the spare part inventories. • We develop an exact method and an approximation to evaluate the system availability. • Adding spare parts and reducing the redundancy level cuts cost by 50% in a case study. • The availability is not very sensitive to the shape of the failure time distribution.

Learning automaton newtork and its dynamics. Gakushu automaton network to sono dynamics

Energy Technology Data Exchange (ETDEWEB)

Quan, F [Hiroshima-Denki Institute of Technology, Hiroshima (Jpaan); Unno, F; Hirata, H [Chiba Univ., Chiba (Japan)

1991-10-20

In order to construct a distributed processing system having learning automata as autonomous elements, a reinforcement learning network of the automaton is proposed and it{prime}s dynamics is investigated. In this paper, it is attempted to add another level of meaning to computational cooperativity by using a reinforcement learning network with generalized leaning automata. The collection of learning automata in the team situation acts as self-interested agents that work toward improving their performance with respect to their individual preference ordering. In the global state space of the network, the case of partially synchronous stochastic process is considered. In this case, the existence of mean field is shown and a reinforcement learning algorithm which can make the dynamics on the average reinforcement trajectory is presented. This algorithm is shown to have a high convergence speed as a result of a simple experiment. 14 refs., 9 figs.

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

Science.gov (United States)

Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

Decoupled Sliding Mode Control for a Novel 3-DOF Parallel Manipulator with Actuation Redundancy

Directory of Open Access Journals (Sweden)

Niu Xuemei

2015-05-01

Full Text Available This paper presents a decoupled nonsingular terminal sliding mode controller (DNTSMC for a novel 3-DOF parallel manipulator with actuation redundancy. According to kinematic analysis, the inverse dynamic model for a novel 3-DOF redundantly actuated parallel manipulator is formulated in the task space using Lagrangian formalism and decoupled into three entirely independent subsystems under generalized coordinates to significantly reduce system complexity. Based on the dynamic model, a decoupled sliding mode control strategy is proposed for the parallel manipulator; the idea behind this strategy is to design a nonsingular terminal sliding mode controller for each subsystem, which can drive states of three subsystems to the original equilibrium points simultaneously by two intermediate variables. Additionally, a RBF neural network is used to compensate the cross-coupling force and gravity to enhance the control precision. Simulation and experimental results show that the proposed DNTSMC can achieve better control performances compared with the conventional sliding mode controller (SMC and the DNTSMC without compensator.

Machine Learning for ATLAS DDM Network Metrics

CERN Document Server

Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

2016-01-01

The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

Do technologies have politics? The new paradigm and pedagogy in networked learning

OpenAIRE

Jones, Chris

2001-01-01

This paper explores the relationships between the technologies deployed in networked and e-Learning and the pedagogies and politics associated with them. Networked learning and the related move to e-Learning are coincident with the globalisation, commodification and massification of Higher Education. It examines the hard and soft forms of technological determinism (TD) found in the current advocacy of technological futures for Higher Education. Hard TD claims that new technologies bring about...

Redundant arrays of IDE drives

Energy Technology Data Exchange (ETDEWEB)

D.A. Sanders et al.

2002-01-02

The authors report tests of redundant arrays of IDE disk drives for use in offline high energy physics data analysis. Parts costs of total systems using commodity EIDE disks are now at the $4000 per Terabyte level. Disk storage prices have now decreased to the point where they equal the cost per Terabyte of Storage Technology tape silos. The disks, however, offer far better granularity; even small institutions can afford to deploy systems. The tests include reports on software RAID-5 systems running under Linux 2.4 using Promise Ultra 100{trademark} disk controllers. RAID-5 protects data in case of a single disk failure by providing parity bits. Tape backup is not required. Journaling file systems are used to allow rapid recovery from crashes. The data analysis strategy is to encapsulate data and CPU processing power. Analysis for a particular part of a data set takes place on the PC where the data resides. The network is only used to put results together. They explore three methods of moving data between sites; internet transfers, not pluggable IDE disks in FireWire cases, and DVD-R disks.

Redundant arrays of IDE drives

International Nuclear Information System (INIS)

Sanders, D.A.

2002-01-01

The authors report tests of redundant arrays of IDE disk drives for use in offline high energy physics data analysis. Parts costs of total systems using commodity EIDE disks are now at the $4000 per Terabyte level. Disk storage prices have now decreased to the point where they equal the cost per Terabyte of Storage Technology tape silos. The disks, however, offer far better granularity; even small institutions can afford to deploy systems. The tests include reports on software RAID-5 systems running under Linux 2.4 using Promise Ultra 100trademark disk controllers. RAID-5 protects data in case of a single disk failure by providing parity bits. Tape backup is not required. Journaling file systems are used to allow rapid recovery from crashes. The data analysis strategy is to encapsulate data and CPU processing power. Analysis for a particular part of a data set takes place on the PC where the data resides. The network is only used to put results together. They explore three methods of moving data between sites; internet transfers, not pluggable IDE disks in FireWire cases, and DVD-R disks

What Online Networks Offer: "Online Network Compositions and Online Learning Experiences of Three Ethnic Groups"

Science.gov (United States)

Lecluijze, Suzanne Elisabeth; de Haan, Mariëtte; Ünlüsoy, Asli

2015-01-01

This exploratory study examines ethno-cultural diversity in youth's narratives regarding their "online" learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents this study compared the…

Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

Science.gov (United States)

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…

Working memory capacity and redundant information processing efficiency.

