Transnasal endoscopy: no gagging no panic!

PubMed Central

Parker, Clare; Alexandridis, Estratios; Plevris, John; O'Hara, James; Panter, Simon

2016-01-01

Background Transnasal endoscopy (TNE) is performed with an ultrathin scope via the nasal passages and is increasingly used. This review covers the technical characteristics, tolerability, safety and acceptability of TNE and also diagnostic accuracy, use as a screening tool and therapeutic applications. It includes practical advice from an ear, nose, throat (ENT) specialist to optimise TNE practice, identify ENT pathology and manage complications. Methods A Medline search was performed using the terms “transnasal”, “ultrathin”, “small calibre”, “endoscopy”, “EGD” to identify relevant literature. Results There is increasing evidence that TNE is better tolerated than standard endoscopy as measured using visual analogue scales, and the main area of discomfort is nasal during insertion of the TN endoscope, which seems remediable with adequate topical anaesthesia. The diagnostic yield has been found to be similar for detection of Barrett's oesophagus, gastric cancer and GORD-associated diseases. There are some potential issues regarding the accuracy of TNE in detecting small early gastric malignant lesions, especially those in the proximal stomach. TNE is feasible and safe in a primary care population and is ideal for screening for upper gastrointestinal pathology. It has an advantage as a diagnostic tool in the elderly and those with multiple comorbidities due to fewer adverse effects on the cardiovascular system. It has significant advantages for therapeutic procedures, especially negotiating upper oesophageal strictures and insertion of nasoenteric feeding tubes. Conclusions TNE is well tolerated and a valuable diagnostic tool. Further evidence is required to establish its accuracy for the diagnosis of early and small gastric malignancies. There is an emerging role for TNE in therapeutic endoscopy, which needs further study. PMID:28839865

Management of Risk and Uncertainty in Systems Acquisition: Proceedings of the 1983 Defense Risk and Uncertainty Workshop Held at Fort Belvoir, Virginia on 13-15 July 1983

DTIC Science & Technology

1983-07-15

categories, however, represent the reality in major acquisition and are often overlooked. Although Figure 1 does not reflect tne dynamics and Interactions...networking and improved computer capabili- ties probabilistic network simulation became a reality . The Naval Sea Systems Command became involved in...reasons for using the WBS are plain: 1. Virtually all risk-prone activities are performed by the contractor, not Government. Government is responsible

Equivalent or Not? Beyond Measuring Teaching and Learning Standards in a Transnational Education Environment

ERIC Educational Resources Information Center

Lim, Choon Boey; Bentley, Duncan; Henderson, Fiona; Pan, Shin Yin; Balakrishnan, Vimala Devi; Balasingam, Dharshini M.; Teh, Ya Yee

2016-01-01

Purpose: The purpose of this paper is to examine issues academics at importing institutions face while delivering Australian degrees in Malaysia. Transnational higher education (TNE) has been widely researched. However, less widely researched is the area of understanding what academics at the offshore locations need to uphold the required academic…

Condition Recognition for a Program Synthesizer.

DTIC Science & Technology

1981-06-01

suited for tnls type work since it neitner complains c±* boredom nor wanders from its assigned task. Tne macnine meticulously sequences throuzh a series...natural language understanding is a difficult problem that can De solvel only in limited domains. The use of natural language in programming ta been...and output behavior. For example, if someone wanted to lescribe a proeram to compute tne Fibonacci numbers tnen tie could supply tne input-outpost pairs

Web-Based Instruction and Learning: Analysis and Needs Assessment

NASA Technical Reports Server (NTRS)

Grabowski, Barbara; McCarthy, Marianne; Koszalka, Tiffany

1998-01-01

An analysis and needs assessment was conducted to identify kindergarten through grade 14 (K-14) customer needs with regard to using the World Wide Web (WWW) for instruction and to identify obstacles K-14 teachers face in utilizing NASA Learning Technologies products in the classroom. The needs assessment was conducted as part of the Dryden Learning Technologies Project which is a collaboration between Dryden Flight Research Center (DFRC), Edwards, California and Tne Pennsylvania State University (PSU), University Park, Pennsylvania. The overall project is a multiyear effort to conduct research in the development of teacher training and tools for Web-based science, mathematics and technology instruction and learning.

Multiple-relaxation-time lattice Boltzmann kinetic model for combustion

NASA Astrophysics Data System (ADS)

Xu, Aiguo; Lin, Chuandong; Zhang, Guangcai; Li, Yingjun

2015-04-01

To probe both the hydrodynamic nonequilibrium (HNE) and thermodynamic nonequilibrium (TNE) in the combustion process, a two-dimensional multiple-relaxation-time (MRT) version of lattice Boltzmann kinetic model (LBKM) for combustion phenomena is presented. The chemical energy released in the progress of combustion is dynamically coupled into the system by adding a chemical term to the LB kinetic equation. Aside from describing the evolutions of the conserved quantities, the density, momentum, and energy, which are what the Navier-Stokes model describes, the MRT-LBKM presents also a coarse-grained description on the evolutions of some nonconserved quantities. The current model works for both subsonic and supersonic flows with or without chemical reaction. In this model, both the specific-heat ratio and the Prandtl number are flexible, the TNE effects are naturally presented in each simulation step. The model is verified and validated via well-known benchmark tests. As an initial application, various nonequilibrium behaviors, including the complex interplays between various HNEs, between various TNEs, and between the HNE and TNE, around the detonation wave in the unsteady and steady one-dimensional detonation processes are preliminarily probed. It is found that the system viscosity (or heat conductivity) decreases the local TNE, but increases the global TNE around the detonation wave, that even locally, the system viscosity (or heat conductivity) results in two kinds of competing trends, to increase and to decrease the TNE effects. The physical reason is that the viscosity (or heat conductivity) takes part in both the thermodynamic and hydrodynamic responses.

Theranostic nanoemulsions: codelivery of hydrophobic drug and hydrophilic imaging probe for cancer therapy and imaging.

PubMed

Yang, Xinggang; Wang, Dun; Ma, Yan; Zhao, Qiang; Fallon, John K; Liu, Dan; Xu, Xian Emma; Wang, Yongjun; He, Zhonggui; Liu, Feng

2014-12-01

To develop a theranostic nanoemulsion (TNE) that can codeliver the conjugates of a hydrophobic drug paclitaxel (PTX) and a hydrophilic imaging probe sulforhodamine B (SRB). The TNE was established using core-matched technology, and can achieve high encapsulation efficiency and synchronized release of the loaded cargo. It has been examined for a correlation between the dynamic uptake of PTX and the intensity of SRB imaging signal in different organs. Our data demonstrate that the TNE, with improved circulation time, increases therapeutic efficacy and imaging efficiency in both drug-sensitive and drug-resistant cancer. The TNE could not satisfy the demand of visual diagnosis in the living animal because of interference. We therefore formulated a long-circulating theranostic nanoemulsion (LCTNE). Results showed that the LCTNE can meet imaging requirements in vivo. The LCTNE plays a good therapeutic and diagnostic role for subcutaneous tumors in the living animal.

The Influence of Total Nodes Examined, Number of Positive Nodes, and Lymph Node Ratio on Survival After Surgical Resection and Adjuvant Chemoradiation for Pancreatic Cancer: A Secondary Analysis of RTOG 9704

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

Showalter, Timothy N.; Winter, Kathryn A.; Berger, Adam C., E-mail: adam.berger@jefferson.edu

2011-12-01

Purpose: Lymph node status is an important predictor of survival in pancreatic cancer. We performed a secondary analysis of Radiation Therapy Oncology Group (RTOG) 9704, an adjuvant chemotherapy and chemoradiation trial, to determine the influence of lymph node factors-number of positive nodes (NPN), total nodes examined (TNE), and lymph node ratio (LNR ratio of NPN to TNE)-on OS and disease-free survival (DFS). Patient and Methods: Eligible patients from RTOG 9704 form the basis of this secondary analysis of lymph node parameters. Actuarial estimates for OS and DFS were calculated using Kaplan-Meier methods. Cox proportional hazards models were performed to evaluatemore » associations of NPN, TNE, and LNR with OS and DFS. Multivariate Cox proportional hazards models were also performed. Results: There were 538 patients enrolled in the RTOG 9704 trial. Of these, 445 patients were eligible with lymph nodes removed. Overall median NPN was 1 (min-max, 0-18). Increased NPN was associated with worse OS (HR = 1.06, p = 0.001) and DFS (HR = 1.05, p = 0.01). In multivariate analyses, both NPN and TNE were associated with OS and DFS. TNE > 12, and >15 were associated with increased OS for all patients, but not for node-negative patients (n = 142). Increased LNR was associated with worse OS (HR = 1.01, p < 0.0001) and DFS (HR = 1.006, p = 0.002). Conclusion: In patients who undergo surgical resection followed by adjuvant chemoradiation, TNE, NPN, and LNR are associated with OS and DFS. This secondary analysis of a prospective, cooperative group trial supports the influence of these lymph node parameters on outcomes after surgery and adjuvant therapy using contemporary techniques.« less

Long-period Intensity Pulsations in Coronal Loops Explained by Thermal Non-equilibrium Cycles

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

Froment, C.; Auchère, F.; Bocchialini, K.

In solar coronal loops, thermal non-equilibrium (TNE) is a phenomenon that can occur when the heating is both highly stratified and quasi-constant. Unambiguous observational identification of TNE would thus permit us to strongly constrain heating scenarios. While TNE is currently the standard interpretation of coronal rain, the long-term periodic evolution predicted by simulations has never been observed. However, the detection of long-period intensity pulsations (periods of several hours) has been recently reported with the Solar and Heliospheric Observatory /EIT, and this phenomenon appears to be very common in loops. Moreover, the three intensity-pulsation events that we recently studied with themore » Solar Dynamics Observatory /Atmospheric Imaging Assembly (AIA) show strong evidence for TNE in warm loops. In this paper, a realistic loop geometry from linear force-free field (LFFF) extrapolations is used as input to 1D hydrodynamic simulations. Our simulations show that, for the present loop geometry, the heating has to be asymmetrical to produce TNE. We analyze in detail one particular simulation that reproduces the average thermal behavior of one of the pulsating loop bundle observed with AIA. We compare the properties of this simulation with those deduced from the observations. The magnetic topology of the LFFF extrapolations points to the presence of sites of preferred reconnection at one footpoint, supporting the presence of asymmetric heating. In addition, we can reproduce the temporal large-scale intensity properties of the pulsating loops. This simulation further strengthens the interpretation of the observed pulsations as signatures of TNE. This consequently provides important information on the heating localization and timescale for these loops.« less

Transnational Student Voices: Reflections on a Second Chance

ERIC Educational Resources Information Center

Hoare, Lynnel

2012-01-01

The intensity of provision of transnational education (TNE) in the Asian region by Australian universities has been increasing over the past three decades. Although much is claimed, little is actually known about the outcomes and opinions of students enrolled in TNE programs. This article investigates student experiences through the longitudinal…

U.S. Reserve Component Training in U.S. Southern Command - An Example of Total Army Concept.

DTIC Science & Technology

1988-03-22

contention from the very 6eginning is what happens in Central aria South America is that it prov± des art opportunity to test all the elements of...of tne best examples of our commitnment to the region is the recently completed exercise in Ecuador-Abriendo Rutas (Opening Roads). This exercise...to recovery is to restore Ecuador’s transportation network, roaas and bridges are absolutely essential as the first stepo.6 So under guidance from JCS

"No zone" approach in penetrating neck trauma reduces unnecessary computed tomography angiography and negative explorations.

PubMed

Ibraheem, Kareem; Khan, Muhammad; Rhee, Peter; Azim, Asad; O'Keeffe, Terence; Tang, Andrew; Kulvatunyou, Narong; Joseph, Bellal

2018-01-01

The most recent management guidelines advocate computed tomography angiography (CTA) for any suspected vascular or aero-digestive injuries in all zones and give zone II injuries special consideration. We hypothesized that physical examination can safely guide CTA use in a "no zone" approach. An 8-year retrospective analysis of all adult trauma patients with penetrating neck trauma (PNT) was performed. We included all patients in whom the platysma was violated. Patients were classified into three groups as follows: hard signs, soft signs, and asymptomatic. CTA use, positive CTA (contrast extravasation, dissection, or intimal flap) and operative details were reported. Primary outcomes were positive CTA and therapeutic neck exploration (TNE) (defined by repair of major vascular or aero-digestive injuries). A total of 337 patients with PNT met the inclusion criteria. Eighty-two patients had hard signs and all of them went to the operating room, of which 59 (72%) had TNE. One hundred fifty-six patients had soft signs, of which CTA was performed in 121 (78%), with positive findings in 12 (10%) patients. The remaining 35 (22%) underwent initial neck exploration, of which 14 (40%) were therapeutic yielding a high rate of negative exploration. Ninty-nine patients were asymptomatic, of which CTA was performed in 79 (80%), with positive findings in 3 (4%), however, none of these patients required TNE. On sub analysis based on symptoms, there was no difference in the rate of TNE between the neck zones in patients with hard signs (P = 0.23) or soft signs (P = 0.51). Regardless of the zone of injury, asymptomatic patients did not require a TNE. Physical examination regardless of the zone of injury should be the primary guide to CTA or TNE in patients with PNT. Following traditional zone-based guidelines can result in unnecessary negative explorations in patients with soft signs and may need rethinking. Copyright © 2017 Elsevier Inc. All rights reserved.

An Adapted Porter Diamond Model for the Evaluation of Transnational Education Host Countries

ERIC Educational Resources Information Center

Tsiligiris, Vangelis

2018-01-01

Purpose: The purpose of this paper is to propose an adapted Porter Diamond Model (PDM) that can be used by transnational education (TNE) countries and institutions as an analytical framework for the strategic evaluation of TNE host countries in terms of attractiveness for exporting higher education. Design/methodology/approach: The study uses a…

Australian TNE Programmes in Southeast Asia: The Student Perspective

ERIC Educational Resources Information Center

Miliszewska, Iwona; Sztendur, Ewa

2010-01-01

Much of the research in transnational education (TNE) to date has focused around its effectiveness as a teaching medium, and the use of new technologies for teaching. Little attention has been given to the beliefs and behaviours that need to accompany technology so that it has the desired effects; these are assumed to follow the introduction of…

Concurrency Control in Distributed Systems with Applications to Long-Lived Transactions and Partitioned Networks.

DTIC Science & Technology

1983-06-01

i in partition Is ana transaction 󈧐 writes oata ooject a in partition 1I, then the conflict pair "TiTi : a" must ue Includea in tne glocal relation...It botn transactions naa written aatd ob]ect a tnen each control site woulu insert a conflict pair in the glocal relation. "TIT2 : a" and ൔii : a...DATA STUULTUhES 1. Transactions Transactions are implemented in the simulation design as a linked list structure pointed to oy a glocal variaole (TIA

On the Occurrence of Thermal Nonequilibrium in Coronal Loops

NASA Astrophysics Data System (ADS)

Froment, C.; Auchère, F.; Mikić, Z.; Aulanier, G.; Bocchialini, K.; Buchlin, E.; Solomon, J.; Soubrié, E.

2018-03-01

Long-period EUV pulsations, recently discovered to be common in active regions, are understood to be the coronal manifestation of thermal nonequilibrium (TNE). The active regions previously studied with EIT/Solar and Heliospheric Observatory and AIA/SDO indicated that long-period intensity pulsations are localized in only one or two loop bundles. The basic idea of this study is to understand why. For this purpose, we tested the response of different loop systems, using different magnetic configurations, to different stratifications and strengths of the heating. We present an extensive parameter-space study using 1D hydrodynamic simulations (1020 in total) and conclude that the occurrence of TNE requires specific combinations of parameters. Our study shows that the TNE cycles are confined to specific ranges in parameter space. This naturally explains why only some loops undergo constant periodic pulsations over several days: since the loop geometry and the heating properties generally vary from one loop to another in an active region, only the ones in which these parameters are compatible exhibit TNE cycles. Furthermore, these parameters (heating and geometry) are likely to vary significantly over the duration of a cycle, which potentially limits the possibilities of periodic behavior. This study also confirms that long-period intensity pulsations and coronal rain are two aspects of the same phenomenon: both phenomena can occur for similar heating conditions and can appear simultaneously in the simulations.

A three-stage hybrid model for regionalization, trends and sensitivity analyses of temperature anomalies in China from 1966 to 2015

NASA Astrophysics Data System (ADS)

Wu, Feifei; Yang, XiaoHua; Shen, Zhenyao

2018-06-01

Temperature anomalies have received increasing attention due to their potentially severe impacts on ecosystems, economy and human health. To facilitate objective regionalization and examine regional temperature anomalies, a three-stage hybrid model with stages of regionalization, trends and sensitivity analyses was developed. Annual mean and extreme temperatures were analyzed using the daily data collected from 537 stations in China from 1966 to 2015, including the annual mean, minimum and maximum temperatures (Tm, TNm and TXm) as well as the extreme minimum and maximum temperatures (TNe and TXe). The results showed the following: (1) subregions with coherent temperature changes were identified using the rotated empirical orthogonal function analysis and K-means clustering algorithm. The numbers of subregions were 6, 7, 8, 9 and 8 for Tm, TNm, TXm, TNe and TXe, respectively. (2) Significant increases in temperature were observed in most regions of China from 1966 to 2015, although warming slowed down over the last decade. This warming primarily featured a remarkable increase in its minimum temperature. For Tm and TNm, 95% of the stations showed a significant upward trend at the 99% confidence level. TNe increased the fastest, at a rate of 0.56 °C/decade, whereas 21% of the stations in TXe showed a downward trend. (3) The mean temperatures (Tm, TNm and TXm) in the high-latitude regions increased more quickly than those in the low-latitude regions. The maximum temperature increased significantly at high elevations, whereas the minimum temperature increased greatly at middle-low elevations. The most pronounced warming occurred in eastern China in TNe and northwestern China in TXe, with mean elevations of 51 m and 2098 m, respectively. A cooling trend in TXe was observed at the northwestern end of China. The warming rate in TNe varied the most among the subregions (0.63 °C/decade).

Use of anticoagulant or antiplatelet agents is not related to epistaxis in patients undergoing transnasal endoscopy

PubMed Central

Kobayashi, Yoshiya; Komazawa, Yoshinori; Yuki, Mika; Ishitobi, Hitomi; Nagaoka, Makoto; Takahashi, Yoshiko; Nakashima, Sayaka; Shizuku, Toshihiro; Kinoshita, Yoshikazu

2018-01-01

Background and study aims  Unsedated transnasal endoscopy (uTNE) has become accepted as a safe and tolerable method for upper gastrointestinal tact examinations. Epistaxis is 1 of the major complications of TNE, though its risk factors have not been elucidated. Generally, patients administered an anticoagulant or antiplatelet drug are considered to have an increased risk of epistaxis during TNE. Here, we investigated risk factors of epistaxis in patients undergoing uTNE, with focus on those who received antithrombotic agents. Patients and methods  We enrolled 6860 patients (average age 55.6 ± 12.97 years; 3405 males, 3455 females) who underwent uTNE and received the same preparations for the procedure. Epistaxis was evaluated using endoscopic images obtained while withdrawing the scope through the nostril. We also noted current use of medications including anticoagulant or antiplatelet agents prior to the endoscopic examination. Results  Epistaxis occurred in 3.6 % of the enrolled patients (245/6860), and that rate was significantly higher in younger patients (average age 49.31 ± 11.8 years for epistaxis group vs. 55.83 ± 13.0 years for no epistaxis group, P  < 0.01) as well as females (4.78 % vs. 2.35 %, P  < 0.01). The odds ratio for occurrence of epistaxis was 2.31 (95 %CI: 1.746 – 3.167) in the younger patients and 2.02 (95 % CI: 1.542 – 2.659) in females. In contrast, there was no significant difference for rate of epistaxis between patients with and without treatment with an antithrombotic agent (3.0 % vs. 3.6 %). Conclusions  The rate of epistaxis was higher in younger and female patients. Importantly, that rate was not significantly increased in patients who were administered an antithrombotic agent. PMID:29344570

Cluster Beam Deposition of High Temperature Materials

DTIC Science & Technology

1991-01-01

include Secur y Classifocation) CLUSTER BEAM DEPOSITION OF HIGH TEMPERATURE MATERIALS 12 . PERSONAL AUTHOR(S) William J. Herron and James F. Garvey 13a TYPE... industria - applications (su:erconducting thin films, diamond-liKe !arbn,. films, patterned or multi-layered thin films, etc...) INT RODIU C’I 1 Recently there...Tne path of the expanc~ nr gas pulse passes perpendicularly (left to right in tne figure) over the surface of the target rod. I I Laser Beam-I I I Lens

Mangifera indica L. Leaf Extract in Combination With Luteolin or Quercetin Enhances VO2peak and Peak Power Output, and Preserves Skeletal Muscle Function During Ischemia-Reperfusion in Humans.

PubMed

Gelabert-Rebato, Miriam; Wiebe, Julia C; Martin-Rincon, Marcos; Gericke, Nigel; Perez-Valera, Mario; Curtelin, David; Galvan-Alvarez, Victor; Lopez-Rios, Laura; Morales-Alamo, David; Calbet, Jose A L

2018-01-01

It remains unknown whether polyphenols such as luteolin (Lut), mangiferin and quercetin (Q) have ergogenic effects during repeated all-out prolonged sprints. Here we tested the effect of Mangifera indica L. leaf extract (MLE) rich in mangiferin (Zynamite®) administered with either quercetin (Q) and tiger nut extract (TNE), or with luteolin (Lut) on sprint performance and recovery from ischemia-reperfusion. Thirty young volunteers were randomly assigned to three treatments 48 h before exercise. Treatment A: placebo (500 mg of maltodextrin/day); B: 140 mg of MLE (60% mangiferin) and 50 mg of Lut/day; and C: 140 mg of MLE, 600 mg of Q and 350 mg of TNE/day. After warm-up, subjects performed two 30 s Wingate tests and a 60 s all-out sprint interspaced by 4 min recovery periods. At the end of the 60 s sprint the circulation of both legs was instantaneously occluded for 20 s. Then, the circulation was re-opened and a 15 s sprint performed, followed by 10 s recovery with open circulation, and another 15 s final sprint. MLE supplements enhanced peak (Wpeak) and mean (Wmean) power output by 5.0-7.0% ( P < 0.01). After ischemia, MLE+Q+TNE increased Wpeak by 19.4 and 10.2% compared with the placebo ( P < 0.001) and MLE+Lut ( P < 0.05), respectively. MLE+Q+TNE increased Wmean post-ischemia by 11.2 and 6.7% compared with the placebo ( P < 0.001) and MLE+Lut ( P = 0.012). Mean VO 2 during the sprints was unchanged, suggesting increased efficiency or recruitment of the anaerobic capacity after MLE ingestion. In women, peak VO 2 during the repeated sprints was 5.8% greater after the administration of MLE, coinciding with better brain oxygenation. MLE attenuated the metaboreflex hyperpneic response post-ischemia, may have improved O 2 extraction by the Vastus Lateralis (MLE+Q+TNE vs. placebo, P = 0.056), and reduced pain during ischemia ( P = 0.068). Blood lactate, acid-base balance, and plasma electrolytes responses were not altered by the supplements. In conclusion, a MLE extract rich in mangiferin combined with either quercetin and tiger nut extract or luteolin exerts a remarkable ergogenic effect, increasing muscle power in fatigued subjects and enhancing peak VO 2 and brain oxygenation in women during prolonged sprinting. Importantly, the combination of MLE+Q+TNE improves skeletal muscle contractile function during ischemia/reperfusion.

General Reevaluation and Supplement to Environmental Impact Statement for Flood Control and Related Purposes. Red and Red Lake Rivers at East Grand Forks, Minnesota.

DTIC Science & Technology

1984-11-01

ORGANIZATION (if applicable) 8c. ADDRESS (City, State, and ZIP Code) 10. SOURCE OF FUNDING NUMBERS PROGRAM PROJECT TASK IWORK UNIT ELEMENT NO. NO. NO...participate in tne project. The city has also entered the regular phase of tne National Flood Insurance program adopted 23 September 1977. The State ’V of...releases It o Possible sites outside area of city control/ during periods of low flow. responsibility. -s Red Lake Watersned District has a current program

EVIDENCE FOR EVAPORATION-INCOMPLETE CONDENSATION CYCLES IN WARM SOLAR CORONAL LOOPS

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

Froment, C.; Auchère, F.; Bocchialini, K.

2015-07-10

Quasi-constant heating at the footpoints of loops leads to evaporation and condensation cycles of the plasma: thermal non-equilibrium (TNE). This phenomenon is believed to play a role in the formation of prominences and coronal rain. However, it is often discounted as being involved in the heating of warm loops because the models do not reproduce observations. Recent simulations have shown that these inconsistencies with observations may be due to oversimplifications of the geometries of the models. In addition, our recent observations reveal that long-period intensity pulsations (several hours) are common in solar coronal loops. These periods are consistent with thosemore » expected from TNE. The aim of this paper is to derive characteristic physical properties of the plasma for some of these events to test the potential role of TNE in loop heating. We analyzed three events in detail using the six EUV coronal channels of the Solar Dynamics Observatory/Atmospheric Imaging Assembly. We performed both a differential emission measure (DEM) and a time-lag analysis, including a new method to isolate the relevant signal from the foreground and background emission. For the three events, the DEM undergoes long-period pulsations, which is a signature of periodic heating even though the loops are captured in their cooling phase, as is the bulk of the active regions. We link long-period intensity pulsations to new signatures of loop heating with strong evidence for evaporation and condensation cycles. We thus simultaneously witness widespread cooling and TNE. Finally, we discuss the implications of our new observations for both static and impulsive heating models.« less

A Microcomputer System for Target Information in the Fire Support Coordination Center: A Data Base Approach.

DTIC Science & Technology

1981-06-01

that nave a sianificant but specific or "short- life " value to their operations in their unit list of targets. ks an illustration, a battalion would...screen It is reco~nized that the desizn and i-plementation is tased on tne ALTCS ACS 3 -1 co"puer. Advances in microcomputer tectnolopy will invariably...artillery firinz an!ts arA naval n’fn. r-e suport statior.s t o determine automratically, w nic, tar ets are witnin tne effective ra,e )f specific s.~p

Transnasal flexible esophagoscopy (TNE): an evaluation of the patient's experience and time management.

PubMed

Streckfuss, Alexandra; Bosch, Nikolaus; Plinkert, Peter K; Baumann, Ingo

2014-02-01

The aim of this study was to evaluate patient's experience when performing transnasal flexible endoscopy using EndoSheath Technology without sedation in an ENT outpatient department. Patients were seen at the laryngological clinic of the Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Heidelberg, presenting with complaints of reflux like throat cleaning, persistent cough, globus sensation, heartburn, or voice problems. First, we performed stroboscopy. In cases where physical examination findings revealed the presence of LPR, we performed a transnasal flexible esophagoscopy (TNE) using sterile EndoSheath Technology under local anesthesia. 55 patients were investigated and completed a questionnaire on subjective discomfort that they felt during the procedure. The different steps of the examination were assessed separately. Complications were noted down by the surgeon. All patients underwent a complete examination of the upper aerodigestive tract. The time needed for preparation, examination and cleaning measures was recorded as well. The average preparation time for each examination was 24 min. No complications were observed during the procedure. The procedure was well tolerated by all patients and was classified on average as "low-grade unpleasant". In summary, TNE is a safe, quick and well-tolerated procedure that can be performed in a regular examination room under local anesthesia without sedation.

Utility of the transnasal esophagoscope in the management of chemoradiation-induced esophageal stenosis.

PubMed

Peng, Kevin A; Feinstein, Aaron J; Salinas, Jonathan B; Chhetri, Dinesh K

2015-03-01

This study aimed to describe management of esophageal stenosis after chemoradiation therapy for head and neck squamous cell carcinoma (HNSCC), with particular emphasis on techniques and outcomes with the use of the transnasal esophagoscope (TNE) in the office as well as operating room settings. Retrospective analysis of all patients with esophageal stenosis following head and neck cancer radiation, with or without chemotherapy, and managed with TNE-assisted esophageal dilation over a 5-year period. Preoperative and postoperative swallowing function were assessed objectively with the Functional Outcome Swallowing Scale (FOSS; ranging from score 0, a normal diet, to score 5, complete dependence on nonoral nutrition). Twenty-five patients met inclusion criteria. The mean pretreatment FOSS score was 4.4, whereas the mean posttreatment FOSS score was 2.7 (Wilcoxon signed-rank test, P<.001). Prior to dilation, 16 patients were completely gastrostomy-tube dependent (FOSS 5), of whom 12 (75%) were able to tolerate oral nutrition for a majority of their diet following treatment according to our protocol. No complications were noted. Dysphagia following chemoradiation therapy for HNSCC is often related to esophageal stenosis. With the aid of TNE, we have developed a successful treatment strategy for esophageal stenosis with improved success rates. © The Author(s) 2014.

Thermal Non-equilibrium Revealed by Periodic Pulses of Random Amplitudes in Solar Coronal Loops

NASA Astrophysics Data System (ADS)

Auchère, F.; Froment, C.; Bocchialini, K.; Buchlin, E.; Solomon, J.

2016-08-01

We recently detected variations in extreme ultraviolet intensity in coronal loops repeating with periods of several hours. Models of loops including stratified and quasi-steady heating predict the development of a state of thermal non-equilibrium (TNE): cycles of evaporative upflows at the footpoints followed by falling condensations at the apex. Based on Fourier and wavelet analysis, we demonstrate that the observed periodic signals are indeed not signatures of vibrational modes. Instead, superimposed on the power law expected from the stochastic background emission, the power spectra of the time series exhibit the discrete harmonics and continua expected from periodic trains of pulses of random amplitudes. These characteristics reinforce our earlier interpretation of these pulsations as being aborted TNE cycles.

Thermal Non-Equilibrium Revealed by Periodic Pulses of Random Amplitudes in Solar Coronal Loops

NASA Astrophysics Data System (ADS)

Auchère, F.; Froment, C.; Bocchialini, K.; Buchlin, E.; Solomon, J.

2016-10-01

We recently detected variations in extreme ultraviolet intensity in coronal loops repeating with periods of several hours. Models of loops including stratified and quasi-steady heating predict the development of a state of thermal non-equilibrium (TNE): cycles of evaporative upflows at the footpoints followed by falling condensations at the apex. Based on Fourier and wavelet analysis, we demonstrate that the observed periodic signals are indeed not signatures of vibrational modes. Instead, superimposed on the power law expected from the stochastic background emission, the power spectra of the time series exhibit the discrete harmonics and continua expected from periodic trains of pulses of random amplitudes. These characteristics reinforce our earlier interpretation of these pulsations as being aborted TNE cycles.

Study on high throughput nanomanufacturing of photopatternable nanofibers using tube nozzle electrospinning with multi-tubes and multi-nozzles

NASA Astrophysics Data System (ADS)

Fang, Sheng-Po; Jao, PitFee; Senior, David E.; Kim, Kyoung-Tae; Yoon, Yong-Kyu

2017-12-01

High throughput nanomanufacturing of photopatternable nanofibers and subsequent photopatterning is reported. For the production of high density nanofibers, the tube nozzle electrospinning (TNE) process has been used, where an array of micronozzles on the sidewall of a plastic tube are used as spinnerets. By increasing the density of nozzles, the electric fields of adjacent nozzles confine the cone of electrospinning and give a higher density of nanofibers. With TNE, higher density nozzles are easily achievable compared to metallic nozzles, e.g. an inter-nozzle distance as small as 0.5 cm and an average semi-vertical repulsion angle of 12.28° for 8-nozzles were achieved. Nanofiber diameter distribution, mass throughput rate, and growth rate of nanofiber stacks in different operating conditions and with different numbers of nozzles, such as 2, 4 and 8 nozzles, and scalability with single and double tube configurations are discussed. Nanofibers made of SU-8, photopatternable epoxy, have been collected to a thickness of over 80 μm in 240 s of electrospinning and the production rate of 0.75 g/h is achieved using the 2 tube 8 nozzle systems, followed by photolithographic micropatterning. TNE is scalable to a large number of nozzles, and offers high throughput production, plug and play capability with standard electrospinning equipment, and little waste of polymer.

THERMAL NON-EQUILIBRIUM REVEALED BY PERIODIC PULSES OF RANDOM AMPLITUDES IN SOLAR CORONAL LOOPS

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

Auchère, F.; Froment, C.; Bocchialini, K.

2016-08-20

We recently detected variations in extreme ultraviolet intensity in coronal loops repeating with periods of several hours. Models of loops including stratified and quasi-steady heating predict the development of a state of thermal non-equilibrium (TNE): cycles of evaporative upflows at the footpoints followed by falling condensations at the apex. Based on Fourier and wavelet analysis, we demonstrate that the observed periodic signals are indeed not signatures of vibrational modes. Instead, superimposed on the power law expected from the stochastic background emission, the power spectra of the time series exhibit the discrete harmonics and continua expected from periodic trains of pulsesmore » of random amplitudes. These characteristics reinforce our earlier interpretation of these pulsations as being aborted TNE cycles.« less

Prominence Mass Supply and the Cavity

NASA Technical Reports Server (NTRS)

Schmit, Donald J.; Gibson, S.; Luna, M.; Karpen, J.; Innes, D.

2013-01-01

A prevalent but untested paradigm is often used to describe the prominence-cavity system; the cavity is under-dense because it it evacuated by supplying mass to the condensed prominence. The thermal non-equilibrium (TNE) model of prominence formation offers a theoretical framework to predict the thermodynamic evolutin of the prominence and the surrounding corona. We examine the evidence for a prominence-cavity connection by comparing the TNE model and diagnostics of dynamic extreme ultraviolet (EUV) emission surrounding the prominence, specifically prominence horns. Horns are correlated extensions of prminence plasma and coronal plasma which appear to connect the prominence and cavity. The TNE model predicts that large-scale brightenings will occur in the Solar Dynamics Observatory Atmospheric Imaging Assembly 171 A badpass near he prominence that are associated with the cooling phase of condensation formation. In our simulations, variations in the magnitude of footpoint heating lead to variations in the duration, spatial scale, and temporal offset between emission enhancements in the other EUV bandpasses. While these predictions match well a subset of the horn observations, the range of variations in the observed structures is not captured by the model. We discuss the implications of one-dimensional loop simulations for the three-dimensional time-averaged equilibrium in the prominence and the cavity. Evidence suggests that horns are likely caused by condensing prominence plasma, but the larger question of whether this process produces a density-depleted cavity requires a more tightly constrained model of heating and better knowledge of the associated magnetic structure.

Thermomagnetic and thermoelectric properties of semiconductors (PbTe, PbSe) at ultrahigh pressures

NASA Astrophysics Data System (ADS)

Ovsyannikov, Sergey V.; Shchennikov, Vladimir V.

2004-02-01

The longitudinal and transverse thermomagnetic Nernst-Ettingshausen (LNE, TNE) effects and the Maggi-Reghi-Leduc (MRL) effect were measured on PbTe and PbSe micro-samples at ultrahigh pressures upto 20 GPa. Values of the mobility of charge carriers as well as the scattering parameter were estimated both for the low- and high-pressure phase of PbTe and PbSe. At about 3 GPa, the maxima of both Nernst-Ettingshausen effects and magnetoresistance (MR) (and hence of the mobility of charge carriers μ), attributed to the gapless state of PbTe and PbSe were established. The TNE effect was found to be the largest among the effects measured, while the MRL was hardly visible even at the highest mobility values of the charge carriers. The possibilities for using thermomagnetic effects in micro-device technologies are discussed.

Transnational Education and Employability Development

ERIC Educational Resources Information Center

Mellors-Bourne, Robin; Jones, Elspeth; Woodfield, Steve

2015-01-01

Internationalisation and employability development are important themes for UK higher education (HE) and the Higher Education Academy (HEA). One aspect of many UK HE institutions' internationalisation strategies has been to increase the number and range of UK programmes delivered "offshore" as transnational education (TNE)--through…

A comparison of direct and indirect analytical approaches to measuring total nicotine equivalents in urine.

PubMed

Taghavi, Taraneh; Novalen, Maria; Lerman, Caryn; George, Tony P; Tyndale, Rachel F

2018-05-31

Total nicotine equivalents (TNE), the sum of nicotine and metabolites in urine, is a valuable tool for evaluating nicotine exposure. Most methods for measuring TNE involve two-step enzymatic hydrolysis for indirect quantification of glucuronide metabolites. Here, we describe a rapid, low-cost direct liquid chromatography - tandem mass spectrometry (LCMS) assay. In 139 smokers' urine samples, Bland-Altman, correlation, and regression analyses were used to investigate differences in quantification of nicotine and metabolites, TNE, and nicotine metabolite ratio (NMR) between direct and indirect LCMS methods. DNA from a subset (n=97 smokers) was genotyped for UGT2B10*2 and UGT2B17*2 and the known impact of these variants was evaluated using urinary ratios determined by the direct versus indirect method. The direct method showed high accuracy (0-9% bias) and precision (3-14% coefficient of variation) with similar distribution of nicotine metabolites to literary estimates and good agreement between the direct and indirect methods for nicotine, cotinine, and 3-hydroxycotinine (ratios 0.99-1.07), but less agreement for their respective glucuronides (ratios 1.16-4.17). The direct method identified urinary 3HC+3HC-GLUC/COT as having the highest concordance with plasma NMR and provided substantially better estimations of the established genetic impact of glucuronidation variants compared to the indirect method. Direct quantification of nicotine and metabolites is less time-consuming and less costly, and provides accurate estimates of nicotine intake, metabolism rate and the impact of genetic variation in smokers. Lower cost and maintenance combined with high accuracy and reproducibility make the direct method ideal for smoking biomarker, NMR and pharmacogenomics studies. Copyright ©2018, American Association for Cancer Research.

Office-based esophageal dilation in head and neck cancer: Safety, feasibility, and cost analysis.

PubMed

Howell, Rebecca J; Schopper, Melissa A; Giliberto, John Paul; Collar, Ryan M; Khosla, Sid M

2018-02-08

To review experience, safety, and cost of office-based esophageal dilation in patients with history of head and neck cancer (HNCA). The medical records of patients undergoing esophageal dilation in the office were retrospectively reviewed between August 2015 and May 2017. Patients were given nasal topical anesthesia. Next, a transnasal esophagoscopy (TNE) was performed. If the patient tolerated TNE, we proceeded with esophageal dilation using Seldinger technique with the CRE™ Boston Scientific (Boston Scientific Corp., Marlborough, MA) balloon system. Patients were discharged directly from the outpatient clinic. Forty-seven dilations were performed in 22 patients with an average of 2.1 dilations/patient (range 1-10, standard deviation [SD] ± 2.2). Seventeen patients (77%) were male. The average age was 67 years (range 35-78 years, SD ± 8.5). The most common primary site of cancer was oral cavity/oropharynx (n = 10), followed by larynx (n = 6). All patients (100%) had history of radiation treatment. Four patients were postlaryngectomy. The indication for esophageal dilation was esophageal stricture and progressive dysphagia. All dilations occurred in the proximal esophagus. There were no major complications. Three focal, superficial lacerations occurred. Two patients experienced mild, self-limited epistaxis. One dilation was poorly tolerated due to discomfort. One patient required pain medication postprocedure. Office-based esophageal dilation generated $15,000 less in health system charges compared to traditional operating room dilation on average per episode of care. In patients with history of HNCA and radiation, office-based TNE with esophageal dilation appears safe, well-tolerated, and cost-effective. In a small cohort, the technique has low complication rate and is feasible in an otolaryngology outpatient office setting. 4. Laryngoscope, 2018. © 2018 The American Laryngological, Rhinological and Otological Society, Inc.