Science.gov (United States)

Endres, Michael J; Houpt, Joseph W; Donkin, Chris; Finn, Peter R

2015-01-01

Working memory capacity (WMC) is typically measured by the amount of task-relevant information an individual can keep in mind while resisting distraction or interference from task-irrelevant information. The current research investigated the extent to which differences in WMC were associated with performance on a novel redundant memory probes (RMP) task that systematically varied the amount of to-be-remembered (targets) and to-be-ignored (distractor) information. The RMP task was designed to both facilitate and inhibit working memory search processes, as evidenced by differences in accuracy, response time, and Linear Ballistic Accumulator (LBA) model estimates of information processing efficiency. Participants (N = 170) completed standard intelligence tests and dual-span WMC tasks, along with the RMP task. As expected, accuracy, response-time, and LBA model results indicated memory search and retrieval processes were facilitated under redundant-target conditions, but also inhibited under mixed target/distractor and redundant-distractor conditions. Repeated measures analyses also indicated that, while individuals classified as high (n = 85) and low (n = 85) WMC did not differ in the magnitude of redundancy effects, groups did differ in the efficiency of memory search and retrieval processes overall. Results suggest that redundant information reliably facilitates and inhibits the efficiency or speed of working memory search, and these effects are independent of more general limits and individual differences in the capacity or space of working memory.

Energy consumption analysis for various memristive networks under different learning strategies

Energy Technology Data Exchange (ETDEWEB)

Deng, Lei; Wang, Dong [Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084 (China); Zhang, Ziyang; Tang, Pei [Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084 (China); Li, Guoqi, E-mail: liguoqi@mail.tsinghua.edu.cn [Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084 (China); Pei, Jing, E-mail: peij@mail.tsinghua.edu.cn [Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084 (China); Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084 (China)

2016-02-22

Highlights: • Estimation methodology for energy consumed by memristor is established. • Energy comparisons for different learning strategies in various networks are touched. • Less-pulses and low-power-first modulation methods are energy efficient. • Proper decreasing the memristor modulation precision reduces the energy consumption. • Helpful solutions for power improving in memristive systems are proposed. - Abstract: Recently, various memristive systems emerge to emulate the efficient computing paradigm of the brain cortex; whereas, how to make them energy efficient still remains unclear, especially from an overall perspective. Here, a systematical and bottom-up energy consumption analysis is demonstrated, including the memristor device level and the network learning level. We propose an energy estimating methodology when modulating the memristive synapses, which is simulated in three typical neural networks with different synaptic structures and learning strategies for both offline and online learning. These results provide an in-depth insight to create energy efficient brain-inspired neuromorphic devices in the future.

Energy consumption analysis for various memristive networks under different learning strategies

International Nuclear Information System (INIS)

Deng, Lei; Wang, Dong; Zhang, Ziyang; Tang, Pei; Li, Guoqi; Pei, Jing

2016-01-01

Highlights: • Estimation methodology for energy consumed by memristor is established. • Energy comparisons for different learning strategies in various networks are touched. • Less-pulses and low-power-first modulation methods are energy efficient. • Proper decreasing the memristor modulation precision reduces the energy consumption. • Helpful solutions for power improving in memristive systems are proposed. - Abstract: Recently, various memristive systems emerge to emulate the efficient computing paradigm of the brain cortex; whereas, how to make them energy efficient still remains unclear, especially from an overall perspective. Here, a systematical and bottom-up energy consumption analysis is demonstrated, including the memristor device level and the network learning level. We propose an energy estimating methodology when modulating the memristive synapses, which is simulated in three typical neural networks with different synaptic structures and learning strategies for both offline and online learning. These results provide an in-depth insight to create energy efficient brain-inspired neuromorphic devices in the future.

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

Science.gov (United States)

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.

REDUNDANT ARRAY CONFIGURATIONS FOR 21 cm COSMOLOGY

Energy Technology Data Exchange (ETDEWEB)

Dillon, Joshua S.; Parsons, Aaron R., E-mail: jsdillon@berkeley.edu [Department of Astronomy, UC Berkeley, Berkeley, CA (United States)

2016-08-01

Realizing the potential of 21 cm tomography to statistically probe the intergalactic medium before and during the Epoch of Reionization requires large telescopes and precise control of systematics. Next-generation telescopes are now being designed and built to meet these challenges, drawing lessons from first-generation experiments that showed the benefits of densely packed, highly redundant arrays—in which the same mode on the sky is sampled by many antenna pairs—for achieving high sensitivity, precise calibration, and robust foreground mitigation. In this work, we focus on the Hydrogen Epoch of Reionization Array (HERA) as an interferometer with a dense, redundant core designed following these lessons to be optimized for 21 cm cosmology. We show how modestly supplementing or modifying a compact design like HERA’s can still deliver high sensitivity while enhancing strategies for calibration and foreground mitigation. In particular, we compare the imaging capability of several array configurations, both instantaneously (to address instrumental and ionospheric effects) and with rotation synthesis (for foreground removal). We also examine the effects that configuration has on calibratability using instantaneous redundancy. We find that improved imaging with sub-aperture sampling via “off-grid” antennas and increased angular resolution via far-flung “outrigger” antennas is possible with a redundantly calibratable array configuration.