Spawning Academic Programs.

ERIC Educational Resources Information Center

Williams, Vernon

This paper briefly describes initiation of academic programming in the area of student development and transplantation of that programming into departmental and college curricula. Obvious advantages of this approach include placing student development courses in tne hands of staff who know students best, insuring the courses' continued existence,…

Towards a New Framework for Analysing Transnational Education

ERIC Educational Resources Information Center

Healey, Nigel; Michael, Lucy

2015-01-01

The well-documented growth of international student mobility has been paralleled by the emergence of so-called "transnational education" (TNE), in which universities deliver their educational services to foreign students in their own countries, rather than the students travelling to the foreign university to study. While universities…

THE AGWA – KINEROS2 SUITE OF MODELING TOOLS

USDA-ARS?s Scientific Manuscript database

A suite of modeling tools ranging from the event-based KINEROS2 flash-flood forecasting tool to the continuous (K2-O2) KINEROS-OPUS biogeochemistry tool. The KINEROS2 flash flood forecasting tool is being tested with the National Weather Service (NEW) is described. Tne NWS version assimilates Dig...

A Historical Analysis of the Biennial Budget Process

DTIC Science & Technology

1988-04-01

commissioned In April 1974. He received a Master of Arts (MA) in Business Management from Tne University of The Philippines in 1983, and, in 1987, complitcd... tardiness there isn’t any empirical evidence to support this con- tention. To fully understand why biennial budgeting will not solve the first * three

On the Foundations of National Military Strategy: Past and Present.

DTIC Science & Technology

1990-12-31

three items. First, her ideology. Marxist thinking is dialectical and teleological . Thus it allows for deviations and temporary setbacKs en route to Its...interest and parochial focus, and breaking from the inertia of tne past- - these are the requirements that the numbers of changes and pace of those

Revolutions in Russian Military Thought: Implications for U.S.-Russian Defense Cooperation

DTIC Science & Technology

1993-06-18

moral parameters [objectives] and physical parameters [means) of the opposing sides. Lenin also introduced the dialectical method of analysis into...Air Command and Saqff Collge Delegatin Viits Gagasin Air principle to conduct bomber Academy (May 92) and fighter unit visits, 0 Rmantai Tne-s Fly to

Transnational Education in Morocco: Current and Future Challenges

ERIC Educational Resources Information Center

Benahnia, Abdellah

2015-01-01

Transnational education (TNE) is becoming a phenomenon in the world of education in many countries. Morocco is included. The flourishing and spreading of many foreign educational institutions, products, and activities is becoming noticeable. As an Islamic nation, Morocco has long maintained its business and educational ties with different foreign…

U.S. EPA, Pesticide Product Label, STARBAR CATTLE & SHEEP DIP, 03/31/1983

EPA Pesticide Factsheets

2011-04-14

... In hhO·\\"rU.1tr.q ttoot \\",f'nt to.",hcll~ dlp~ n \\"'''K .. r ... n·oft .,ter c,rr,'t"'9 to •• phenC' e...,rlng ".rtlcwlnt muur IrotO \\ ... ... In elver ("t', tne ) poor:o, 0' tetl_. ...

Seven Special Kids: Employment Problems of Handicapped Youth.

ERIC Educational Resources Information Center

Smith, R. C.

A study of the employment problems facing physically and mentally handicapped youth is reported. To illustrate the main points, results of extensive interviews with seven handicapped youth are juxtaposed with statistics and findings. The study looks at tne continuum of services offered to handicapped individuals, including understanding the…

Is It Genocide? The Military Implications

DTIC Science & Technology

1998-01-01

effective des-ructlon ol another group -0 occur tie msrrumenrs of state >oner have -0 ze ml, olvec Three oi tie mos- ob\\lous euanqles oI^ genocrce...mos--> from -IS Tutsl social groupmg but mcludmg some moderate Hutus -- Lxere slaughterec I0 -4s tne s;;s:emanc k:..Ims vegan . >laJor Gen Dallaxe. hex

Is UK Transnational Education "One of Britain's Great Growth Industries of the Future?"

ERIC Educational Resources Information Center

Healey, Nigel

2013-01-01

Against the backdrop of unprecedented growth in the global demand for higher education, the UK government has recognised that there is a huge potential market beyond conventional "export education," if its universities can find ways of providing "transnational education" (TNE) to the millions of foreign students unable or…

Forestry Decisions

Treesearch

Stephen G. Boyce

1985-01-01

Viewing the forest as a system that self-organizes in response to a schedule of harvest and culture provides a new basis for making forestry decisions. Computer simulations of states of forest organization through time provide displays of tne production of forest benefits ranging from timber and water to wildlife and recreation. From these displays, the manager chooses...

Exploring the Diverse Motivations of Transnational Higher Education in China: Complexities and Contradictions

ERIC Educational Resources Information Center

Hou, Junxia; Montgomery, Catherine; McDowell, Liz

2014-01-01

This article analyses the current situation of transnational higher education (TNE) in China by conducting a comprehensive documentary analysis. It first situates the phenomenon in global transnational mobility in higher education and then explores the diverse motivations of importing and exporting countries taking China and the UK as linked…

The Radiation Sensitivity of Select Metal Chelate Polymers: Mechanistic Changes at Higher Energies.

DTIC Science & Technology

1987-10-01

mass croducticn necessary for the lo-; cost devices currently on the market . Ai’houan electron beat litho- graphy can provide hiaher resolution, tne...nhotochemistrv of the uranvi ion. Them Soc Rev 3: 139-165 Charlesby A (195Ř) Molecular weight changes in degradation onln - chain polymers. Proc Royal Soc

Grab a Byte. Courseware Evaluation for Vocational and Technical Education.

ERIC Educational Resources Information Center

Rosenfeld, Vila M.; And Others

This courseware evaluation rates the "Grab a Byte" program developed by tne National Dairy Council. (The program--not included in this document--is divided into three sections: Grab-a-Grape uses a quiz-show format to examine students' knowledge of food groups; Nutrition Sleuth reinforces students' nutrient knowledge; and Have-a-Byte…

E622, a miniature, virulence-associated mobile element.

PubMed

Stavrinides, John; Kirzinger, Morgan W B; Beasley, Federico C; Guttman, David S

2012-01-01

Miniature inverted terminal repeat elements (MITEs) are nonautonomous mobile elements that have a significant impact on bacterial evolution. Here we characterize E622, a 611-bp virulence-associated MITE from Pseudomonas syringae, which contains no coding region but has almost perfect 168-bp inverted repeats. Using an antibiotic coupling assay, we show that E622 is transposable and can mobilize an antibiotic resistance gene contained between its borders. Its predicted parent element, designated TnE622, has a typical transposon structure with a three-gene operon, consisting of resolvase, integrase, and exeA-like genes, which is bounded by the same terminal inverted repeats as E622. A broader genome level survey of the E622/TnE622 inverted repeats identified homologs in Pseudomonas, Salmonella, Shewanella, Erwinia, Pantoea, and the cyanobacteria Nostoc and Cyanothece, many of which appear to encompass known virulence genes, including genes encoding toxins, enzymes, and type III secreted effectors. Its association with niche-specific genetic determinants, along with its persistence and evolutionary diversification, indicates that this mobile element family has played a prominent role in the evolution of many agriculturally and clinically relevant pathogenic bacteria.

Low Cotinine Glucuronidation Results in Higher Serum and Saliva Cotinine in African American Compared to White Smokers.

PubMed

Murphy, Sharon E; Sipe, Christopher J; Choi, Kwangsoo; Raddatz, Leah M; Koopmeiners, Joseph S; Donny, Eric C; Hatsukami, Dorothy K

2017-07-01

Background: Tobacco exposure is often quantified by serum or saliva concentrations of the primary nicotine metabolite, cotinine. However, average cotinine concentrations are higher in African Americans (AA) compared with Whites with similar smoking levels. Cotinine is metabolized by UGT2B10 and CYP2A6, and low UGT2B10 activity is common in AA, due to the prevalence of a UGT2B10 splice variant. Methods: UGT2B10 activity was phenotyped in 1,446 smokers (34% AA) by measuring the percentage of cotinine excreted as a glucuronide. Urinary total nicotine equivalents (TNE), the sum of nicotine and 6 metabolites, were determined to quantify smoking dose, and cotinine and 3'-hydroxycotinine were quantified in saliva (study 1) or serum (study 2). Results: Ninety-seven smokers (78% AA) were null for UGT2B10 activity, and the saliva and serum cotinine levels, after adjustment for TNE and cigarettes per day (CPD), were 68% and 48% higher in these smokers compared with nonnull smokers ( P < 0.001). After adjustment for TNE and CPD, salivary cotinine was 35% higher, and serum cotinine 24% higher in AA versus White smokers, but with additional adjustment for UGT2B10 activity, there were no significant differences in saliva and serum cotinine concentrations between these two groups. Conclusions: UGT2B10 activity significantly influences plasma cotinine levels, and higher cotinine concentrations in AA versus White smokers (after adjustment for smoking dose) result from lower levels of UGT2B10-catalyzed cotinine glucuronidation by AA. Impact: UGT2B10 activity or genotype should be considered when using cotinine as a tobacco exposure biomarker, particularly in populations such as AA with high frequencies of UGT2B10 nonfunctional variants. Cancer Epidemiol Biomarkers Prev; 26(7); 1093-9. ©2017 AACR . ©2017 American Association for Cancer Research.

Critical Success Attributes of Transnational IT Education Programmes: The Client Perspective

ERIC Educational Resources Information Center

Miliszewska, Iwona; Sztendur, Ewa

2011-01-01

How can transnational education (TNE) programs be made more effective? According to the literature, no one is in a better position to comment on this question than the students themselves. At the same time, there is a recognized scarcity in the literature of student input into the issue of transnational program effectiveness. In consideration of…

TNE-Trans-National Education or Tensions between National and External? a Case Study of Malaysia

ERIC Educational Resources Information Center

Hill, Christopher; Cheong, Kee-Cheok; Leong, Yin-Ching; Fernandez-Chung, Rozilini

2014-01-01

Transnational education, primarily at the tertiary level, has been growing rapidly, bringing with it high hopes and expectations of benefits to institutions in the countries of origin and destination. However, these potential benefits come with a set of challenges that must be overcome. These challenges include the need to reconcile the…

The Challenges of Managing Transnational Education Partnerships: The Views of "Home-Based" Managers vs "In-Country" Managers

ERIC Educational Resources Information Center

Healey, Nigel Martin

2018-01-01

Purpose: The purpose of this paper is to investigate the challenges of managing transnational education (TNE) partnerships from the perspective of the home university managers. Design/methodology/approach: The study adopts a qualitative, "insider researcher" methodology. It uses a sample set of eight mangers who operate from the home…

Transnational Higher Education Partnerships and the Role of Operational Faculty Members: Developing an Alternative Theoretical Approach for Empirical Research

ERIC Educational Resources Information Center

Bordogna, Claudia M.

2018-01-01

For too long, transnational higher education (TNE) has been linked to discourse predominately focused upon strategic implementation, quality assurance, and pedagogy. While these aspects are important when designing and managing overseas provisions, there is a lack of research focusing on the social interactions that influence the pace and…

A Historical Analysis of Basic Air Force Doctrine Education within the United States States Air Force Air Command and Staff College, 1947-1987.

DTIC Science & Technology

1987-09-01

doctrine, especially joint doctrine. Because of this we make mistakes. I believe that the Air Force needs to develop a formal doctrinallo education...jresenteo arguments for all three points of view, but ne was particularly critical of tne educacional system within tae United States military. He said tnat

Cultural Resources Survey of the Burnside Revetment Item, Ascension and St. James Parishes, Louisiana.

DTIC Science & Technology

1986-08-27

he finds.. .that (parish) of Cantrelle... Each of those four communities (the parishes of Clesets Rouges , Cote des Allemands, Bonnet Carre, and...tne vicinity of Whitehall (Office of Public Works, Baton Rouge ) ........................................... 34 9. Excerpt from the 1877 Mississippi... Rouge )............................................ 62 14. Excerpt from Chart 50, Levee setback maps, Pontchartrain Levee District, ca. 1926, showing a

Bid Responsiveness and the Acceptable Nonconforming Bid.

DTIC Science & Technology

1979-09-30

the. fuidaIenta prin ciples of responsiveness. The Conhl trot ler, hnwever, has ta ken this sairn "reasonable- tit’, ’ t. ,t ’’ , T (I I e l iLd i I...cvo rcome otherwise material deviation i f; the bid, thereby renderin gj it a(,ceptable. Tne Comptroller General’s o) p loach to the , i..sue ft

Annual Progress Report - Fiscal Year 1982

DTIC Science & Technology

1982-10-01

activity . A published method developed in our laboratory for determining tne antibacterial activity of mouse peritoneal phagocytes in vivo (1) has been...EDITOR’S NOTE This FY 1982 Annual Progress report is a general review of research activities of the U. S. Army Medical Research Institute of Infectious...years earlier. Passive immunization, active immunization using killed or living attenuated whole agent, or immunization with sub-unit antigens achieved

Nanoflare vs Footpoint Heating : Observational Signatures

NASA Technical Reports Server (NTRS)

Winebarger, Amy; Alexander, Caroline; Lionello, Roberto; Linker, Jon; Mikic, Zoran; Downs, Cooper

2015-01-01

Time lag analysis shows very long time lags between all channel pairs. Impulsive heating cannot address these long time lags. 3D Simulations of footpoint heating shows a similar pattern of time lags (magnitude and distribution) to observations. Time lags and relative peak intensities may be able to differentiate between TNE and impulsive heating solutions. Adding a high temperature channel (like XRT Be-­thin) may improve diagnostics.

The Imperatives of Tactical Level Maintenance,

DTIC Science & Technology

1986-11-24

consist of the imperatives of tactical level maintenance. These imperatives are: fix forward; provision of repair parts supply in a responsive manner...Fix forward; Provide rer,onsi,,e repair parts supply support; Conduct responsive recoer’, and e,)acuat!Dr operations; Establish and maintain...1 primary battlefield source of supple becomes the maintenance system. The role of maintenance, therefore, is to ass-ist in tne pro ’,51 _n

Tests of Rock Cores Scott Study Area, Missouri

DTIC Science & Technology

1970-05-01

porphyry with some granodiorite and small amounts of dolomite, acid metavolcanics, and dark gray volcanic breccia. Specific gravity, * Schmidt...petrographically identified as predominantly rhyolite and dacite porphyry with some granodiorite and small amounts of dolomite, acid metavolcanics, and dark...exhibit- ing little, if any, hysteresis. 9. Direct and indirect tensile strengths exhibited by tne rhyro- lite and dacite porphyry and granite are very high

Mobilization Base Requirements Model (MOBREM) Study. Phases I-V.

DTIC Science & Technology

1984-08-01

Department Health Services Command Base Mobilization Plan; DARCOM; Army Communications Command (ACC); Military Transportation Manage- ment Command...Chief of Staff. c. The major commands in CONUS are represented on the next line. FORSCOM, DARCOM, TRADOC, and Health Service Commands are the larger...specialized combat support and combat service support training. Tile general support force (GSF) units are non- deployable ’inits supporting tne CONUS

Material decomposition images generated from spectral CT: detectability of urinary calculi and influencing factors.

PubMed

Lv, Peijie; Zhang, Yonggao; Liu, Jie; Ji, Lijuan; Chen, Yan; Gao, Jianbo

2014-01-01

To evaluate the detectability of urinary calculi on material decomposition (MD) images generated from spectral computed tomography (CT) and identify the influencing factors. Forty-six patients were examined with true nonenhanced (TNE) CT and spectral CT urography in the excretory phase. The contrast medium was removed from excretory phase images using water-based (WB) and calcium-based (CaB) MD analysis. The sensitivity for detection on WB and CaB images was evaluated using TNE results as the reference standard. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) on MD images were evaluated. Using logistic regression, the influences of image noise, attenuation, stone size, and patient's body mass index (BMI) were assessed. Threshold values with maximal sensitivity and specificity were calculated by means of receiver operating characteristic analyses. One hundred thirty-six calculi were detected on TNE images; 98 calculi were identified on WB images (sensitivity, 72.06%) and 101 calculi on CaB images (sensitivity, 74.26%). Sensitivities were 76.92% for the 3-5-mm stones and 84.51% for the 5-mm or larger stones on both WB and CaB images but reduced to 46.15% on WB images and 53.85% on CaB images for small calculi (<3 mm). Compared to WB images, CaB images showed lower image noise, higher SNR but similar CNR. Larger stone sizes (both >2.71 mm on WB and CaB) and greater CT attenuation (>280 Hounsfield units [HU] on WB, >215 HU on CaB) of the urinary stones were significantly associated with higher stone visibility rates on WB and CaB images (P ≤ .003). Image noise and BMI showed no impact on the stone detection. MD images generated from spectral CT showed good reliability for the detection of large (>2.71 mm) and hyperattenuating (>280 HU on WB, >215 HU on CaB) urinary calculi. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Virtual nonenhanced dual-energy CT urography with tin-filter technology: determinants of detection of urinary calculi in the renal collecting system.

PubMed

Mangold, Stefanie; Thomas, Christoph; Fenchel, Michael; Vuust, Morten; Krauss, Bernhard; Ketelsen, Dominik; Tsiflikas, Ilias; Claussen, Claus D; Heuschmid, Martin

2012-07-01

To retrospectively determine which features of urinary calculi are associated with their detection after virtual elimination of contrast medium at dual-energy computed tomographic (CT) urography by using a novel tin filter. The institutional ethics committee approved this retrospective study, with waiver of informed consent. A total of 152 patients were examined with single-energy nonenhanced CT and dual-energy CT urography in the excretory phase (either 140 and 80 kV [n=44] or 140 and 100 kV [n=108], with tin filtration at 140 kV). The contrast medium in the renal pelvis and ureters was virtually removed from excretory phase images by using postprocessing software, resulting in virtual nonenhanced (VNE) images. The sensitivity regarding the detection of calculi on VNE images compared with true nonenhanced (TNE) images was determined, and interrater agreement was evaluated by using the Cohen k test. By using logistic regression, the influences of image noise, attenuation, and stone size, as well as attenuation of the contrast medium, on the stone detection rate were assessed. Threshold values with maximal sensitivity and specificity were calculated by means of receiver operating characteristic analyses. Eighty-seven stones were detected on TNE images; 46 calculi were identified on VNE images (sensitivity, 52.9%). Interrater agreement revealed a κ value of 0.95 with TNE images and 0.91 with VNE data. Size (long-axis diameter, P=.005; short-axis diameter, P=.041) and attenuation (P=.0005) of the calculi and image noise (P=.0031) were significantly associated with the detection rate on VNE images. As threshold values, size larger than 2.9 mm, maximum attenuation of the calculi greater than 387 HU, and image noise less than 20 HU were found. After virtual elimination of contrast medium, large (>2.9 mm) and high-attenuation (>387 HU) calculi can be detected with good reliability; smaller and lower attenuation calculi might be erased from images, especially with increased image noise. © RSNA, 2012.

Syntax, Semantics, and Speech

DTIC Science & Technology

1975-04-01

D.R. Reddy (ed.), Academic Press (1975). \\h \\ i: LJ r- I. I. 0 i bbN Heport No. 3067 bolt Beranek and Newman Ine S. mm he directi...context is tne type ^ at trie ielt -nanu gie nonterrainai nonempty string pe 3 grammars, more restricted u in generative cnaracterized by 1...Collins, A. (eds.) Academic Press, (in press [23] wewell , A. et al . ( 1973) Speech Understanoing Systems: tlinal heport of a Study Group. North

Urban Redevelopment and the Structure of Power: The Impact of Private Interests on the Policy-Making Process in Columbus, Ohio.

DTIC Science & Technology

1982-01-01

the southwest by Runtinqton Bank, and the northeast by an attorney and a pharmacist . While some miqht 5 contend that the center of town was at Third...projects load to a breaking up of racial bogenelty of the black omunity? black camin Ity?[, UtI tne stetl’s or e*hbnco *oomc activity in the r. mat do

Visual Accommodation, the Mandelbaum Effect, and Apparent Size.

DTIC Science & Technology

1979-11-01

remarks can lead the designer of visual displays to inappropriate application of some so-called laws of vision. As Ogle (1950) has suggested the objective...SNS control of accommodation, discussing much of tne above literature, and presenting clinical, experimental , and pnarmacological evidence for his view...the mechanisms operates and how to evaluate the relevance of the experimental findings under varying conditions. P1. 7. 7 7 - Cogan (1937) reviewed

The Spanish Pacification of the Philippines, 1565-1600

DTIC Science & Technology

1992-06-05

repartimiento de generos ). These officials were usually able to collect more of a scarce commodity than specified in the repartimiento and sell the excess at...determining tne method of payment, usually profited from it. This was especially the case during the period of the repartimiento de generos . Th...Spaniards Japanese interest continued. Juan de Salcedo, one of the Spanish conquistadors, fought with three piratical Japanese ships in 1572 off the coast

Effects of various spacings on loblolly pine growth

Treesearch

W.E. Walmer; E.G. Owens; J.R. Jorgensen

1975-01-01

Four spacings of loblolly pine trees (6 by 6 ft, 8 by 8 ft, 10 by 10 ft, II by 12 ft) were studied for 15 years at the Calhoun Experlmental Forest ne.ar Union, South carolina. The two wider spacings at 15 years produced trees of greater height, larger diometer, and more sawtimber voll.tne while the two narrower spacings favored bds4l area growth and total cubfc volume...

Autonomous Navigation of USAF Spacecraft

DTIC Science & Technology

1983-12-01

ASSEMBLY 21.LACn. THERM AL RADEARTOR ASEML 21.5 in REFERENC BASE PLATE JELECTRONICS REFERENMODULE ASSEMBLY (4 PLACES) PORRO PRISM & BASE MIRROR -24.25...involved in active satellite-to- satellite cracking for 14 days following one day of ground tracking. Earth geopotential resonance terms are the largest...rotates a prism at 9 rps such that optical signals are injected into each telescope parallel to the reielved starlight. The angle between tne two lines

Articles on Psychology in Communist China

DTIC Science & Technology

1960-06-21

of psychopathy are the confusion of the functions of the brain with those of mental obstacles. The work for the prevention and cure of psychopathy ...analysis of pathology, the etiology of psychopathy is complex and multiple. Of course, ,tne psychogenic factors are of importance in the development of... psychopathy , and some even assume the chief importance. In addition to physical examination as a basis, the diagnosis of psychopathy has to infer

Nigeria: Developing a Strategy for Sub-Saharan Africa

DTIC Science & Technology

1993-04-20

single Islamic government. This extension of Islam and consolidation of tne caliphate accounts for the dichotomy between northern and southern Nigeria...in Nigeria and its eventual colonization of Nigeria in the early twentieth century. Northern and Southern Nigeria were officially united as the Colony...south; a still larger dry central plateau, with much open woodland and savanna ; and a strip of semidesert on the fringes of the Sahel in the north

Electrostatic Safety with Explosion Suppressant Foams.

DTIC Science & Technology

1983-03-01

the foam, and (2) sorption of alkylphenol type substances, present as oxidation inhibitors in the fuel, by the foam. It had been previously reported... alkylphenol type substances. The use of antistatic ingredients in the reticulated polyurethane foam was suggested as a means of minimizing static...foam with JP-4 are: o Removal of diethylhexyl phthalate from the foam. o Sorption of alkylphenol type compounds by the foam. Tne latter of these two

Nicotine and Anatabine Exposure from Very Low Nicotine Content Cigarettes

PubMed Central

Denlinger, Rachel L.; Smith, Tracy T.; Murphy, Sharon E.; Koopmeiners, Joseph S.; Benowitz, Neal L.; Hatsukami, Dorothy K.; Pacek, Lauren R.; Colino, Cirielle; Cwalina, Samantha N.; Donny, Eric C.

2018-01-01

Objectives Research using very low nicotine content (VLNC) cigarettes has shown that participants underreport use of non-study cigarettes. Biomarkers of nicotine exposure could be used to verify compliance with VLNC cigarettes. This study aimed to characterize biomarkers of exposure when participants exclusively use VLNC cigarettes. Methods 23 participants stayed in a hotel that permitted smoking for 5 days and 4 nights. They were provided 2 packs of VLNC cigarettes each day (0.4 mg of nicotine/g of tobacco; Spectrum cigarettes) and did not have access to other tobacco products. 24-hour urine samples were collected to assess exposure to nicotine and anatabine. Results After 4 days of exclusive use, the geometric means for urinary total cotinine, total nicotine equivalents (TNE), and anatabine were 1.13 nmol/ml (92% reduction), 3.17 nmol/ml (94% reduction) and 0.0031 nmol/ml (93% reduction). The population estimates of the 95th percentile of cotinine, TNE, and anatabine levels were 2.69, 6.41, and 0.0099 nmol/ml, respectively. Conclusions Study participants exclusively smoking 0.4 mg/g Spectrum cigarettes are unlikely to have biomarker values above these levels. The data presented here will be valuable to researchers conducting research on use of VLNC cigarettes. PMID:29600258

Modular Digital Missile Guidance, Phase I2

DTIC Science & Technology

1976-01-28

at OMR. The revised study plan was fornally approved by ONR on 29 May 1975, confining the sinulatlon analysis work to a Class II missile with...functions analyzed in tne Phase I and Phase 11 studies . It can be seen thatf tnere practicable» (based on the results of function partitioning trade...llaillAU AS a result of this study » three generic missile families have oeen established and» relative to this classification» on

Project Squid. Annual Program Report

DTIC Science & Technology

1950-01-01

cooled, with helical flow around the combustion chamber walls and approximately longitudinal flow over the nozzle walls. All injectors are of the...instantaneous pressures in the channel near tne rotating valve were recorded by means of a condenser -type pressure gauge3 and the mean pressure was...air ’P k = thermal conductivity of air Nusselt and Jurges2 in 1928, derived an equation similar to the above on the basis of a somewhat different

Program Analysis and Design Requirements for tne National Science Center

DTIC Science & Technology

1991-02-01

shell of an old exposition building with secondhand furniture to display exhibit items, to the Ontario Science Center, which is a more modem building...Storage Area Pigeonhole storage cabinets for children’s school books , coats, and boots are provided at the Indianapolis Center. The Ontario center...used shopping carts for school groups to store their coats and books . They do not work well according to center staff and are cumbersome and unsightly

Law on Geological Exploration in Poland.

DTIC Science & Technology

1961-02-23

control of mining bureaus, as set forth in the mining law and the decree of 21 October 1954 concerning mining enterprises (Dziennik Ustaw, No 47, Poz...meters, unless such work is carried out on mining lane or in a protected mountain mineral sorites area. _Supp.xvis.ion and control over activities...the grounds and the methods of performing tne work defined in paragraph 2, as well as the administration of supervision and control over this work

The Use of Military Force to Counter International Terrorism - A Policy Dilemma.

DTIC Science & Technology

1987-06-05

front ot U.S. attention . Secretary of State George Shultz emphasized U.S. concerns about this growing threat when he wrote: For we must understand...grow;ny. While it captures world attention with vioient *riu spectacular attacks, international terrorism is tacget.,, U.S. foreign interests and its... attention away from the real issue of imperialistic oppression, racism and colonialism.2 2 Critics of the resolution also maintained thaz tne proposal

Modeling Fault Diagnosis Performance on a Marine Powerplant Simulator.

DTIC Science & Technology

1985-08-01

two definitions are very similar. They emphasize that fidelity is a two dimensional -:oncept. They also pointed out the measurement prob- lems. Tasks...simulator duplicares cne enscr-: ztimulation, 4. . rnamic motion cues , visual :ues, ec. ?svcno ogicai fidelity is simply the degree to which the trainee...functions is only acceptable if the performance is paced by tne system, i.e., cues from the system serve to initiate elementary, skilled sub-routines

International Conference on Case Histories in Geotechnical Engineering (2nd) Held in Rolla, Missouri on 1-5 June 1988. Volume 2

DTIC Science & Technology

1989-06-30

Wright (USA) AAV. Shroff Epoxy Resin (rout System for Solutions to Traditional D.P. Am in (;eotechnical Problems. 5.66...echate Containment System -(eotechnical (U SA ) Considerations . .......................................................... 1577 W D .I. Finn Case...girders were set in place in the formed under co er of tne above ra iling system . clayey overburden by low-frequency vibro-driving methoo, then

Estimates of Crustal Transmission Losses Using MLM Array Processing.

DTIC Science & Technology

1982-07-01

boundary with a half space below, and with some form of reflection characteristic and/or loss mechanism. If acoustic energy , upon encountering the bottom...sea-sediment interface would probably be sufficient. However, sound energy does penetrate beneath the sea -2- floor and is both reflected and refracted...back to the water. In an active acoustical experiment, especially at longer ranges, a significant amount of tne received energy may come from waves

An Investigation of Wing Lift Augmentation with Spanwise Tip Blowing.

DTIC Science & Technology

1987-04-22

ON8 Michael R. Mendenhall Steven C. CarusoS Co) D Daniel J. Lesieutre Nielsen Engineering & Research, Inc. 510 Clyde Avenue Mountain View, CA 94043...Augmentation with Spanwise Tip Blowing 12. PERSONALAUTHOR(S) Michael R. Mendenhall, Steven C. Caruso, Daniel J. Lesieutre, and Robert E. Childs 13a. TYPE OF...hardware and associated electronics for the flowfield survey 99 traverse rig mechanism. Mrs. Susana N. Nazario contributed tne software necessary for the

Northeast Scotland Coastal Field Guide and Geographical Essays,

DTIC Science & Technology

1983-07-01

Hill 1970 The Influence of Sediment Availability on the Patterns of Beacn Ridge Development in tne Vicinity of Snoalnaven Delta , N.S.W...metres at high watar. Above the Brig the Don is confined within a narrow gorge. Althoagh estuarine, in that tidal influence on water levels extends up...conditions, although tidal influence may affect water levels within the gorge section above the Brig ’o’ Balgownie, at high water saline penetration

High Altitude Supersonic Target (HAST), Phase 2

DTIC Science & Technology

1974-08-01

consists of a padded cradle assembly and a tubular steel stand with lockable swivel casters on the front wheels . c Tne recovery module lifting handle is...eds no modification of the ordnance fired at it in order to function. With the HAST system a target will be provided to evaluate the most advanced...Components were tested under environmental extremes. With the completion of the preflight readiness tests and with modification incorporated during the

Effect of Chemicals on the Cell Membrane Transport of Nucleosides.

DTIC Science & Technology

1983-08-01

hypoxanthine in the external buffer and the efflux mte is decreased by uric acid in tne buffer. Perfluorodecanoic acid ( PFDA ), adenine, or xanthlne...uric acid in the buffer. Perfluorodecanoic acid ( PFDA ), Sadenine, or xanthine in the external buffer have no direct effect on the rate of AP efflux, in...observed that perfluorooctanoic acid ( PFOA ) produces a transient weight N loss, but no mortality in young rats. By contrast, the treatment of rats with

Advances in the Techniques and Technology of the Application of Nonlinear Filters and Kalman Filters

DTIC Science & Technology

1982-03-01

which ate composed of experts appointed by the National Delegates, the Consultant and Exchange Programme and the Aerospace Applications Studies ...back to the begirning of the twentieth century and the study and mathematical description of diffusion processes (Einstein /19/). This is because the...similarly tne equat in for the covariance matrix P, taking into allount that P(tit) does not depend immed.ately on the observations (the factor

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

PubMed

Flaschberger, Edith; Gugglberger, Lisa; Dietscher, Christina

2013-12-01

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

An Inquiry into the Cost of Post Deployment Software Support (PDSS)

DTIC Science & Technology

1989-09-01

Equations .......... ii vi AFIT/GLM/LSY/835- I0 The increasing cost of software maintenance is taking a larger share of the military bidget each year... increments as needed (3:59). The second page of tne Form 75 starts with a section stating how the hours, and consequently the funds, will be allocated to...length of time required, the timeline can be in hourly, weekly, mnunthly, or quarterly increments . Some milestones included are formal approval, test

Fire Safety Analysis of the Polar Icebreaker Replacement Design. Volume 2

DTIC Science & Technology

1987-10-01

report. ; iote : At t tne -3f incident only five or sx men were aboard: therefore, they could not atterrot to attack a fire of this intensmtp t hemse I...fire extinguisher (PKP) AUTOMATIC: A1301 Halon 1301 total flooding system - remotely actuated AF AFFF (3%) sprinkler system - remotely actuated AFM...simulate wind effects, we have found that its judicious use along with the vent and shaft routines allows for the modelling of simple HVAC systems

Historic Building Inventory, Aberdeen Proving Ground, Maryland

DTIC Science & Technology

1982-01-01

installation into compliance with the National Historic Preservation Act of i9bo ana its amendments, and related federal laws and regulations. To this ena, the...century. OLD BALTIMORE The first formal authorization for the establishment of a Court House was the 1674 Act of Assembly for the construction of a Court...official recorded meeting at the Court House was in 1692, at which Thomas Heath, innkeeper , filed suit for expenses incurreo by tne Justices at the 1687

Annual Summary Report on Thermionic Cathode Project.

DTIC Science & Technology

1986-01-09

Voltage Operation The electron gun cathode is driven negative by a high voltageRadiation pulse modulator in the circuit of Figure 3-1. Typical current...tungsten filament. The bombardment heating system is stabilized by a feed- back control circuit . The power required to heat tne cathode is 315 W bom...project. The primary purpose of the first phase was to develop the bombardment heating circuit used to heat the LaB 6 cathode, and to test the beam

If It Ain't Broke, Why Fix It? Framework and Processes for Engaging in Constructive Institutional Development and Renewal in the Context of Increasing Standards, Assessments, and Accountability for University-Based Teacher Preparation

ERIC Educational Resources Information Center

Lit, Ira; Nager, Nancy; Snyder, Jon David

2010-01-01

In this article, the authors offer a descriptive essay outlining the framework and processes of a five-year institutional renewal effort at Bank Street College of Education. Extended the opportunity to participate in the "Teachers for a New Era" (TNE) initiative, a multi-year, multi-million dollar effort to enhance and "radically…

Commitment in American Foreign Policy, a Theoretical Examination for the Post-Vietnam Era

DTIC Science & Technology

1980-04-01

matters as diverse as public diplomacy and U4S security commitments, and since 1977 has been a policy analyst for tne Unicorn Group, Ltd., an international... growing Soviet military power. The administration’s strengthening of NATO and its recent support of Thailand and North Yemen prove that some officials...9 Needless to say, such developments point to a significant and growing American military commitment to Saudi Arabia. Physical commitment on the

Effects of Electromagnetic Noise and Interference on Performance of Military Radio Communication Systems

DTIC Science & Technology

1988-12-01

December 1988 Copyright C AGARD 1988 All Rights Reserved ISBN 92-835-0487-9 b Printed by Specialised Printing Services Limited 40 Chigwell Lane...atmospheric radio noise, Report No. 322-3, International Telecommunication Union, Geneva, 1986. (6) Giordano, A. A., J. R. Herman, X. A. DeAngelis, K. F...tne CCIR Xth Plenary Assembly. Geneva, 1963, Report 322, International Telecommunications Union, Geneva, 1964. 5. Maxwell, E.L., D.L. Stone, R.D

U. S. Naval Forces, Vietnam Monthly Historical Summary for May 1971

DTIC Science & Technology

1971-07-05

ship convoys during-- CONFIDENTIAL 66C.. 2~.,---’.-- •-- W W -- " --- "- * ., ~ *~ ** -. *16 1.*~ CONFIDENTIAL World Wars I and 11. There have been...COMG.BPAC 𔄁 -7 7-7.7 77 77 . 77-7777..... UNCILASSIBEID COMCBLANT CO MMINEPAC Commandant, Armed Forces Staff College IA Commandant, U.S. Army War College...nil) Npvy Seawolf helicopters carried the war to tne enemy as they struck ten times * killing eight soldiers) damaging five bunkers) and destroying one

Family Housing Metering Test. A Test Program to Determine the Feasibility of Installing Utility Meters in Military Family Housing, Developing Energy Ceilings, and Operating a Penalty Billing System for Occupants Who Overconsume Energy. Volume II. Appendices.

DTIC Science & Technology

1980-03-01

implementation. 5.2 NORM PAMPHLET The norm pamphlet is a short, picture-oriented brochure to introduce military housing occupants to the use of the energy...tne final version of the pamplet , feedbaci roejrdiag the. draf t version sill be solicited from the fiefl test par- ticipants. rhe followlag is a rouga

Co-delivery of antigen and a lipophilic anti-inflammatory drug to cells via a tailorable nanocarrier emulsion.

PubMed

Chuan, Yap Pang; Zeng, Bi Yun; O'Sullivan, Brendan; Thomas, Ranjeny; Middelberg, Anton P J

2012-02-15

Nanotechnology promises new drug carriers that can be tailored to specific applications. Here we report a new approach to drug delivery based on tailorable nanocarrier emulsions (TNEs), motivated by a need to co-deliver a protein antigen and a lipophilic drug for specific inhibition of nuclear factor kappa B (NF-κB) in antigen presenting cells (APCs). Co-delivery for NF-κB inhibition holds promise as a strategy for the treatment of rheumatoid arthritis. We used a highly surface-active peptide (SAP) to prepare a nanosized emulsion having defined surface properties predictable from the SAP sequence. Incorporating the lipophilic drug into the oil phase at the time of emulsion formation enabled its facile packaging. The SAP is depleted from bulk during emulsification, allowing simple subsequent addition of the drug-loaded oil-in-water emulsion to a solution of protein antigen. Decoration of emulsion surface with antigen was achieved via electrostatic deposition. In vitro data showed that the TNE prepared this way was internalized and well-tolerated by model APCs, and that good suppression of NF-κB expression was achieved. This work reports a new type of nanotechnology-based carrier, a TNE, which can potentially be tailored for co-delivery of multiple therapeutic components, and can be made using simple methods using only biocompatible materials. Copyright © 2011 Elsevier Inc. All rights reserved.

Secondary tracheoesophageal puncture in-office using Seldinger technique.