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

Energy Technology Data Exchange (ETDEWEB)

Chen, Barry Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-11-29

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 patterns 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.

Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks.

Directory of Open Access Journals (Sweden)

Marie-Therese Kuhnert

Full Text Available Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.

Machine learning for network-based malware detection

DEFF Research Database (Denmark)

Stevanovic, Matija

and based on different, mutually complementary, principles of traffic analysis. The proposed approaches rely on machine learning algorithms (MLAs) for automated and resource-efficient identification of the patterns of malicious network traffic. We evaluated the proposed methods through extensive evaluations...

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

Science.gov (United States)

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.

‘Living' theory: a pedagogical framework for process support in networked learning

Directory of Open Access Journals (Sweden)

Philipa Levy

2006-12-01

Full Text Available This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case-study analysis of learners' experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case-study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes–orientation, communication, socialisation and organisation–that were associated with ‘learning to learn' in the course's networked environment, and offers a flavour of participants' experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process-focused (as well as domain-focused learning tasks.

Analysis and Design of Offset QPSK Using Redundant Filter Banks

International Nuclear Information System (INIS)

Fernandez-Vazquez, Alfonso; Jovanovic-Dolecek, Gordana

2013-01-01

This paper considers the analysis and design of OQPSK digital modulation. We first establish the discrete time formulation, which allows us to find the equivalent redundant filter banks. It is well known that redundant filter banks are related with redundant transformation of the Frame theory. According to the Frame theory, the redundant transformations and corresponding representations are not unique. In this way, we show that the solution to the pulse shaping problem is not unique. Then we use this property to minimize the effect of the channel noise in the reconstructed symbol stream. We evaluate the performance of the digital communication using numerical examples.

Innovating Design for Learning in the Networked Society

DEFF Research Database (Denmark)

Levinsen, Karin Tweddell; Nielsen, Janni

2012-01-01

The transition from the industrial to the knowledge or networked society has, together with the worldwide digitalization and e-permeation of our social, political and economic lives, brought challenges to the educational systems. The changes call for new key competences in terms of self-initiated......The transition from the industrial to the knowledge or networked society has, together with the worldwide digitalization and e-permeation of our social, political and economic lives, brought challenges to the educational systems. The changes call for new key competences in terms of self......-initiated and lifelong learning and digital literacy. At the same time, the implementation of new public management in educational institutions put pressure on students’ available time for studying and the qualitative outcome of learning processes. These conditions give birth to emerging tensions at the organizational...... in their practice are students who are (if at all) only familiar with the curriculum at a surface level and who expect the teachers to present digested versions of the curriculum. This chapter presents a design for teaching and learning approach in the shape of a design for learning model that aims to scaffold...

Learning a Markov Logic network for supervised gene regulatory network inference.

Science.gov (United States)

Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

Redundancy for electric motors in spacecraft applications

Science.gov (United States)

Smith, Robert J.; Flew, Alastair R.

1986-01-01

The parts of electric motors which should be duplicated in order to provide maximum reliability in spacecraft application are identified. Various common types of redundancy are described. The advantages and disadvantages of each are noted. The principal types are illustrated by reference to specific examples. For each example, constructional details, basic performance data and failure modes are described, together with a discussion of the suitability of particular redundancy techniques to motor types.

Dynamic Control of Kinematically Redundant Robotic Manipulators

Directory of Open Access Journals (Sweden)

Erling Lunde

1987-07-01

Full Text Available Several methods for task space control of kinematically redundant manipulators have been proposed in the literature. Most of these methods are based on a kinematic analysis of the manipulator. In this paper we propose a control algorithm in which we are especially concerned with the manipulator dynamics. The algorithm is particularly well suited for the class of redundant manipulators consisting of a relatively small manipulator mounted on a larger positioning part.

Study of redundant Models in reliability prediction of HXMT's HES

International Nuclear Information System (INIS)

Wang Jinming; Liu Congzhan; Zhang Zhi; Ji Jianfeng

2010-01-01

Two redundant equipment structures of HXMT's HES are proposed firstly, the block backup and dual system cold-redundancy. Then prediction of the reliability is made by using parts count method. Research of comparison and analysis is also performed on the two proposals. A conclusion is drawn that a higher reliability and longer service life could be offered by taking a redundant equipment structure of block backup. (authors)

3D seismic denoising based on a low-redundancy curvelet transform

International Nuclear Information System (INIS)

Cao, Jingjie; Zhao, Jingtao; Hu, Zhiying

2015-01-01

Contamination of seismic signal with noise is one of the main challenges during seismic data processing. Several methods exist for eliminating different types of noises, but optimal random noise attenuation remains difficult. Based on multi-scale, multi-directional locality of curvelet transform, the curvelet thresholding method is a relatively new method for random noise elimination. However, the high redundancy of a 3D curvelet transform makes its computational time and memory for massive data processing costly. To improve the efficiency of the curvelet thresholding denoising, a low-redundancy curvelet transform was introduced. The redundancy of the low-redundancy curvelet transform is approximately one-quarter of the original transform and the tightness of the original transform is also kept, thus the low-redundancy curvelet transform calls for less memory and computational resource compared with the original one. Numerical results on 3D synthetic and field data demonstrate that the low-redundancy curvelet denoising consumes one-quarter of the CPU time compared with the original curvelet transform using iterative thresholding denoising when comparable results are obtained. Thus, the low-redundancy curvelet transform is a good candidate for massive seismic denoising. (paper)

Networks and learning: communities, practices and the metaphor of networks–a commentary

Directory of Open Access Journals (Sweden)

Bruce Ingraham

2004-12-01

Full Text Available In issue 12(1, Jones (2004 in his article ‘Networks and learning: communities, practices and the metaphor of networks' sets out to address three inter-related sets of issues: … firstly that learning technology needs to take account of the wider debate about networks and secondly that research in this field needs to address the theoretical and practical issues raised by advances in the field of networks. A third point is that the idea of the network acts as a powerful metaphor even if we are able to discount any particular theory generated in its support. The network metaphor can act as a unifying concept allowing us to bring together apparently disparate elements of the field.

Bifurcation and category learning in network models of oscillating cortex

Science.gov (United States)

Baird, Bill

1990-06-01

A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

The cellular robustness by genetic redundancy in budding yeast.

Directory of Open Access Journals (Sweden)

Jingjing Li

2010-11-01

Full Text Available The frequent dispensability of duplicated genes in budding yeast is heralded as a hallmark of genetic robustness contributed by genetic redundancy. However, theoretical predictions suggest such backup by redundancy is evolutionarily unstable, and the extent of genetic robustness contributed from redundancy remains controversial. It is anticipated that, to achieve mutual buffering, the duplicated paralogs must at least share some functional overlap. However, counter-intuitively, several recent studies reported little functional redundancy between these buffering duplicates. The large yeast genetic interactions released recently allowed us to address these issues on a genome-wide scale. We herein characterized the synthetic genetic interactions for ∼500 pairs of yeast duplicated genes originated from either whole-genome duplication (WGD or small-scale duplication (SSD events. We established that functional redundancy between duplicates is a pre-requisite and thus is highly predictive of their backup capacity. This observation was particularly pronounced with the use of a newly introduced metric in scoring functional overlap between paralogs on the basis of gene ontology annotations. Even though mutual buffering was observed to be prevalent among duplicated genes, we showed that the observed backup capacity is largely an evolutionarily transient state. The loss of backup capacity generally follows a neutral mode, with the buffering strength decreasing in proportion to divergence time, and the vast majority of the paralogs have already lost their backup capacity. These observations validated previous theoretic predictions about instability of genetic redundancy. However, departing from the general neutral mode, intriguingly, our analysis revealed the presence of natural selection in stabilizing functional overlap between SSD pairs. These selected pairs, both WGD and SSD, tend to have decelerated functional evolution, have higher propensities of co

Redundant nerve roots of the cauda equina : MR findings

International Nuclear Information System (INIS)

Oh, Kyu Hyen; Lee, Jung Man; Jung, Hak Young; Lee, Young Hwan; Sung, Nak Kwan; Chung, Duck Soo; Kim, Ok Dong; Lee, Sang Kwon; Suh, Kyung Jin

1997-01-01

To evaluate MR findings of redundant nerve roots (RNR) of the cauda equina. 17 patients with RNR were studied; eight were men and nine were women, and their ages ranged from 46 to 82 (mean 63) years. Diagroses were established on the basis of T2-weighted sagittal and coronal MRI, which showed a tortuous or coiled configuration of the nerve roots of the cauda equina. MR findings were reviewed for location, magnitude, and signal intensity of redundant nerve roots, and the relationship between magnitude of redundancy and severity of lumbar spinal canal stenosis (LSCS) was evaluated. In all 17 patients, MR showed moderate or severe LSCS caused by herniation or bulging of an intervertebral disc, osteophyte from the vertebral body or facet joint, thickening of the ligamentum flavum, degenerative spondylolisthesis, or a combination of these. T2-weighted sagittal and coronal MR images well clearly showed the location of RNR of the cauda equina;in 16 patients(94%), these were seen above the level of constriction of the spinal canal, and in one case, they were observed below the level of constriction. T2-weighted axial images showed the thecal sac filled with numerous nerve roots. The magnitude of RNR was mild in six cases (35%), moderate in five cases (30%), and severe in six cases (35%). Compared with normal nerve roots, the RNR signal on T2-weighted images was iso-intense. All patients with severe redundancy showed severe LSCS, but not all cases with severe LSCS showed severe redundancy. Redundant nerve roots of cauda equina were seen in relatively older patients with moderate or severe LSCS and T2-weighted MR images were accurate in identifying redundancy of nerve roots and evaluating their magnitude and location