PubMed

Britt, Christopher J; Lippert, Dylan; Kammer, Rachael; Ford, Charles N; Dailey, Seth H; McCulloch, Timothy; Hartig, Gregory

2014-05-01

Evaluate the safety and efficacy of in-office secondary tracheoesophageal puncture (TEP) technique using transnasal esophagoscopy (TNE) and the Seldinger technique in conjunction with a cricothyroidotomy kit for placement. Case series with chart review. Academic medical center. A retrospective chart review was performed on 83 subjects who underwent in-office secondary TEP. Variables that were examined included disease site, staging, histologic diagnosis, extent of resection and reconstruction, chemoradiation, functional voice status (as assessed by speech pathologist in most recent note), and complications directly related to the procedure. Eighty-three individuals from our institution met our criteria for in-office secondary TEP from 2005 to August 2012. Of these, 97.6% (81/83) had no complications of TEP. The overall complication rate was 2.4% (2/83). Complications included bleeding from puncture site and closure of puncture site after dislodgement of prosthesis at the time of puncture. Fluent conversational speech was achieved in 69.9% of all patients (58/83), and an additional 19.3% (16/83) achieved functional/intelligible speech; of those, 3.6% (3/83) were unable to achieve fluent conversational speech due to anatomic defects from previous surgery. An in-office TEP can be safely performed using the Seldinger technique with direct visualization using TNE, despite the extent of resection or reconstruction, with functional speech outcomes comparable to other studies available in the literature.

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

PubMed

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

2015-04-01

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

Impact of the AF Milcon (Military Construction) Funding Shortfall Alternatives and Recommendations.

DTIC Science & Technology

1988-04-01

mix of NM and CM reqg.: eMertEs Theoretically, in an unconstrained funding environr-r.t, tne annual Milcon request would include the sum of all W and... market fall of 19 October 1987, and the record decline in the strength of the dollar. Furthermore, in a press conference 22 October 1987, President...APPENDIX APPENDIX A: MILCON ALLOCATION STATEGY EXCERPT FROM CORONA SOUTH CONFERENCE, (19:1-8) APPEND!:. B: PICTC)RIAL rODEL OF PROPOSED CHANGE TO

Role of the Chaplain in Ministry Related to Psychogenic Diseases.

DTIC Science & Technology

1980-10-01

34 section of this report.) In fact, most physicians, prior to the advent of tne medical model, readily acknowlcdged the reality of the influence of the... virtually all diseases as having a psychogenic component. Although the G.A.S. process is a genetic reaction that, in the past, has served human beings... reality ie is very insecure. Tnis is manifest in his incessant desire to please hi’s superior; even if it means ignoring camarade’ie or congenial

Fibre Optic Sensors Using Adiabatically Tapered Single Mode Fibres

DTIC Science & Technology

1994-02-01

strum in 980 ~i! of buffer. Tne f’uorescent levels seen in this immunoassay are also higher, than what can be predicted using the p + S p estimates ...labelled IgG versus amount of IgG present in various dilutions (serial) of sera sample. .............. 107 Figure I-I Sensing arrangements used in the...case for sensor work in cations is the detection of pH change, sometimes for industrial applications. Further, since pH can be used to diagnose changes

Design of Propeller Ducts to Reduce Cavitation and Vibration.

DTIC Science & Technology

1982-09-01

529 of the paper by Van Manen ind Oosterveld that the sectional lift on an axially symmetric duct should not exceed unity at the risk of inducing stall...max mum lift is only experienced local lv as .ie proceed around the circu"-ere,ce, tne Van Manen and Oosterveld limitation may be too restrictive...The results of carrying out the computations indicated by Eqs. (18) and (13) show that C = 3.0. Since this exceeds the Van Manen and Oosterveld limit

National Dam Safety Program. Structure F-2 (MO 20513), Verdigris - Neosho River Basin, Newton County, Missouri. Phase I Inspection Report.

DTIC Science & Technology

1980-08-01

AXIAL- INITIAL [00 NS CL 13~ rf START DEGOF SATI END CONS;0LI- PRINCIPIAL s T. -s STRAIN AT PU n I DIE~)0.1 AT S f A RT OF IA WON STES I ru Pcf cO DA...SAM.PLE. LUCAII’N FIELD SMk -11 N,) (1711H CLOLOCIZ Cii... 3~ ( i_ . __ _ _ _ __ _ _ TNE I LNETU)D AT API’lOV’U) V~ CA CLASSIFICATION Il!LL ill/P1

A Study to Determine Patient Waiting Time at the Outpatient Pharmacy at Wilford Hall USAF Medical Center

DTIC Science & Technology

1988-06-01

at Wilford Hall USAF Medical Center significantly reduced the patient wait time at the main outpatient pharmacy. Satellite pharmacies have been ).’l...PRESENTING TO WINDOW 1, 19 MAR 88. 47 C:. A’.’E-:A: -ESCRIRTIONS PER PATIENT ...........48 H. WILFORD HALL MEDICAL CENTER OUTPATIENT QUESTIONNAIRE...that wait times at tne outpatient pharmacy were excessive. It was this concern that motivated the Medical Center Administrator to request that patient

Assessment of Threshold Visibility - T56 Turboprop Engines

DTIC Science & Technology

1981-06-01

thus negligible in Equation (3-4). The cross-section for absorption, on the other hand, is a volume effect and thus decreases with the third power of the...little structure exists--it has been damped out. Wittig et al. (Reference 14) have examined the effect of adding an abosrbing component to the refractive...knowledge on tne structure and composition of the exhaust particulates presents two problems. The t ¾rst is how to accourt for shape factors and their effect

Stimulated Cerenkov-Raman Scattering.

DTIC Science & Technology

1983-12-14

operate well into the infrared portion of the spectrum. This will be discussed further in a later section. V -37- II D. THE EFFECT OF BEAM VELOCITY SPREAD... discussed . Cercn.ov ri.itsn at kilawatt nower levels in tne *Submitted by L. R. Ra-.lh~n nili~,et.er reg:cn. her. a descrition of tcn experime:ntal...systems will be discussed . Olin 14al, Room 126 at 9:00 A.M. 1. D. Kapilow and .E. Walsh, Bull. A m. Phys. Soc., P. Glans. p esiding ZS, 6 (1980

Historical and Future Roles of the Tactical Signal Officer

DTIC Science & Technology

1991-03-27

can soil . LTC James. his sigralmen ind ooat crew on tr,e snic m-4Ca scccmolished both feats while under heavy artillery fi-e rrom tne Soanish on...the capture of Fort Malate to Admiral Dewey’s fleet in Manila Bay. Sergeant Gibbs later became Major General Gibbs, and Chief of Signal in 1928.17...kept critical equipment out of operation required in commmand and control, and degraded the unit’s ability to see the enemy at night. These officers

Defense RDT and E Planning and Strategy Parameters: Methodological Considerations. Appendices

DTIC Science & Technology

1974-12-01

true in the case of Saudi Arabia , Kuwait, Qatar and Libya. The "unaligned Third World" is neither unaligned nor is it the entity the term connotes...Liberial Ethiopia, Morocco, Bahrain, Kuwait, Saudi Arabia , etc., which relate to bases and broad considerations of intention relating to defense. The scope...garner a trade surplus of $65 billion, from a base of only $7 billion 3 in 1973. While Italy verges on bankruptcy Saudi Arabia , Kuwait, tne United

National Program for Inspection of Non-Federal Dams. Knox Mill Dam (ME 00276), Megunticook River Basin, Camden, Maine. Phase I Inspection Report.

DTIC Science & Technology

1978-08-01

been furnished the owner, Camden Water & Power Co., 33 Mechanic Street, ..... a ine 0,-43. Co-ies of this report will be made available to the public...gn Branch Engineering Division SAUL CO ER, Member Chief, Water Control Branch Engineering Division APPROVAL RECOMMENDED: JOE B. FRYAR Chief...Camden Water & Power Co. 33 Mechanic Street Camden, Maine 04843 Tne Camden Water and Power Company is an affiliate of Knox Woolen Mills Company. f

Some Effects of Stress on Users of a Voice Recognition System: A Preliminary Inquiry.

DTIC Science & Technology

1983-03-01

criterion of face valiaity Is also imposed (i.e., tne tasks are cctiigrea tc be acce;table to ta:get populations, e.g., pilots ... .Re :.11: pp. 22-Z5j...being the hlgtesz level of eacrt. it was thougtt tnat these iraividual response levels trighz soirehow te reiated to recognitio , rates. L. CUNCI-kTAL...generalizable ;heuorenon, it would irply that after some few training sessions with a reccenizer, the distinction vanishes. If so, faced with a

Investigation of Sand-Cement Grouts

DTIC Science & Technology

1960-09-01

I -IEN NO Isis Table 1 InvestiiatLon of Sand-Cement Crouts Data on Lhe Physical Properties of the inely Divided Mineral Admixt)res Blaine Specific...Itoi, tuicrlt.nel, Caiftrnia; fl1; aish, Illinois; ;1iaricito, California; Lo’ss, Yisniasi~pi; bentornitoe, Wy~caing. Physical drnta for the raateriais...increase i’: tne a.cunt of .anj th-?t coul be puiped. As the diatomite had a specific ,i’face about 1C tines that of the loe33, it would appear that this

JPRS Report, Science & Technology, 16th International Congress of the International Society for Photogrammetry and Remote Sensing -- Volume 2

DTIC Science & Technology

1989-01-27

Rego Barbosa, Osvaldo Caldas; MCT - MINISTERIO DA CIENCIA E TECNOLOGIA , INPE - INSTITUTO DE PESQUISAS ESPACIAIS, Rod. Presidente Dutra, km 40 12630...economic crisis tne country J.- xa^+ y recoanition from the governmental planners of the priorities ana importance of INPE’s role...87 8, 216 430 8, 646 The situation changed and in the last three years a clear -cove^is being observed. Not only Jh«* •=>«* fche u-er tenden y

KAPSE Interface Team (KIT) Public Report. Volume 8, Part 2

DTIC Science & Technology

1989-10-01

impierenetations, for the indus- library’, is rmguired to augmnent tne machne trial and c="mer’,jal (non-Government) independent portion cf C with sufficient market ...a nuxnbc;- 6.1 Kernel Impleuneutatlon ofdefetrred iterns. Thcese include: ofThe onlN project under way which is in this Database Schemna and Entity...710 I U - Ada is expected to become widely accepted in the RiD community. The approach is also Justift.id by the fact that market pressurms will

An Analysis of Elliptic Grid Generation Techniques Using an Implicit Euler Solver.

DTIC Science & Technology

1986-06-09

automatic determination of the control fu.nction, . elements of covariant metric tensor in the elliptic grid generation system , from the Cm = 1,2,3...computational fluid d’nan1-cs code. Tne code Inclues a tnree-dimensional current research is aimed primaril: at algebraic generation system based on transfinite...start the iterative solution of the f. ow, nea, transfer, and combustion proble:s. elliptic generation system . Tn13 feature also .:ven-.ts :.t be made

Modern Hardware Technologies and Software Techniques for On-Line Database Storage and Access.

DTIC Science & Technology

1985-12-01

of the information in a message narrative. This method employs artificial intelligence techniques to extract information, In simalest terms, an...disf ribif ion (tape replacemenf) systemns Database distribution On-fine mass storage Videogame ROM (luke-box I Media Cost Mt $2-10/438 $10-SO/G38...trajninq ot tne great intelligence for the analyst would be required. If, on’ the other hand, a sentence analysis scneme siTole enouq,. for the low-level

Ligand Structural Influences upon Electrochemical Reactivity: Organic Substituent Effects upon Carboxylatopentaamminecobalt(III) Reductions at Mercury and Gold Electrodes.

DTIC Science & Technology

1985-01-01

surface interactions, substituent effects, nonaqueous medi\\ ZO’ ST RA T (Continue an rev roe . d f nroomy aidje e i a bloc nube) .tne e-ectroreducton...seen for the 2+ . Ru(NH3 6 reduction kinetics in aqueous solution. * . .,:. *.**f: % *% *. ** % ** * * * * *% **% - ........... <. . . -W ! -- .... d ...accordance with Eq. (1) has been found for a number of systems, substantial deviations have also been observed. b d 2b Of particular interest are the rate

Fluoxetine Hydrochloride Enhances In Vitro Susceptibility to Chloroquine in Resistant Plasmodium falciparum

DTIC Science & Technology

1992-12-01

34AD"A 59 579 N.PAGE 10M07.""l kflll/IllI/ 1. AGENCY USE ONLv - 1 1 j 1 TT•PE DATES COVERD 4. TnE AN0 SUBTITI S. FUNDING NUMBERS Fluoxetine ...of currently available antimalarial drugs, such as chloroquine. We identified fluoxetine hydro- chloride (Prozac), a commonly prescribed antidepressant...locations confirmed our initial observations with a chloroquine-resistant P. falciparum clone, W2. Fluoxetine con- centrations of 500 nM were found to

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

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

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

PubMed

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

2015-04-15

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

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

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

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

ERIC Educational Resources Information Center

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

2015-01-01

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

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

ERIC Educational Resources Information Center

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

2018-01-01

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

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

ERIC Educational Resources Information Center

Smith, Sue; Kempster, Steve; Barnes, Stewart

2017-01-01

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

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

PubMed Central

Sinapayen, Lana; Ikegami, Takashi

2017-01-01

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

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

PubMed

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

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

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

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

Blending Formal and Informal Learning Networks for Online Learning

ERIC Educational Resources Information Center

Czerkawski, Betül C.

2016-01-01

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

The Substitution of IVD (Ion Vapor Deposition) Aluminum for Cadmium

DTIC Science & Technology

1989-08-01

environment, it can finu its A in( the water suHpl o- fooo chain. Also, with electroplated cadmium processing, tnere are auditional nazaros associated win...no , - r e CUr r in ( polut io n- r elIt e costs. Tne po IIut ion controlI costs for all caG;iul n processing , nclIUd ino, clpitl treatm’ient , col...H T P, H VacuumCadmium None T. O. H T. P. H Key T - Toxic materials 0 - OSHA standards imposed P - Polution control required H - Hazardous waste

Justification of Estimates for Fiscal Year 1984 Submitted to Congress January 1983: Missile Procurement.

DTIC Science & Technology

1983-01-01

for procurement of the first operational SRAM/ Altair /MV. (RDTE PE 64406F, 1 245JF) Space Suttle - Tne Space Shuttle is a NASA development program to...ACQUISITION IA.. R STUART IINCIASSIFIED JAN 83 RO.J- AC-64- FIG 5/ 1 NL IIIIIIIIIIIIIu IIIIIIIIIIIIIl IIIIIIIIIIIIIhl IIIhhhhmh ii1w 1112., 0 1111Ŗ--5, I i...MICROCOPY RESOLUTION TEST CHART NATIONAL BUREAU OF STANARS - 963 -A ’!/ 1 JA 12 5955 DEPARTMENT OF THE AIR FORCE JUSTIFICATION OF E&IMATES FOR FISCAL YEAR

Heritage Forward: The Central Command Historical-Cultural Advisory Group

DTIC Science & Technology

2013-10-22

other provision of law , no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a...ol Defame (000] together m an «Hon to :tqprmt cultural and heritage WWriawi ol m.iit»r> Benenne I .»»«rating ■■■■«■i BUtufepf tneUeMeri&aHi...ROTC curriculum dealing with the Law of Warfare, in which the issue of Cultural Property Protection is addressed with reference to the U.S

Design of Dielectric-Loaded Circumferential Slot Antennas of Arbitrary Size for Conical and Cylindrical Bodies

DTIC Science & Technology

1974-09-01

designed in the surface of small or large dielectric structures and results in durable antennas that may operate in the UHF or microwave frequerncy...in tne guide is given by g g =, o (i) 0c 1Moreno, T. Microwave Transmission Design Data, McGraw-Hill Book Co., N.Y., 1948. 2 Sevenson, A. F., Jr...size and a high Q that makes it useful in the UHF and microwave frequency regions. Such a resonant cavity is shown in figure 1. Normally, waveguide

The Evolution and Breakdown to Turbulence of a Wave Packet Propagating in a Laminar Boundary Layer

DTIC Science & Technology

1981-12-01

F .0RAGENCY REPORT NJUBER BLDC " 1, Is L D C --’ I l / t A DC 20)332--< 48 1. SUPtlkMUTARY NOTES 12. ISTRUUTIONIAVALAUITY STATEMENT 12t. DiSTR.UTION...dependent on the spectrum of the wavetrain, and hence on that of the excitation provided by the environment. Observations for a ranwe of controlled ...excitation amplitude. Control o h the amplitude and tne duration of the electrical pulse fee, i r the driving earohone is provJed ty a micro-computer

Proceedings of the RADC Spectrum Estimation Workshop (2nd) held 3, 4, & 5 October 1979, Griffiss AFB, NY

DTIC Science & Technology

1979-10-01

However, this author’s ex- perience has shown that most order selection rules , including Akaike’s, are not enough to be effeutive against the line splitting...and reduced spectral peak frequency estimation biases. A set of sensitive stopping rules for order se- L lection has been found for the algorithm...7] as the rule for order selection, the minimum FPE of the 41-point sequence with tne Burg algorithm was found at order 23. The AR spectrum based on

Audit Report. Quick-Reaction Report on the Review of Defense Base Closure and Realignment Budget Data for Carswell, Barksdale, Dyess, Minot, and Tinker Air Force Bases

DTIC Science & Technology

1992-11-27

for 10 construction pr~n’jcts for realigning Carswell AFB was not adeauately documented as required by Air Force Regulation (AFR) 86-1, " Programming ...Engineering Programming , Standard Facility Requirements." paragraph 24-70, allows for a total of 25,200 square feet of space for tne warehouse and...cantonment area, and replaced an existing capability. The fact that a replacement wash rack was previously programmed does not alter this requirement

U.S. Maritime Strategy In a Post-Cold War World?

DTIC Science & Technology

1990-05-16

worlo in wnIcn zne East-West squarea off across an iron curzain nas oeen- aramatically transformed . A chain reaction of nhslocic events in Eastern Europe...research will n e to exam int- tne -- ri :.me Componen t ot the Un itec St ates Natioanal M ~r z a , egov ,71tni1n the context of the changing geoo~o...experience. 12 :Zi. Historical BacKqrouna By maritime strategy we mean the principies wnicn govern a war in which the sea is a suostantia! factor. Naval

Development of Computer Program TWTVA for Calculation of 3-D Electron Trajectories in Coupled-Cavity TWTs

DTIC Science & Technology

1976-03-10

ferrule corner radius, which is typically not more than about 20). The radial mesh numbers are denoted NQR for the rf vector potential and Njyro for...the magnetic vector poten- tial matrices. Allowing for tne guard rows, the matrices run from -1 to NQR + 1, and -1 to Nj^ + 1. When the program...CR * 5» an(^ only one row ^or NCR up to 10, which covers most cases; as stated ir. the introduction, we do not expect NQR ever to exceed 20. For n

Radio Engineering System of Short-Range Navigation. RSBN-2. Part 2, (Radioteknicheskaya Sistema Blizhney Navigatisii RSBN-2)

DTIC Science & Technology

1977-02-09

windings of transformers Tp1 and TP2 through the switch VI. Constructions. Unit is assembled on L-shaped steel chassis/linding 93ar (Fig. 134). The cell...obtained square pulses are DOC = 77140017 PAGE differentiated by circuit C9 , H13 , they are amplified by pulse amplifier and are supplied to the starting...cscoud by screw coveL for i protection from dirt. the colas of groundiinq are DOC = 77150017 PAGE5 7 placed in cabinet with entrenching tool . ~Tne

Kalman Filter Time Series Analysis of Gamma-Ray Data from NaI(T1) Detectors for the ND6620 Computer.

DTIC Science & Technology

1985-05-08

J ., ,mi. m- n.I Illa IdI~ikll di I I I " i~i l ll . . ... " " . .... " ". . . . .. MIDAS FORTRAN IV 21 DEC...Washington, D.C. ApprovedI for publi releasec. di ~tiiin unlimited . ... ... ... . . . . . . . . . . . . . . S~ i’C a;s (,.-,ON OF _45 S ACE REPORT...be gaussian with a mean of zero and covariance Rk wnich is known or can oe estimated. Tne oehavior of the source between times k and K+ l is assumed

Studying the Earth from space

USGS Publications Warehouse

,

1974-01-01

Pictures of the Earth's surface obtained from satellites are providing scientists with new tools to investigate tne Earth and its environment. A growing population and an everexpanding technology place demands on our natural resources. However, man can no longer treat his resources strictly according to immediate economic dictates; a balance must be struck between the short-term demands of technological and industrial development and the long-term effects on the environment. Intelligent development, management, and conservation of resources are goals that represent an unprecedented challenge in the acquisition and use of information.

Assessing the Madigan Effort: Capitation, Purple Suits, CHAMPUS (Civilian Health and Medical Program of the Uniformed Services) and Other Issues.

DTIC Science & Technology

1980-04-01

added to the RCBCC membership, bringing the membership to a total of eight medical and dental organizations. The power vested in this committee is...utilization of the -oS above powers IS &cc;4,4l isred semi-democratically in that "tne senior Medical Corps officer froth each service will have one... Endometriosis 1 1625 --- 1625 Other diseases of the uterus 1 318 --- 318 Intermenstrual bleeding 2 1373 466-907 686 Unspecified ectopic pregnancy, without

Constructing of Research-Oriented Learning Mode Based on Network Environment

ERIC Educational Resources Information Center

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

2007-01-01

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

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

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

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

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

PubMed

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

2014-05-01

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

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

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

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

ERIC Educational Resources Information Center

Miquel, Ester; Duran, David

2017-01-01

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

The Integration of Personal Learning Environments & Open Network Learning Environments

ERIC Educational Resources Information Center

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

2012-01-01

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

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

ERIC Educational Resources Information Center

Cohen, Moshe; And Others

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

Cessation of Alcohol Consumption Decreases Rate of Nicotine Metabolism In Male Alcohol-Dependent Smokers#

PubMed Central

Gubner, Noah R.; Kozar-Konieczna, Aleksandra; Szoltysek-Boldys, Izabela; Slodczyk-Mankowska, Ewa; Goniewicz, Jerzy; Sobczak, Andrzej; Jacob, Peyton; Benowitz, Neal L.; Goniewicz, Maciej L.

2016-01-01

Background Rate of nicotine metabolism is an important factor influencing cigarette smoking behavior, dependence, and efficacy of nicotine replacement therapy. The current study examined the hypothesis that chronic alcohol abuse can accelerate the rate of nicotine metabolism. Nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism) and patterns of nicotine metabolites were assessed at three time points after alcohol cessation. Methods Participants were 22 Caucasian men randomly selected from a sample of 165 smokers entering a 7-week alcohol dependence treatment program in Poland. Data were collected at three time points: baseline (week 1, after acute alcohol detoxification), week 4, and week 7. Urine was analyzed for nicotine and metabolites and used to determine the nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism), and total nicotine equivalents (TNE, a biomarker for total daily nicotine exposure). Results and conclusions There was a significant decrease in urine NMR over the 7 weeks after alcohol abstinence (F(2,42)=18.83, p<0.001), indicating a decrease in rate of nicotine metabolism. On average NMR decreased 50.0% from baseline to week 7 (9.6 ± 1.3 vs. 4.1 ± 0.6). There was no change in urine TNE across the three sessions, indicating no change daily nicotine intake. The results support the idea that chronic alcohol abuse may increases the rate of nicotine metabolism, which then decreases over time after alcohol cessation. This information may help to inform future smoking cessation interventions in this population. PMID:27107849

Cessation of alcohol consumption decreases rate of nicotine metabolism in male alcohol-dependent smokers.

PubMed

Gubner, Noah R; Kozar-Konieczna, Aleksandra; Szoltysek-Boldys, Izabela; Slodczyk-Mankowska, Ewa; Goniewicz, Jerzy; Sobczak, Andrzej; Jacob, Peyton; Benowitz, Neal L; Goniewicz, Maciej L

2016-06-01

Rate of nicotine metabolism is an important factor influencing cigarette smoking behavior, dependence, and efficacy of nicotine replacement therapy. The current study examined the hypothesis that chronic alcohol abuse can accelerate the rate of nicotine metabolism. Nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism) and patterns of nicotine metabolites were assessed at three time points after alcohol cessation. Participants were 22 Caucasian men randomly selected from a sample of 165 smokers entering a 7-week alcohol dependence treatment program in Poland. Data were collected at three time points: baseline (week 1, after acute alcohol detoxification), week 4, and week 7. Urine was analyzed for nicotine and metabolites and used to determine the nicotine metabolite ratio (NMR, a biomarker for rate of nicotine metabolism), and total nicotine equivalents (TNE, a biomarker for total daily nicotine exposure). There was a significant decrease in urine NMR over the 7 weeks after alcohol abstinence (F(2,42)=18.83, p<0.001), indicating a decrease in rate of nicotine metabolism. On average NMR decreased 50.0% from baseline to week 7 (9.6±1.3 vs 4.1±0.6). There was no change in urine TNE across the three sessions, indicating no change daily nicotine intake. The results support the idea that chronic alcohol abuse may increase the rate of nicotine metabolism, which then decreases over time after alcohol cessation. This information may help to inform future smoking cessation interventions in this population. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

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

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

PubMed

Lin, C T; Jou, C P

2000-01-01

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

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

ERIC Educational Resources Information Center

Bottrup, Pernille

2005-01-01

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

How Neural Networks Learn from Experience.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

1992-01-01

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

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Quantitative learning strategies based on word networks

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

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

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

PubMed

Fung, Wai-keung; Liu, Yun-hui

2003-12-01

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

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

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

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

The Structural Underpinnings of Policy Learning: A Classroom Policy Simulation

NASA Astrophysics Data System (ADS)

Bird, Stephen

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

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

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

Rethinking the learning of belief network probabilities

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

Musick, R.

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

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

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

Learning in Artificial Neural Systems

NASA Technical Reports Server (NTRS)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

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

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

ERIC Educational Resources Information Center

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

2007-01-01

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

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

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

ERIC Educational Resources Information Center

Transue, Beth M.

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Three learning phases for radial-basis-function networks.

PubMed

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

2001-05-01

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

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

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

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

PubMed

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

2017-08-01

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

The Pattern of Soviet Conduct in the Third World, Review and Preview. Part 2

DTIC Science & Technology

1983-03-07

be quoted or copied without approval from OSD/Net Assessment. Aooession For DTIC TAB El Dit I-’I t Y " c•doi ;Avi.- ± end/or L i :itst I pc l 4•al IA...successcors have stooc, y that cecision. (Moscow odes riot recconioze tnis action.) Alth•cuph revolutionary Iran has i nioroved or estaolisned relations...far nesw tne world rar.et ievel and oepat, repotiaiions for a orice increase of roucniv t0w oercenz. ? y marc- I5d. witm the Soviets’ best otfer stiil

Sabers, Lances, B-17s and F-105s: An Essay on the Human Element, Napoleonic Warfare, and Air Combat.

DTIC Science & Technology

1987-04-01

AD~~AB8 SERS LAKCES 9-175 S F-1SSS: AN ESSAY ON TNE HUMAN 1/1 COMMANDAND STAFF COLL MAXWELL AFD AL. M K WELLS APR *? UNCLASSIFIED ACSC-B go69 F/0 15...available to any writer on this subject. This document is the property of the United States Government. It is available for distribution to the general...Air Command and Staff College." -- All reproduced copies must contain the name( s ) of the report’s author( s ). -- If format modification is necessary to

Thermodynamic and NMR Studies of Solvent Effect on Enantiomeric Recognition for a Chiral Organiz Ammonium Cation by Chiral Diketopyridino-18- Crown-6 Type Ligands at 25.0 deg C.

DTIC Science & Technology

1994-05-11

analyses, 1H NM&, and JR specPit oscopy. The purities were also determined quantitatively by a thermometric titration technique. 19 By titrting...enantiomers of NapEt with 18- crown-6 (SIGMA Chemical Company, its purity was 99.5% as determined by therm--ometric titration agtains a standard NaBr...isoperibol titration calorimetry at 25.0 - 0.1oC in CACi6CHOH solvent mixtures. The initial solution volume in the dewar was 20 mL Tne calorimeter (wac

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

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

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

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

Learning as Issue Framing in Agricultural Innovation Networks

ERIC Educational Resources Information Center

Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

2014-01-01

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

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

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

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

ERIC Educational Resources Information Center

Harding, Ansie; Engelbrecht, Johann

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

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

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

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

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

PubMed Central

Miconi, Thomas

2017-01-01

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

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

PubMed

Miconi, Thomas

2017-02-23

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

How and What Do Academics Learn through Their Personal Networks

ERIC Educational Resources Information Center

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

2015-01-01

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

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

ERIC Educational Resources Information Center

Iowa Department of Education, 2014

2014-01-01

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

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

ERIC Educational Resources Information Center

McDonald, Joseph P.; Klein, Emily J.

2003-01-01

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

Network reciprocity by coexisting learning and teaching strategies

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Brede, Markus; Yamauchi, Atsuo

2012-03-01

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

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

ERIC Educational Resources Information Center

Jamaludin, Azilawati; Shaari, Imran

2016-01-01

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

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

PubMed

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

2018-02-01

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

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

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

Just the Facts: Personal Learning Networks

ERIC Educational Resources Information Center

Nussbaum-Beach, Sheryl

2013-01-01

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

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

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

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

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

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

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

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

PubMed

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

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

VLSI Implementation of Neuromorphic Learning Networks

DTIC Science & Technology

1993-03-31

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Learning oncogenetic networks by reducing to mixed integer linear programming.

PubMed

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

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

ERIC Educational Resources Information Center

Sackey, Shera Carter

2012-01-01

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

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-04-01

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

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

ERIC Educational Resources Information Center

Álvarez Valencia, José Aldemar

2016-01-01

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

Tobacco toxicant exposure in cigarette smokers who use or do not use other tobacco products.

PubMed

Nollen, Nicole L; Mayo, Matthew S; Clark, Lauren; Cox, Lisa Sanderson; Khariwala, Samir S; Pulvers, Kim; Benowitz, Neal L; Ahluwalia, Jasjit S

2017-10-01

Non-cigarette other tobacco products (OTP; e.g., cigarillos, little cigars) are typically used in combination with cigarettes, but limited data exists on the tobacco toxicant exposure profiles of dual cigarette-OTP (Cig-OTP) users. This study examined biomarkers of nicotine and carcinogen exposure in cigarette smokers who used or did not use OTP. 111 Cig-OTP and 111 cigarette only (Cig Only) users who smoked equivalent cigarettes per day were matched on age (< 40, >=40), race (African American, White), and gender. Participants reported past 7-day daily use of cigarettes and OTP and provided urine for nicotine, cotinine, total nicotine equivalents (TNE) and total NNAL concentrations. Cig-OTP users reported greater past 7-day tobacco use (15.9 versus 13.0 products/day, p<0.01) but had significantly lower creatinine-normalized nicotine (606 versus 1301ng/mg), cotinine (1063 versus 2125ng/mg), TNE (28 versus 57 nmol/mg) and NNAL (251 versus 343pg/mg) than Cig Only users (p<0.001). Cig-OTP users had lower levels of nicotine and metabolites of a lung carcinogen relative to Cig-Only users, but concentrations of toxicants among Cig-OTP users were still at levels that place smokers at great risk from the detrimental health effects of smoking. Our study finds that nicotine and carcinogen exposure in Cig-OTP users are lower compared to cigarette only users, but still likely to be associated with substantial harm. A better understanding of why toxicant levels may be lower in Cig-OTP is an important area for future study. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2015-08-01

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

How to Trigger Emergence and Self-Organisation in Learning Networks

NASA Astrophysics Data System (ADS)

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

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

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

PubMed

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

2016-01-01

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

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

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

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

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

NASA Astrophysics Data System (ADS)

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

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

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

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

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

PubMed

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

2016-10-01

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

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

PubMed

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

2018-06-19

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

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

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

Distance Learning in a Multimedia Networks Project: Main Results.

ERIC Educational Resources Information Center

Ruokamo, Heli; Pohjolainen, Seppo

2000-01-01

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

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

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

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

Knowledgeable Lemurs Become More Central in Social Networks.

PubMed

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

2018-04-23

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

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

DTIC Science & Technology

2007-06-01

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

Benefits of Cooperative Learning in Weblog Networks

ERIC Educational Resources Information Center

Wang, Jenny; Fang, Yuehchiu

2005-01-01

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

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

ERIC Educational Resources Information Center

Dafoulas, Georgios; Shokri, Azam

2016-01-01

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

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

PubMed Central

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

2017-01-01

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

The application of network teaching in applied optics teaching

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

NASA Astrophysics Data System (ADS)

Miller, Robert D.

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

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

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

2018-06-21

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

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

ERIC Educational Resources Information Center

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

2018-01-01

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

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

ERIC Educational Resources Information Center

Ackland, Aileen; Swinney, Ann

2015-01-01

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

Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

ERIC Educational Resources Information Center

Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

2015-01-01

The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

Edmodo social learning network for elementary school mathematics learning

NASA Astrophysics Data System (ADS)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

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

Military Police Authority over Civilians. They look like Police, They Act like Police, But Are They Police

DTIC Science & Technology

1978-06-09

ndsc.t.=. accomplishing tZheize-r missLon toantaa n aw W-c order on m a- ry instaa- tiors. Tne proposed sta t=rv l -nguage -D -e d di dnce b" specifically...Military Moie.........10 Loss of Court-Matial jurisdiction Over Civilian4s . . . 10 Prohibitions Against Execution of Civil l ~aws by Soldiers...have only the authority of - a Ti---k---- 3 10a~~z~ ~ry c t ens- to er c!tizes onli nr ci e in law enforcement against other citizens. 1fT. irr l ’fL

A New Look at Offshore Assembly: The Internationalization of Industry,

DTIC Science & Technology

1981-03-01

7oissy in ’exico City. 17:. irnesto Calderon, " Las maa.uiladaras de los 2aises Centr~leS jue operan en el Zercer v~undo,’ in 1jjjjjL2 Lecturas 171...for eien 1o.cr). A3out 15 percent of all worters ani 2 percent of tne unJ.ille,l receive’i Mor than the minimu:m ( La ~uir, Lecturas 3 S;.;:. 3, i l e...ienJez, p. 142, and BstamnIte, p. 191. LO Sonica-Zvaira G~siitiril1 " La ruerza Ja lraajo. . . 11 in jjgj iia Ljj, Lecturas de C~T~!(nl I. , Ca !9E

Vision Tests For Medical Surveillance Or To Insure Job Fitness

NASA Astrophysics Data System (ADS)

Wolbarsht, M. L.; Landers, M. B.

1986-05-01

The rationale for designing screening type eye examinations to document visual capabilities for specific jobs or changes in visual function following exposure to specific ocular hazards is discussed. Possible applications to clinical situations are also discussed. Specific tests meeting requirements of definite end point quantification, ease of administration, and reproducibility are given for contrast (glare) sensitivity, distortions in macular imaging (Amsler grid), and color vision. The selection is aetailed for tne individual test combinations of various populations such as automobile uriver license applicants, visual display operators, and persons exposed to lasers, including military as well as non-military installers and repairers of optical fibers for communications.

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.

Towards a Social Networks Model for Online Learning & Performance

ERIC Educational Resources Information Center

Chung, Kon Shing Kenneth; Paredes, Walter Christian

2015-01-01

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

Social Networks, Communication Styles, and Learning Performance in a CSCL Community

ERIC Educational Resources Information Center

Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

2007-01-01

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

Networked Learning for Agricultural Extension: A Framework for Analysis and Two Cases

ERIC Educational Resources Information Center

Kelly, Nick; Bennett, John McLean; Starasts, Ann

2017-01-01

Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)- mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

Neural networks for self-learning control systems

NASA Technical Reports Server (NTRS)

Nguyen, Derrick H.; Widrow, Bernard

1990-01-01

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Lifelong Learning in German Learning Cities/Regions

ERIC Educational Resources Information Center

Reghenzani-Kearns, Denise; Kearns, Peter

2012-01-01

This paper traces the policies and lessons learned from two consecutive German national programs aimed at developing learning cities/regions. Known as Learning Regions Promotion of Networks, this first program transitioned into the current program, Learning on Place. A case study chosen is from the Tolzer region where a network has self-sustained…

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

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

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

PubMed

Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

2017-02-01

Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

Exploring Practice-Research Networks for Critical Professional Learning

ERIC Educational Resources Information Center

Appleby, Yvon; Hillier, Yvonne

2012-01-01

This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

Graduate Employability: The Perspective of Social Network Learning

ERIC Educational Resources Information Center

Chen, Yong

2017-01-01

This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

Learning polynomial feedforward neural networks by genetic programming and backpropagation.

PubMed

Nikolaev, N Y; Iba, H

2003-01-01

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.

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

PubMed

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

2013-09-01

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

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Co-Operative Learning and Development Networks.

ERIC Educational Resources Information Center

Hodgson, V.; McConnell, D.

1995-01-01

Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

A Statewide Service Learning Network Ignites Teachers and Students.

ERIC Educational Resources Information Center

Monsour, Florence

Service learning, curriculum-linked community service, has proved remarkably effective in igniting students' desire to learn. In 1997, the Wisconsin Partnership in Service Learning was initiated as a cross-disciplinary, cross-institutional endeavor. Supported by a grant from Learn and Serve America, the partnership created a network throughout…

Scaffolding in Connectivist Mobile Learning Environment

ERIC Educational Resources Information Center

Ozan, Ozlem

2013-01-01

Social networks and mobile technologies are transforming learning ecology. In this changing learning environment, we find a variety of new learner needs. The aim of this study is to investigate how to provide scaffolding to the learners in connectivist mobile learning environment: (1) to learn in a networked environment; (2) to manage their…

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

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

ERIC Educational Resources Information Center

Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

2011-01-01

The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

The Practices of Student Network as Cooperative Learning in Ethiopia

ERIC Educational Resources Information Center

Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

2015-01-01

Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

Hypermedia-Assisted Instruction and Second Language Learning: A Semantic-Network-Based Approach.

ERIC Educational Resources Information Center

Liu, Min

This literature review examines a hypermedia learning environment from a semantic network basis and the application of such an environment to second language learning. (A semantic network is defined as a conceptual representation of knowledge in human memory). The discussion is organized under the following headings and subheadings: (1) Advantages…

The STIN in the Tale: A Socio-Technical Interaction Perspective on Networked Learning

ERIC Educational Resources Information Center

Walker, Steve; Creanor, Linda

2009-01-01

In this paper, we go beyond what have been described as "mechanistic" accounts of e-learning to explore the complexity of relationships between people and technology as encountered in cases of networked learning. We introduce from the social informatics literature the concept of sociotechnical interaction networks which focus on the…

Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

ERIC Educational Resources Information Center

Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

2017-01-01

Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

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

ERIC Educational Resources Information Center

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

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

ERIC Educational Resources Information Center

Maglajlic, Seid

2016-01-01

This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

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

ERIC Educational Resources Information Center

Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

2012-01-01

Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

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

ERIC Educational Resources Information Center

Sarkar, Nurul I.