Improving Accessibility for Seniors in a Life-Long Learning Network: A Usability Study of Learning Websites

Science.gov (United States)

Gu, Xiaoqing; Ding, Rui; Fu, Shirong

2011-01-01

Senior citizens are comparatively vulnerable in accessing learning opportunities offered on the Internet due to usability problems in current web design. In an effort to build a senior-friendly learning web as a part of the Life-long Learning Network in Shanghai, usability studies of two websites currently available to Shanghai senior citizens…

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

Science.gov (United States)

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.

SME Innovation and Learning: The Role of Networks and Crisis Events

Science.gov (United States)

Saunders, Mark N. K.; Gray, David E; Goregaokar, Harshita

2014-01-01

Purpose: The purpose of this paper is to contribute to the literature on innovation and entrepreneurial learning by exploring how SMEs learn and innovate, how they use both formal and informal learning and in particular the role of networks and crisis events within their learning experience. Design/methodology/approach: Mixed method study,…

Lifelong learning of human actions with deep neural network self-organization.

Science.gov (United States)

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Learning to trust : network effects through time.

NARCIS (Netherlands)

Barrera, D.; Bunt, G. van de

2009-01-01

This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

Learning to trust: network effects through time

NARCIS (Netherlands)

Barrera, D.; van de Bunt, G

2009-01-01

This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

Inter-firm Networks, Organizational Learning and Knowledge Updating: An Empirical Study

Science.gov (United States)

Zhang, Su-rong; Wang, Wen-ping

In the era of knowledge-based economy which information technology develops rapidly, the rate of knowledge updating has become a critical factor for enterprises to gaining competitive advantage .We build an interactional theoretical model among inter-firm networks, organizational learning and knowledge updating thereby and demonstrate it with empirical study at last. The result shows that inter-firm networks and organizational learning is the source of knowledge updating.

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

Science.gov (United States)

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.

Compliant behaviour of redundant robot arm - experiments with null-space

Directory of Open Access Journals (Sweden)

Petrović Petar B.

2015-01-01

Full Text Available This paper presents theoretical and experimental aspects of Jacobian nullspace use in kinematically redundant robots for achieving kinetostatically consistent control of their compliant behavior. When the stiffness of the robot endpoint is dominantly influenced by the compliance of the robot joints, generalized stiffness matrix can be mapped into joint space using appropriate congruent transformation. Actuation stiffness matrix achieved by this transformation is generally nondiagonal. Off-diagonal elements of the actuation matrix can be generated by redundant actuation only (polyarticular actuators, but such kind of actuation is very difficult to realize practically in technical systems. The approach of solving this problem which is proposed in this paper is based on the use of kinematic redundancy and nullspace of the Jacobian matrix. Evaluation of the developed analytical model was done numerically by a minimal redundant robot with one redundant d.o.f. and experimentally by a 7 d.o.f. Yaskawa SIA 10F robot arm. [Projekat Ministarstva nauke Republike Srbije, br. TR35007

Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

Science.gov (United States)

Manning, Christin

2013-01-01

Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

Statistical learning problem of artificial neural network to control roofing process

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Lapidus Azariy

2017-01-01

Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.

Advanced Learning Technologies and Learning Networks and Their Impact on Future Aerospace Workforce

Science.gov (United States)

Noor, Ahmed K. (Compiler)

2003-01-01

This document contains the proceedings of the training workshop on Advanced Learning Technologies and Learning Networks and their impact on Future Aerospace Workforce. The workshop was held at the Peninsula Workforce Development Center, Hampton, Virginia, April 2 3, 2003. The workshop was jointly sponsored by Old Dominion University and NASA. Workshop attendees came from NASA, other government agencies, industry, and universities. The objectives of the workshop were to: 1) provide broad overviews of the diverse activities related to advanced learning technologies and learning environments, and 2) identify future directions for research that have high potential for aerospace workforce development. Eighteen half-hour overviewtype presentations were made at the workshop.