2013-01-01

Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

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

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

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

Toolkits and Libraries for Deep Learning.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Deep learning for computational chemistry.

PubMed

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Deep learning for computational chemistry

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

Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. Inmore » this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.« less

Identifying Gatekeepers in Online Learning Networks

ERIC Educational Resources Information Center

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

NASA Astrophysics Data System (ADS)

Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

2018-05-01

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

The research of "blind" spot in the LVQ network

NASA Astrophysics Data System (ADS)

Guo, Zhanjie; Nan, Shupo; Wang, Xiaoli

2017-04-01

Nowadays competitive neural network has been widely used in the pattern recognition, classification and other aspects, and show the great advantages compared with the traditional clustering methods. But the competitive neural networks still has inadequate in many aspects, and it needs to be further improved. Based on the learning Vector Quantization Network proposed by Learning Kohonen [1], this paper resolve the issue of the large training error, when there are "blind" spots in a network through the introduction of threshold value learning rules and finally programs the realization with Matlab.

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

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

PubMed

Sadeghi, Zahra

2016-09-01

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

Designing a holistic end-to-end intelligent network analysis and security platform

NASA Astrophysics Data System (ADS)

Alzahrani, M.

2018-03-01

Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Functional connectivity changes in second language vocabulary learning.

PubMed

Ghazi Saidi, Ladan; Perlbarg, Vincent; Marrelec, Guillaume; Pélégrini-Issac, Mélani; Benali, Habib; Ansaldo, Ana-Inés

2013-01-01

Functional connectivity changes in the language network (Price, 2010), and in a control network involved in second language (L2) processing (Abutalebi & Green, 2007) were examined in a group of Persian (L1) speakers learning French (L2) words. Measures of network integration that characterize the global integrative state of a network (Marrelec, Bellec et al., 2008) were gathered, in the shallow and consolidation phases of L2 vocabulary learning. Functional connectivity remained unchanged across learning phases for L1, whereas total, between- and within-network integration levels decreased as proficiency for L2 increased. The results of this study provide the first functional connectivity evidence regarding the dynamic role of the language processing and cognitive control networks in L2 learning (Abutalebi, Cappa, & Perani, 2005; Altarriba & Heredia, 2008; Leonard et al., 2011; Parker-Jones et al., 2011). Thus, increased proficiency results in a higher degree of automaticity and lower cognitive effort (Segalowitz & Hulstijn, 2005). Copyright © 2012 Elsevier Inc. All rights reserved.

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

PubMed Central

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task. PMID:29441027

Convergence analysis of sliding mode trajectories in multi-objective neural networks learning.

PubMed

Costa, Marcelo Azevedo; Braga, Antonio Padua; de Menezes, Benjamin Rodrigues

2012-09-01

The Pareto-optimality concept is used in this paper in order to represent a constrained set of solutions that are able to trade-off the two main objective functions involved in neural networks supervised learning: data-set error and network complexity. The neural network is described as a dynamic system having error and complexity as its state variables and learning is presented as a process of controlling a learning trajectory in the resulting state space. In order to control the trajectories, sliding mode dynamics is imposed to the network. It is shown that arbitrary learning trajectories can be achieved by maintaining the sliding mode gains within their convergence intervals. Formal proofs of convergence conditions are therefore presented. The concept of trajectory learning presented in this paper goes further beyond the selection of a final state in the Pareto set, since it can be reached through different trajectories and states in the trajectory can be assessed individually against an additional objective function. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

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

PubMed Central

Rittner, Jessica Levin; Johnson, Karin E.; Gerteis, Jessie; Miller, Therese

2017-01-01

Objective: Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Data sources/study setting: Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. Study design: The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. Data collection: The authors reviewed detailed notes from the interviews to distill salient themes. Principal findings: Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. Conclusion: The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. PMID:28277202

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

ERIC Educational Resources Information Center

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

2017-01-01

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

Practices and Strategies of Self-Initiated Language Learning in an Online Social Network Discussion Forum: A Descriptive Case Study

ERIC Educational Resources Information Center

Hsieh, Hsiu-Wei

2012-01-01

The proliferation of information and communication technologies and the prevalence of online social networks have facilitated the opportunities for informal learning of foreign languages. However, little educational research has been conducted on how individuals utilize those social networks to take part in self-initiated language learning without…

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

ERIC Educational Resources Information Center

Ferreday, Debra; Hodgson, Vivien; Jones, Chris

2006-01-01

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

The Fire Learning Network: A promising conservation strategy for forestry

Treesearch

Bruce E. Goldstein; William H. Butler; R. Bruce Hull

2010-01-01

Conservation Learning Networks (CLN) are an emerging conservation strategy for addressing complex resource management challenges that face the forestry profession. The US Fire Learning Network (FLN) is a successful example of a CLN that operates on a national scale. Developed in 2001 as a partnership between The Nature Conservancy, the US Forest Service, and land-...

Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

ERIC Educational Resources Information Center

Engelthaler, Tomas; Hills, Thomas T.

2017-01-01

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…

Professional Online Presence and Learning Networks: Educating for Ethical Use of Social Media

ERIC Educational Resources Information Center

Forbes, Dianne

2017-01-01

In a teacher education context, this study considers the use of social media for building a professional online presence and learning network. This article provides an overview of uses of social media in teacher education, presents a case study of key processes in relation to professional online presence and learning networks, and highlights…

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

PubMed

Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

2017-03-01

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

A neural network model for credit risk evaluation.

PubMed

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Workplace Learning in Informal Networks

ERIC Educational Resources Information Center

Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

2014-01-01

Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…

Professional Learning Networks Designed for Teacher Learning

ERIC Educational Resources Information Center

Trust, Torrey

2012-01-01

In the information age, students must learn to navigate and evaluate an expanding network of information. Highly effective teachers model this process of information analysis and knowledge acquisition by continually learning through collaboration, professional development, and studying pedagogical techniques and best practices. Many teachers have…

Interprofessional practice and learning in a youth mental health service: A case study using network analysis.

PubMed

Barnett, Tony; Hoang, Ha; Cross, Merylin; Bridgman, Heather

2015-01-01

Few studies have examined interprofessional practice (IPP) from a mental health service perspective. This study applied a mixed-method approach to examine the IPP and learning occurring in a youth mental health service in Tasmania, Australia. The aims of the study were to investigate the extent to which staff were networked, how collaboratively they practiced and supported student learning, and to elicit the organisation's strengths and opportunities regarding IPP and learning. Six data sets were collected: pre- and post-test readiness for interprofessional learning surveys, Social Network survey, organisational readiness for IPP and learning checklist, "talking wall" role clarification activity, and observations of participants working through a clinical case study. Participants (n = 19) were well-networked and demonstrated a patient-centred approach. Results confirmed participants' positive attitudes to IPP and learning and identified ways to strengthen the organisation's interprofessional capability. This mixed-method approach could assist others to investigate IPP and learning.

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

PubMed

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

2013-06-01

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

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

PubMed Central

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

2014-01-01

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

Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

PubMed

Xing, Youlu; Shen, Furao; Zhao, Jinxi

2016-03-01

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.

A European Languages Virtual Network Proposal

NASA Astrophysics Data System (ADS)

García-Peñalvo, Francisco José; González-González, Juan Carlos; Murray, Maria

ELVIN (European Languages Virtual Network) is a European Union (EU) Lifelong Learning Programme Project aimed at creating an informal social network to support and facilitate language learning. The ELVIN project aims to research and develop the connection between social networks, professional profiles and language learning in an informal educational context. At the core of the ELVIN project, there will be a web 2.0 social networking platform that connects employees/students for language practice based on their own professional/academic needs and abilities, using all relevant technologies. The ELVIN remit involves the examination of both methodological and technological issues inherent in achieving a social-based learning platform that provides the user with their own customized Personal Learning Environment for EU language acquisition. ELVIN started in November 2009 and this paper presents the project aims and objectives as well as the development and implementation of the web platform.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

PubMed

Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

2013-11-01

The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Predicting the survival of diabetes using neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Data mining techniques at the present time are used in predicting diseases of health care industries. Neural Network is one among the prevailing method in data mining techniques of an intelligent field for predicting diseases in health care industries. This paper presents a study on the prediction of the survival of diabetes diseases using different learning algorithms from the supervised learning algorithms of neural network. Three learning algorithms are considered in this study: (i) The levenberg-marquardt learning algorithm (ii) The Bayesian regulation learning algorithm and (iii) The scaled conjugate gradient learning algorithm. The network is trained using the Pima Indian Diabetes Dataset with the help of MATLAB R2014(a) software. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is further computed to validate the accurate prediction

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

PubMed

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

Spatial features of synaptic adaptation affecting learning performance.

PubMed

Berger, Damian L; de Arcangelis, Lucilla; Herrmann, Hans J

2017-09-08

Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.

Fast detection of the fuzzy communities based on leader-driven algorithm

NASA Astrophysics Data System (ADS)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

Environmental Design for a Structured Network Learning Society

ERIC Educational Resources Information Center

Chang, Ben; Cheng, Nien-Heng; Deng, Yi-Chan; Chan, Tak-Wai

2007-01-01

Social interactions profoundly impact the learning processes of learners in traditional societies. The rapid rise of the Internet using population has been the establishment of numerous different styles of network communities. Network societies form when more Internet communities are established, but the basic form of a network society, especially…

Using Social Networks to Create Powerful Learning Communities

ERIC Educational Resources Information Center

Lenox, Marianne; Coleman, Maurice

2010-01-01

Regular readers of "Computers in Libraries" are aware that social networks are forming increasingly important linkages to professional and personal development in all libraries. Live and virtual social networks have become the new learning playground for librarians and library staff. Social networks have the ability to connect those who are…

Goals, Motivation for, and Outcomes of Personal Learning through Networks: Results of a Tweetstorm

ERIC Educational Resources Information Center

Sie, Rory L. L.; Pataraia, Nino; Boursinou, Eleni; Rajagopal, Kamakshi; Margaryan, Anoush; Falconer, Isobel; Bitter-Rijpkema, Marlies; Littlejohn, Allison; Sloep, Peter B.

2013-01-01

Recent developments in the use of social media for learning have posed serious challenges for learners. The information overload that these online social tools create has changed the way learners learn and from whom they learn. An investigation of learners' goals, motivations and expected outcomes when using a personal learning network is…

A Sharing Mind Map-Oriented Approach to Enhance Collaborative Mobile Learning with Digital Archiving Systems

ERIC Educational Resources Information Center

Chang, Jui-Hung; Chiu, Po-Sheng; Huang, Yueh-Min

2018-01-01

With the advances in mobile network technology, the use of portable devices and mobile networks for learning is not limited by time and space. Such use, in combination with appropriate learning strategies, can achieve a better effect. Despite the effectiveness of mobile learning, students' learning direction, progress, and achievement may differ.…

Learning in Structured Connectionist Networks

DTIC Science & Technology

1988-04-01

the structure is too rigid and learning too difficult for cognitive modeling. Two algorithms for learning simple, feature-based concept descriptions...and learning too difficult for cognitive model- ing. Two algorithms for learning simple, feature-based concept descriptions were also implemented. The...Term Goals Recent progress in connectionist research has been encouraging; networks have success- fully modeled human performance for various cognitive

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

ERIC Educational Resources Information Center

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,…

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

ERIC Educational Resources Information Center

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…

Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

ERIC Educational Resources Information Center

Wang, Jingying; Wen, Ming-Lee; Jou, Min

2016-01-01

Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

The Influence of Adult Learners' Self-Directed Learning Readiness and Network Literacy on Online Learning Effectiveness: A Study of Civil Servants in Taiwan

ERIC Educational Resources Information Center

Lai, Horng-Ji

2011-01-01

This study examined the effect of civil servants' Self-Directed Learning Readiness (SDLR) and network literacy on their online learning effectiveness in a web-based training program. Participants were 283 civil servants enrolled in an asynchronous online learning program through an e-learning portal provided by the Regional Civil Service…

An Analysis of Density and Degree-Centrality According to the Social Networking Structure Formed in an Online Learning Environment

ERIC Educational Resources Information Center

Ergün, Esin; Usluel, Yasemin Koçak

2016-01-01

In this study, we assessed the communication structure in an educational online learning environment using social network analysis (SNA). The communication structure was examined with respect to time, and instructor's participation. The course was implemented using ELGG, a network learning environment, blended with face-to-face sessions over a…

Language, Learning, and Identity in Social Networking Sites for Language Learning: The Case of Busuu

ERIC Educational Resources Information Center

Alvarez Valencia, Jose Aldemar

2014-01-01

Recent progress in the discipline of computer applications such as the advent of web-based communication, afforded by the Web 2.0, has paved the way for novel applications in language learning, namely, social networking. Social networking has challenged the area of Computer Mediated Communication (CMC) to expand its research palette in order to…

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

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

PubMed

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.

Model-free distributed learning

NASA Technical Reports Server (NTRS)

Dembo, Amir; Kailath, Thomas

1990-01-01

Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights. The perturbation may be either via noise sources or orthogonal signals. The invariance to detailed network structure mitigates large variability between supposedly identical networks as well as implementation defects. This local, regular, and completely distributed mechanism requires no central control and involves only a few global signals. Thus it allows for integrated on-chip learning in large analog and optical networks.

Control Theoretic Modeling for Uncertain Cultural Attitudes and Unknown Adversarial Intent

DTIC Science & Technology

2009-02-01

Constructive computational tools. 15. SUBJECT TERMS social learning, social networks , multiagent systems, game theory 16. SECURITY CLASSIFICATION OF: a...over- reactionary behaviors; 3) analysis of rational social learning in networks : analysis of belief propagation in social networks in various...general methodology as a predictive device for social network formation and for communication network formation with constraints on the lengths of

Neural networks supporting switching, hypothesis testing, and rule application

PubMed Central

Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S.; Seger, Carol A.

2015-01-01

We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example “choose the blue letter.” Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest learning speed late in the time course of learning. As subjects shifted from hypothesis testing to rule application, activity in this network decreased and activity in the somatomotor and default mode networks increased. PMID:26197092

Neural networks supporting switching, hypothesis testing, and rule application.

PubMed

Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S; Seger, Carol A

2015-10-01

We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest learning speed late in the time course of learning. As subjects shifted from hypothesis testing to rule application, activity in this network decreased and activity in the somatomotor and default mode networks increased. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed

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.

Building a Virtual Learning Network for Teachers in a Suburban School District

ERIC Educational Resources Information Center

Kurtzworth-Keen, Kristin A.

2011-01-01

Emerging research indicates that learning management systems such as Moodle can function as virtual, collaborative environments, where collegial interactions promote professional learning opportunities. This study deployed a mixed methods design in order to describe and analyze teacher participation in a virtual learning network (VLN) that was…

Learning Communities for Curriculum Change: Key Factors in an Educational Change Process in New Zealand

ERIC Educational Resources Information Center

Edwards, Frances

2012-01-01

Increasingly school change processes are being facilitated through the formation and operation of groups of teachers working together for improved student outcomes. These groupings are variously referred to as networks, networked learning communities, communities of practice, professional learning communities, learning circles or clusters. The…

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

PubMed

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.

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

PubMed Central

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

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% 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 data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

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

PubMed

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.

Reinforcement learning for routing in cognitive radio ad hoc networks.

PubMed

Al-Rawi, Hasan A A; Yau, Kok-Lim Alvin; Mohamad, Hafizal; Ramli, Nordin; Hashim, Wahidah

2014-01-01

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.

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

PubMed Central

Al-Rawi, Hasan A. A.; Mohamad, Hafizal; Hashim, Wahidah

2014-01-01

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. PMID:25140350

An Adaptive Resonance Theory account of the implicit learning of orthographic word forms.

PubMed

Glotin, H; Warnier, P; Dandurand, F; Dufau, S; Lété, B; Touzet, C; Ziegler, J C; Grainger, J

2010-01-01

An Adaptive Resonance Theory (ART) network was trained to identify unique orthographic word forms. Each word input to the model was represented as an unordered set of ordered letter pairs (open bigrams) that implement a flexible prelexical orthographic code. The network learned to map this prelexical orthographic code onto unique word representations (orthographic word forms). The network was trained on a realistic corpus of reading textbooks used in French primary schools. The amount of training was strictly identical to children's exposure to reading material from grade 1 to grade 5. Network performance was examined at each grade level. Adjustment of the learning and vigilance parameters of the network allowed us to reproduce the developmental growth of word identification performance seen in children. The network exhibited a word frequency effect and was found to be sensitive to the order of presentation of word inputs, particularly with low frequency words. These words were better learned with a randomized presentation order compared with the order of presentation in the school books. These results open up interesting perspectives for the application of ART networks in the study of the dynamics of learning to read. 2009 Elsevier Ltd. All rights reserved.

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

PubMed

Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

2008-12-01

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

NASA Astrophysics Data System (ADS)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Progress on a generalized coordinates tensor product finite element 3DPNS algorithm for subsonic

NASA Technical Reports Server (NTRS)

Baker, A. J.; Orzechowski, J. A.

1983-01-01

A generalized coordinates form of the penalty finite element algorithm for the 3-dimensional parabolic Navier-Stokes equations for turbulent subsonic flows was derived. This algorithm formulation requires only three distinct hypermatrices and is applicable using any boundary fitted coordinate transformation procedure. The tensor matrix product approximation to the Jacobian of the Newton linear algebra matrix statement was also derived. Tne Newton algorithm was restructured to replace large sparse matrix solution procedures with grid sweeping using alpha-block tridiagonal matrices, where alpha equals the number of dependent variables. Numerical experiments were conducted and the resultant data gives guidance on potentially preferred tensor product constructions for the penalty finite element 3DPNS algorithm.

Effect of Deregulation in the Telecommunications Industry on Military Base Telephone Communications.

DTIC Science & Technology

1983-03-01

a s stell11 n=etwcrk in srder to antar -,nt, the long d iS -c s market - In addiio n co he z ccal I e 1eph one ma~ke=t *whcre_ : ow opi:ates. Other...ra n out. By 1910 +he i-ndzp=-dans: (%:Dn-Bell) ele- ph.-one companias accounted fc=r. ne ar=l"y h ..f o f th4 - eieohcrn, * market , anI rates had...vazious ?ffc---;s of tne3 challz-cqers to ant er the stablished markets of thh, commcM Car:4=erS. 2 DurJig te thirties and f3_ i s s, t:hs only s

Measures and Trends U.S. and U.S.S.R. Strategic Force Effectiveness. (Sanitized)

DTIC Science & Technology

1978-07-01

is limited by the uncer- tainties and inaccracies present in the data which are used to develop the moasure. In the main body of the text the...alplizan1e to the prvostwu noas;urO5 arc ar;ici~to tn; ~~z~ ’i.r 2ctwo mt- azurer i-rmores tne fact that t Cftl4t.0 av’curacy -f I tnk~ atv :a;r le... azured in aitica1 nilios, of all thv straregic nuclear weaorjzf in CaC!. forCe iS CO c~aztd. V. (U) in order to utlijZe .he stanl1ard qra:pl.ic re

Learning for Work and Professional Development: The Significance of Informal Learning Networks of Digital Media Industry Professionals

ERIC Educational Resources Information Center

Campana, Joe

2014-01-01

Informal learning networks play a key role in the skill and professional development of professionals, working in micro-businesses within Australia's digital media industry, as they do not have access to learning and development or human resources sections that can assist in mapping their learning pathway. Professionals working in this environment…

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

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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…

Social Networking Tools and Teacher Education Learning Communities: A Case Study

ERIC Educational Resources Information Center

Poulin, Michael T.