Energy consumption analysis for various memristive networks under different learning strategies

Science.gov (United States)

Deng, Lei; Wang, Dong; Zhang, Ziyang; Tang, Pei; Li, Guoqi; Pei, Jing

2016-02-01

Recently, various memristive systems emerge to emulate the efficient computing paradigm of the brain cortex; whereas, how to make them energy efficient still remains unclear, especially from an overall perspective. Here, a systematical and bottom-up energy consumption analysis is demonstrated, including the memristor device level and the network learning level. We propose an energy estimating methodology when modulating the memristive synapses, which is simulated in three typical neural networks with different synaptic structures and learning strategies for both offline and online learning. These results provide an in-depth insight to create energy efficient brain-inspired neuromorphic devices in the future.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Celebrating the Tenth Networked Learning Conference: Looking Back and Moving Forward

DEFF Research Database (Denmark)

de Laat, Maarten; Ryberg, Thomas

2018-01-01

, actor network theory), learning environments and social media (e.g. LMS, MOOC, Virtual Worlds, Twitter, Facebook), technologies (e.g. phone, laptop, tablet), methodology (e.g. quantitative, qualitative) and related research in the domain of e-learning (e-learning, CSCL, TEL). The findings are placed...

Personal Profiles: Enhancing Social Interaction in Learning Networks

NARCIS (Netherlands)

Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

2009-01-01

Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

Machine learning using a higher order correlation network

Energy Technology Data Exchange (ETDEWEB)

Lee, Y.C.; Doolen, G.; Chen, H.H.; Sun, G.Z.; Maxwell, T.; Lee, H.Y.

1986-01-01

A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

Overcoming uncertainty for within-network relational machine learning

OpenAIRE

Pfeiffer, Joseph J.

2015-01-01

People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in...

Home and away : learning in and learning from organisational networks in Europe

NARCIS (Netherlands)

Docherty, P.; Huzzard, T.; Leede, J. de

2003-01-01

This report is a comparative analysis of the various learning networks established within the Innoflex Project. The report recaps on the central argument underpinning Innoflex, namely that traditional ways of organising workplaces and traditional styles of management cannot achieve the commitment,

Practical, redundant, failure-tolerant, self-reconfiguring embedded system architecture

Science.gov (United States)

Klarer, Paul R.; Hayward, David R.; Amai, Wendy A.

2006-10-03

This invention relates to system architectures, specifically failure-tolerant and self-reconfiguring embedded system architectures. The invention provides both a method and architecture for redundancy. There can be redundancy in both software and hardware for multiple levels of redundancy. The invention provides a self-reconfiguring architecture for activating redundant modules whenever other modules fail. The architecture comprises: a communication backbone connected to two or more processors and software modules running on each of the processors. Each software module runs on one processor and resides on one or more of the other processors to be available as a backup module in the event of failure. Each module and backup module reports its status over the communication backbone. If a primary module does not report, its backup module takes over its function. If the primary module becomes available again, the backup module returns to its backup status.

EFFICIENCY OF REDUNDANT QUERY EXECUTION IN MULTI-CHANNEL SERVICE SYSTEMS

Directory of Open Access Journals (Sweden)

V. A. Bogatyrev

2016-03-01

Full Text Available Subject of Research.The paper deals with analysis of the effectiveness of redundant queries based on untrusted computing in computer systems, represented by multi-channel queuing systems with a common queue. The objective of research is the possibility of increasing the efficiency of service requests while performing redundant copies of requests in different devices of a multi-channel system under conditions of calculations unreliability. The redundant service of requests requires the infallibility of its implementation at least in one of the devices.Method. We have considered estimation of the average time spent in the system with and without the use of redundant requests at the presentation of a simple queuing model of the M / M / n type to analyze the effectiveness of redundant service of requests. Presented evaluation of the average waiting time in the redundant queries is the upper one, since it ignores the possibility of reducing the average waiting time as a result of the spread of the probability of time querying at different devices. The integrated efficiency of redundant service of requests is defined based on the multiplicative index that takes into account the infallibility of calculations and the average time allowance with respect to the maximum tolerated delay of service. Evaluation of error-free computing at reserved queries is received at the requirement of faultless execution of at least one copy of the request. Main Results. We have shown that the reservation of requests gives the gain in efficiency of the system at low demand rate (load. We have defined the boundaries of expediency (efficiency for redundant service of requests. We have shown the possibility of the effectiveness increasing of the adaptive changes in the multiplicity of the reservation of requests, depending on the intensity of the flow of requests. We have found out that the choice of service discipline in information service systems is largely determined by

Social Software: Participants' Experience Using Social Networking for Learning

Science.gov (United States)

Batchelder, Cecil W.

2010-01-01

Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

Aids to Computer-Based Multimedia Learning.

Science.gov (United States)

Mayer, Richard E.; Moreno, Roxana

2002-01-01

Presents a cognitive theory of multimedia learning that draws on dual coding theory, cognitive load theory, and constructivist learning theory and derives some principles of instructional design for fostering multimedia learning. These include principles of multiple representation, contiguity, coherence, modality, and redundancy. (SLD)

Palpebral redundancy from hypothyroidism.

Science.gov (United States)

Wortsman, J; Wavak, P

1980-01-01

A patient is described with disabling palpebral edema. Primary hypothyroidism had been previously diagnosed and treated. Testing of thyroid function revealed persistence of the hypothyroidism. Treatment with L-thyroxine produced normalization of the biochemical parameters and resolution of palpebral edema. The search for hypothyrodism in patients with palpebral redundancy is emphasized.

Deep learning classification in asteroseismology using an improved neural network

DEFF Research Database (Denmark)

Hon, Marc; Stello, Dennis; Yu, Jie

2018-01-01

Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic...... frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower...

Deep learning with convolutional neural network in radiology.

Science.gov (United States)

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Bayesian network learning for natural hazard assessments

Science.gov (United States)

Vogel, Kristin

2016-04-01

Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

Exploring the Peer Interaction Effects on Learning Achievement in a Social Learning Platform Based on Social Network Analysis

Science.gov (United States)

Lin, Yu-Tzu; Chen, Ming-Puu; Chang, Chia-Hu; Chang, Pu-Chen

2017-01-01

The benefits of social learning have been recognized by existing research. To explore knowledge distribution in social learning and its effects on learning achievement, we developed a social learning platform and explored students' behaviors of peer interactions by the proposed algorithms based on social network analysis. An empirical study was…

Parameter identifiability and redundancy: theoretical considerations.

Directory of Open Access Journals (Sweden)

Mark P Little

Full Text Available BACKGROUND: Models for complex biological systems may involve a large number of parameters. It may well be that some of these parameters cannot be derived from observed data via regression techniques. Such parameters are said to be unidentifiable, the remaining parameters being identifiable. Closely related to this idea is that of redundancy, that a set of parameters can be expressed in terms of some smaller set. Before data is analysed it is critical to determine which model parameters are identifiable or redundant to avoid ill-defined and poorly convergent regression. METHODOLOGY/PRINCIPAL FINDINGS: In this paper we outline general considerations on parameter identifiability, and introduce the notion of weak local identifiability and gradient weak local identifiability. These are based on local properties of the likelihood, in particular the rank of the Hessian matrix. We relate these to the notions of parameter identifiability and redundancy previously introduced by Rothenberg (Econometrica 39 (1971 577-591 and Catchpole and Morgan (Biometrika 84 (1997 187-196. Within the widely used exponential family, parameter irredundancy, local identifiability, gradient weak local identifiability and weak local identifiability are shown to be largely equivalent. We consider applications to a recently developed class of cancer models of Little and Wright (Math Biosciences 183 (2003 111-134 and Little et al. (J Theoret Biol 254 (2008 229-238 that generalize a large number of other recently used quasi-biological cancer models. CONCLUSIONS/SIGNIFICANCE: We have shown that the previously developed concepts of parameter local identifiability and redundancy are closely related to the apparently weaker properties of weak local identifiability and gradient weak local identifiability--within the widely used exponential family these concepts largely coincide.

Self-teaching neural network learns difficult reactor control problem

International Nuclear Information System (INIS)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

Input relegation control for gross motion of a kinematically redundant manipulator

Energy Technology Data Exchange (ETDEWEB)

Unseren, M.A.

1992-10-01

This report proposes a method for resolving the kinematic redundancy of a serial link manipulator moving in a three-dimensional workspace. The underspecified problem of solving for the joint velocities based on the classical kinematic velocity model is transformed into a well-specified problem. This is accomplished by augmenting the original model with additional equations which relate a new vector variable quantifying the redundant degrees of freedom (DOF) to the joint velocities. The resulting augmented system yields a well specified solution for the joint velocities. Methods for selecting the redundant DOF quantifying variable and the transformation matrix relating it to the joint velocities are presented so as to obtain a minimum Euclidean norm solution for the joint velocities. The approach is also applied to the problem of resolving the kinematic redundancy at the acceleration level. Upon resolving the kinematic redundancy, a rigid body dynamical model governing the gross motion of the manipulator is derived. A control architecture is suggested which according to the model, decouples the Cartesian space DOF and the redundant DOF.

Learning Initiatives for Network Economies in Asia (LIRNEasia ...

International Development Research Centre (IDRC) Digital Library (Canada)

Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building Capacity in ICT Policy ... LIRNEasia seeks to build capacity for evidence-based interventions in the public policy process by persons attuned to the ... Project status.

Unraveling networked learning initiatives: an analytic framework

NARCIS (Netherlands)

Rusman, Ellen; Prinsen, Fleur; Vermeulen, Marjan

2016-01-01

Networked learning happens naturally within the social systems of which we are all part. However, in certain circumstances individuals may want to actively take initiative to initiate interaction with others they are not yet regularly in exchange with. This may be the case when external influences

Building and Sustaining Learning Networks.

OpenAIRE

Bessant, John; Barnes, Justin; Morris, Mike; Kaplinsky, Raphael

2003-01-01

Research suggests that there are a number of potential advantages to learning in some form of network which include being able to benefit from other’s experience, being able to reduce the risks in experimentation, being able to engage in challenging reflection and in making use of peer group support. Examples of such configurations can be found in regional clusters, in sector groupings, in heterogeneous groups sharing a common topic of interest, in user groups concerned with le...

Efficient data communication protocols for wireless networks

Science.gov (United States)

Zeydan, Engin

In this dissertation, efficient decentralized algorithms are investigated for cost minimization problems in wireless networks. For wireless sensor networks, we investigate both the reduction in the energy consumption and throughput maximization problems separately using multi-hop data aggregation for correlated data in wireless sensor networks. The proposed algorithms exploit data redundancy using a game theoretic framework. For energy minimization, routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function used in routing takes into account distance, interference and in-network data aggregation. The proposed energy-efficient correlation-aware routing algorithm significantly reduces the energy consumption in the network and converges in a finite number of steps iteratively. For throughput maximization, we consider both the interference distribution across the network and correlation between forwarded data when establishing routes. Nodes along each route are chosen to minimize the interference impact in their neighborhood and to maximize the in-network data aggregation. The resulting network topology maximizes the global network throughput and the algorithm is guaranteed to converge with a finite number of steps using best response dynamics. For multiple antenna wireless ad-hoc networks, we present distributed cooperative and regret-matching based learning schemes for joint transmit beanformer and power level selection problem for nodes operating in multi-user interference environment. Total network transmit power is minimized while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching based power minimization algorithms, transmit beanformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to

The Role of Action Research in the Development of Learning Networks for Entrepreneurs

Science.gov (United States)

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…

Threshold Learning Dynamics in Social Networks

Science.gov (United States)

González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi

2011-01-01

Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. PMID:21637714

Social Networking Services in E-Learning

Science.gov (United States)

Weber, Peter; Rothe, Hannes

2016-01-01

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

Reconstructing Causal Biological Networks through Active Learning.

Directory of Open Access Journals (Sweden)

Hyunghoon Cho

Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Directory of Open Access Journals (Sweden)

Junkai Yi

2017-01-01

Full Text Available Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

On Deep Learning for Trust-Aware Recommendations in Social Networks.

Science.gov (United States)

Deng, Shuiguang; Huang, Longtao; Xu, Guandong; Wu, Xindong; Wu, Zhaohui

2017-05-01

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL

Energy Technology Data Exchange (ETDEWEB)

Dale K. Tiller; Gregor P. Henze

2004-11-01

Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.

Reliability Analysis and Calibration of Partial Safety Factors for Redundant Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

1998-01-01

Redundancy is important to include in the design and analysis of structural systems. In most codes of practice redundancy is not directly taken into account. In the paper various definitions of a deterministic and reliability based redundancy measure are reviewed. It is described how reundancy can...... be included in the safety system and how partial safety factors can be calibrated. An example is presented illustrating how redundancy is taken into account in the safety system in e.g. the Danish codes. The example shows how partial safety factors can be calibrated to comply with the safety level...

Redundancy Optimization for Error Recovery in Digital Microfluidic Biochips

DEFF Research Database (Denmark)

Alistar, Mirela; Pop, Paul; Madsen, Jan

2015-01-01

Microfluidic-based biochips are replacing the conventional biochemical analyzers, and are able to integrate all the necessary functions for biochemical analysis. The digital microfluidic biochips are based on the manipulation of liquids not as a continuous flow, but as discrete droplets. Research......Microfluidic-based biochips are replacing the conventional biochemical analyzers, and are able to integrate all the necessary functions for biochemical analysis. The digital microfluidic biochips are based on the manipulation of liquids not as a continuous flow, but as discrete droplets....... Researchers have proposed approaches for the synthesis of digital microfluidic biochips, which, starting from a biochemical application and a given biochip architecture, determine the allocation, resource binding, scheduling, placement and routing of the operations in the application. During the execution...... propose an online recovery strategy, which decides during the execution of the biochemical application the introduction of the redundancy required for fault-tolerance. We consider both time redundancy, i.e., re-executing erroneous operations, and space redundancy, i.e., creating redundant droplets...

Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

Science.gov (United States)

Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

2017-02-01

Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Social Networking Sites and Addiction: Ten Lessons Learned

OpenAIRE

Kuss, Daria J.; Griffiths, Mark D.

2017-01-01

Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning onl...

Management of redundancy in flight control systems using optimal decision theory

Science.gov (United States)

1981-01-01

The problem of using redundancy that exists between dissimilar systems in aircraft flight control is addressed. That is, using the redundancy that exists between a rate gyro and an accelerometer--devices that have dissimilar outputs which are related only through the dynamics of the aircraft motion. Management of this type of redundancy requires advanced logic so that the system can monitor failure status and can reconfigure itself in the event of one or more failures. An optimal decision theory was tutorially developed for the management of sensor redundancy and the theory is applied to two aircraft examples. The first example is the space shuttle and the second is a highly maneuvering high performance aircraft--the F8-C. The examples illustrate the redundancy management design process and the performance of the algorithms presented in failure detection and control law reconfiguration.

Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Hui He

2016-01-01

Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

Science.gov (United States)

Coop, Robert; Mishtal, Aaron; Arel, Itamar

2013-10-01

Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.

Analytical reasoning task reveals limits of social learning in networks.

Science.gov (United States)

Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

2014-04-06

Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

Science.gov (United States)

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…