2014-01-01

Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

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

ERIC Educational Resources Information Center

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…

Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review

ERIC Educational Resources Information Center

Cela, Karina L.; Sicilia, Miguel Ángel; Sánchez, Salvador

2015-01-01

E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at…

To Enhance Collaborative Learning and Practice Network Knowledge with a Virtualization Laboratory and Online Synchronous Discussion

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Kongcharoen, Chaknarin; Ghinea, Gheorghita

2014-01-01

Recently, various computer networking courses have included additional laboratory classes in order to enhance students' learning achievement. However, these classes need to establish a suitable laboratory where each student can connect network devices to configure and test functions within different network topologies. In this case, the Linux…

Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

PubMed

Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

2018-03-23

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

Detection of eardrum abnormalities using ensemble deep learning approaches

NASA Astrophysics Data System (ADS)

Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yua, Lianbo; Gurcan, Metin N.

2018-02-01

In this study, we proposed an approach to report the condition of the eardrum as "normal" or "abnormal" by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(+/- 12.1%) and 82.6% (+/- 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (+/- 10.3%).

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

PubMed

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.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

PubMed

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

PubMed Central

Gerstner, Wulfram

2017-01-01

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically. PMID:29173280

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

PubMed

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

2015-12-01

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

Foundation Year Students' Perceptions of Using Social Network Sites for Learning English in the Saudi Context

ERIC Educational Resources Information Center

AlShoaibi, Rana; Shukri, Nadia

2017-01-01

The major aim of this study is to better understand the university students' perceptions and attitudes towards using social network sites for learning English as well as to identify if there is a difference between male and female university students in terms of using social networking sites for learning English inside and outside the classroom.…

Deep Gate Recurrent Neural Network

DTIC Science & Technology

2016-11-22

Schmidhuber. A system for robotic heart surgery that learns to tie knots using recurrent neural networks. In IEEE International Conference on...tasks, such as Machine Translation (Bahdanau et al. (2015)) or Robot Reinforcement Learning (Bakker (2001)). The main idea behind these networks is to...and J. Peters. Reinforcement learning in robotics : A survey. The International Journal of Robotics Research, 32:1238–1274, 2013. ISSN 0278-3649. doi

Discriminative Learning with Markov Logic Networks

DTIC Science & Technology

2009-10-01

Discriminative Learning with Markov Logic Networks Tuyen N. Huynh Department of Computer Sciences University of Texas at Austin Austin, TX 78712...emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted...TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of Texas at Austin,Department of Computer

From Idea to Virtual Reality: ALADIN - The Adult Learning Documentation and Information Network. Report of a CONFINTEA V Workshop and Its Follow-Up.

ERIC Educational Resources Information Center

Giere, Ursula, Ed.; Imel, Susan, Ed.

This publication contains the story of how the idea for a network conceived through CONFINTEA V became a [virtual] reality in ALADIN, the Adult Learning Documentation and Information Network. Part I contains 15 papers delivered as a part of the CONFINTEA workshop, "Global Community of Adult Learning through Information and Documentation:…

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Query-based learning for aerospace applications.

PubMed

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

2003-01-01

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

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Evolution of Associative Learning in Chemical Networks

PubMed Central

McGregor, Simon; Vasas, Vera; Husbands, Phil; Fernando, Chrisantha

2012-01-01

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ‘memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells. PMID:23133353

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

PubMed Central

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

Explaining technological change of wind power in China and the United States: Roles of energy policies, technological learning, and collaboration

NASA Astrophysics Data System (ADS)

Tang, Tian

The following dissertation explains how technological change of wind power, in terms of cost reduction and performance improvement, is achieved in China and the US through energy policies, technological learning, and collaboration. The objective of this dissertation is to understand how energy policies affect key actors in the power sector to promote renewable energy and achieve cost reductions for climate change mitigation in different institutional arrangements. The dissertation consists of three essays. The first essay examines the learning processes and technological change of wind power in China. I integrate collaboration and technological learning theories to model how wind technologies are acquired and diffused among various wind project participants in China through the Clean Development Mechanism (CDM)--an international carbon trade program, and empirically test whether different learning channels lead to cost reduction of wind power. Using pooled cross-sectional data of Chinese CDM wind projects and spatial econometric models, I find that a wind project developer's previous experience (learning-by-doing) and industrywide wind project experience (spillover effect) significantly reduce the costs of wind power. The spillover effect provides justification for subsidizing users of wind technologies so as to offset wind farm investors' incentive to free-ride on knowledge spillovers from other wind energy investors. The CDM has played such a role in China. Most importantly, this essay provides the first empirical evidence of "learning-by-interacting": CDM also drives wind power cost reduction and performance improvement by facilitating technology transfer through collaboration between foreign turbine manufacturers and local wind farm developers. The second essay extends this learning framework to the US wind power sector, where I examine how state energy policies, restructuring of the electricity market, and learning among actors in wind industry lead to performance improvement of wind farms. Unlike China, the restructuring of the US electricity market created heterogeneity in transmission network governance across regions. Thus, I add transmission network governance to my learning framework to test the impacts of different transmission network governance models. Using panel data of existing utility-scale wind farms in US during 2001-2012 and spatial models, I find that the performance of a wind project is improved through more collaboration among project participants (learning-by-interacting), and this improvement is even greater if the wind project is interconnected to a regional transmission network coordinated by an independent system operator or a regional transmission organization (ISO/RTO). In the third essay, I further explore how different transmission network governance models affect wind power integration through a comparative case study. I compare two regional transmission networks, which represent two major transmission network governance models in the US: the ISO/RTO-governance model and the non-RTO model. Using archival data and interviews with key network participants, I find that a centralized transmission network coordinated through an ISO/RTO is more effective in integrating wind power because it allows resource pooling and optimal allocating of the resources by the central network administrative agency (NAO). The case study also suggests an alternative path to improved network effectiveness for a less cohesive network, which is through more frequent resource exchange among subgroups within a large network. On top of that, this essay contributes to the network governance literature by providing empirical evidence on the coexistence of hierarchy, market, and collaboration in complex service delivery networks. These coordinating mechanisms complement each other to provide system flexibility and stability, particularly when the network operates in a turbulent environment with changes and uncertainties.

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

PubMed

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.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

PubMed Central

Burbank, Kendra S.

2015-01-01

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. PMID:26633645

Reinforcement Learning of Two-Joint Virtual Arm Reaching in a Computer Model of Sensorimotor Cortex

PubMed Central

Neymotin, Samuel A.; Chadderdon, George L.; Kerr, Cliff C.; Francis, Joseph T.; Lytton, William W.

2014-01-01

Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement. PMID:24047323

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

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. 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. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Hybrid computing using a neural network with dynamic external memory.

PubMed

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

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

Recommending Peers for Learning: Matching on Dissimilarity in Interpretations to Provoke Breakdown

ERIC Educational Resources Information Center

Rajagopal, Kamakshi; van Bruggen, Jan M.; Sloep, Peter B.

2017-01-01

People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners' Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based…

Unlocking the Potential of Urban Communities: Case Studies of Twelve Learning Cities

ERIC Educational Resources Information Center

Valdés-Cotera, Raúl, Ed.; Longworth, Norman, Ed.; Lunardon, Katharina, Ed.; Wang, Mo, Ed.; Jo, Sunok, Ed.; Crowe, Sinéad, Ed.

2015-01-01

UNESCO established the UNESCO Global Network of Learning Cities (GNLC) to encourage the development of learning cities. By providing technical support, capacity development, and a platform where members can share ideas on policies and best practice, this international exchange network helps urban communities create thriving learning cities. The…

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

Synchronization of heteroclinic circuits through learning in coupled neural networks

NASA Astrophysics Data System (ADS)

Selskii, Anton; Makarov, Valeri A.

2016-01-01

The synchronization of oscillatory activity in neural networks is usually implemented by coupling the state variables describing neuronal dynamics. Here we study another, but complementary mechanism based on a learning process with memory. A driver network, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven network, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner can "copy" the coupling structure and thus synchronize oscillations with the teacher. The replication of the WLC dynamics occurs for intermediate memory lengths only, consequently, the learner network exhibits a phenomenon of learning resonance.

Person re-identification over camera networks using multi-task distance metric learning.

PubMed

Ma, Lianyang; Yang, Xiaokang; Tao, Dacheng

2014-08-01

Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

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.

Learning in stochastic neural networks for constraint satisfaction problems

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Adorf, Hans-Martin

1989-01-01

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

The ASE Improving Practical Work in Triple Science Learning Skills Network

ERIC Educational Resources Information Center

Barber, Paul; Chapman, Georgina; Ellis-Sackey, Cecilia; Grainger, Beth; Jones, Steve

2011-01-01

In July 2010, the Association for Science Education won a bid to run a "Sharing innovation network" for the Triple Science Support Programme, which is delivered by the Learning Skills Network on behalf of the Department for Education. The network involves schools from the London boroughs of Tower Hamlets and Greenwich. In this article,…

A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

ERIC Educational Resources Information Center

West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

2010-01-01

A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

Network Training for a Boy with Learning Disabilities and Behaviours That Challenge

ERIC Educational Resources Information Center

Cooper, Kate; McElwee, Jennifer

2016-01-01

Background: Network Training is an intervention that draws upon systemic ideas and behavioural principles to promote positive change in networks of support for people defined as having a learning disability. To date, there are no published case studies looking at the outcomes of Network Training. Materials and Methods: This study aimed to…

Designing a Self-Contained Group Area Network for Ubiquitous Learning

ERIC Educational Resources Information Center

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

2008-01-01

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

Social Network Innovation in the Internet's Global Coffee Houses: Designing a Mobile Help Seeking Tool in Learning Layers

ERIC Educational Resources Information Center

Cook, John; Santos, Patricia

2014-01-01

In this paper, we argue that there is much that we can learn from the past as we explore the issues raised when designing innovative social media and mobile technologies for learning. Like the social networking that took place in coffee houses in the 1600s, the Internet-enabled social networks of today stand accused of being the so-called…

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

NASA Astrophysics Data System (ADS)

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.

Learning characteristics of a space-time neural network as a tether skiprope observer

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1993-01-01

The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Learning characteristics of a space-time neural network as a tether skiprope observer

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1992-01-01

The Software Technology Laboratory at JSC is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

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.

Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

PubMed

Ammenwerth, Elske; Hackl, Werner O

2017-01-01

Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

PubMed Central

Tonelli, Paul; Mouret, Jean-Baptiste

2013-01-01

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities. PMID:24236099

Large-scale Cortical Network Properties Predict Future Sound-to-Word Learning Success

PubMed Central

Sheppard, John Patrick; Wang, Ji-Ping; Wong, Patrick C. M.

2013-01-01

The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants’ future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults. PMID:22360625

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Analysis and Visualization of Relations in eLearning

NASA Astrophysics Data System (ADS)

Dráždilová, Pavla; Obadi, Gamila; Slaninová, Kateřina; Martinovič, Jan; Snášel, Václav

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

Lessons Learned and Lessons To Be Learned: An Overview of Innovative Network Learning Environments.

ERIC Educational Resources Information Center

Jacobson, Michael J.; Jacobson, Phoebe Chen

This paper provides an overview of five innovative projects involving network learning technologies in the United States: (1) the MicroObservatory Internet Telescope is a collection of small, high-quality, and low-maintenance telescopes operated by the Harvard-Smithsonian Center for Astrophysics (Massachusetts), which may be used remotely via the…

Discovery of Action Patterns and User Correlations in Task-Oriented Processes for Goal-Driven Learning Recommendation

ERIC Educational Resources Information Center

Zhou, Xiaokang; Chen, Jian; Wu, Bo; Jin, Qun

2014-01-01

With the high development of social networks, collaborations in a socialized web-based learning environment has become increasing important, which means people can learn through interactions and collaborations in communities across social networks. In this study, in order to support the enhanced collaborative learning, two important factors, user…

Social Networks-Based Adaptive Pairing Strategy for Cooperative Learning

ERIC Educational Resources Information Center

Chuang, Po-Jen; Chiang, Ming-Chao; Yang, Chu-Sing; Tsai, Chun-Wei

2012-01-01

In this paper, we propose a grouping strategy to enhance the learning and testing results of students, called Pairing Strategy (PS). The proposed method stems from the need of interactivity and the desire of cooperation in cooperative learning. Based on the social networks of students, PS provides members of the groups to learn from or mimic…

Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

ERIC Educational Resources Information Center

Loiseau, Mathieu; Zourou, Katerina

2012-01-01

This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

Structuring and Regulating Collaborative Learning in Higher Education with Wireless Networks and Mobile Tools

ERIC Educational Resources Information Center

Jarvela, Sanna; Naykki, Piia; Laru, Jari; Luokkanen, Tiina

2007-01-01

In our recent research we have explored possibilities to scaffold collaborative learning in higher education with wireless networks and mobile tools. The pedagogical ideas are grounded on concepts of collaborative learning, including the socially shared origin of cognition, as well as self-regulated learning theory. This paper presents our three…

Effects of the ISIS Recommender System for Navigation Support in Self-Organised Learning Networks

ERIC Educational Resources Information Center

Drachsler, Hendrik; Hummel, Hans; van den Berg, Bert; Eshuis, Jannes; Waterink, Wim; Nadolski, Rob; Berlanga, Adriana; Boers, Nanda; Koper, Rob

2009-01-01

The need to support users of the Internet with the selection of information is becoming more important. Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research…

Creating and Sustaining Inquiry Spaces for Teacher Learning and System Transformation

ERIC Educational Resources Information Center

Kaser, Linda; Halbert, Judy

2014-01-01

Over a 15-year period, one Western Canadian province, British Columbia, has been exploring the potential of inquiry learning networks to deepen teacher professional learning and to influence the system as a whole. During this time, we have learned a great deal about shifting practice through inquiry networks. In this article, we provide a…

Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

ERIC Educational Resources Information Center

Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.

2010-01-01

Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

Enhancing Formal E-Learning with Edutainment on Social Networks

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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.…

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

ERIC Educational Resources Information Center

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…

Providing Hearing-Impaired Students with Learning Care after Classes through Smart Phones and the GPRS Network

ERIC Educational Resources Information Center

Liu, Chen-Chung; Hong, Yi-Ching

2007-01-01

Although computers and network technology have been widely utilised to assist students learn, few technical supports have been developed to help hearing-impaired students learn in Taiwan. A significant challenge for teachers is to provide after-class learning care and assistance to hearing-impaired students that sustain their motivation to…

Bridging the Gap between Students and Computers: Supporting Activity Awareness for Network Collaborative Learning with GSM Network

ERIC Educational Resources Information Center

Liu, C.-C.; Tao, S.-Y.; Nee, J.-N.

2008-01-01

The internet has been widely used to promote collaborative learning among students. However, students do not always have access to the system, leading to doubt in the interaction among the students, and reducing the effectiveness of collaborative learning, since the web-based collaborative learning environment relies entirely on the availability…

Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

NASA Astrophysics Data System (ADS)

Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

1995-02-01

We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

Chinese lexical networks: The structure, function and formation

NASA Astrophysics Data System (ADS)

Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

2012-11-01

In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

Dynamic reconfiguration of human brain functional networks through neurofeedback.

PubMed

Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

2013-11-01

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

Network mechanisms of intentional learning

PubMed Central

Hampshire, Adam; Hellyer, Peter J.; Parkin, Beth; Hiebert, Nole; MacDonald, Penny; Owen, Adrian M.; Leech, Robert; Rowe, James

2016-01-01

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated. PMID:26658925

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

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

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

Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

PubMed

Bunger, Alicia C; Lengnick-Hall, Rebecca

Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future research directions that can disentangle the relationship between learning collaboratives and team networks.

Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

PubMed

Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

2005-12-01

In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

Teacher Networks Companion Piece

ERIC Educational Resources Information Center

Hopkins, Ami Patel; Rulli, Carolyn; Schiff, Daniel; Fradera, Marina

2015-01-01

Network building vitally impacts career development, but in few professions does it impact daily practice more than in teaching. Teacher networks, known as professional learning communities, communities of practice, peer learning circles, virtual professional communities, as well as other names, play a unique and powerful role in education. In…

Network Learning for Educational Change. Professional Learning

ERIC Educational Resources Information Center

Veugelers, Wiel, Ed.; O'Hair, Mary John, Ed.

2005-01-01

School-university networks are becoming an important method to enhance educational renewal and student achievement. Networks go beyond tensions of top-down versus bottom-up, school development and professional development of individuals, theory and practice, and formal and informal organizational structures. The theoretical base of networking…

Learning Universal Computations with Spikes

PubMed Central

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2017-03-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77+/-0.08 and 0.75+/-0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74+/-0.07 and 0.74+/-0.08 with a large network. The AUCs were 0.73+/-0.08 and 0.62+/-0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77+/-0.08 and 0.73+/-0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72+/-0.06 and 0.64+/-0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

Quantitation of the Minor Tobacco Alkaloids Nornicotine, Anatabine, and Anabasine in Smokers' Urine by High Throughput Liquid Chromatography-Mass Spectrometry.

PubMed

von Weymarn, Linda B; Thomson, Nicole M; Donny, Eric C; Hatsukami, Dorothy K; Murphy, Sharon E

2016-03-21

Nicotine is the most abundant alkaloid in tobacco accounting for 95% of the alkaloid content. There are also several minor tobacco alkaloids; among these are nornicotine, anatabine, and anabasine. We developed and applied a 96 well plate-based capillary LC-tandem mass spectrometry method for the analysis of nornicotine, anatabine, and anabasine in urine. The method was validated with regard to accuracy and precision. Anabasine was quantifiable to low levels with a limit of quantitation (LOQ) of 0.2 ng/mL even when nicotine, which is isobaric, is present at concentrations >2500-fold higher than anabasine. This attribute of the method is important since anatabine and anabasine in urine have been proposed as biomarkers of tobacco use for individuals using nicotine replacement therapies. In the present study, we analyzed the three minor tobacco alkaloids in urine from 827 smokers with a wide range of tobacco exposures. Nornicotine (LOQ 0.6 ng/mL) was detected in all samples, and anatabine (LOQ, 0.15 ng/mL) and anabasine were detected in 97.7% of the samples. The median urinary concentrations of nornicotine, anatabine, and anabasine were 98.9, 4.02, and 5.53 ng/mL. Total nicotine equivalents (TNE) were well correlated with anatabine (r(2) = 0.714) and anabasine (r(2) = 0.760). TNE was most highly correlated with nornicotine, which is also a metabolite of nicotine. Urine samples from a subset of subjects (n = 110) were analyzed for the presence of glucuronide conjugates by quantifying any increase in anatabine and anabasine concentrations after β-glucuronidase treatment. The median ratio of the glucuronidated to free anatabine was 0.74 (range, 0.1 to 10.9), and the median ratio of glucuronidated to free anabasine was 0.3 (range, 0.1 to 2.9). To our knowledge, this is the largest population of smokers for whom the urinary concentrations of these three tobacco alkaloids has been reported.

Prespeech motor learning in a neural network using reinforcement.

PubMed

Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

2013-02-01

Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa. Copyright © 2012 Elsevier Ltd. All rights reserved.

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

Internal and External Pressures: How Does an Organic Cotton Production Network Learn to Keep Its Hybrid Nature?

ERIC Educational Resources Information Center

Turcato, Carolina; Barin-Cruz, Luciano; Pedrozo, Eugenio Avila

2012-01-01

Purpose: This study aims to investigate how an organic cotton production network learns to maintain its hybrid network and its sustainability in the face of internal and external pressures. Design/methodology/approach: A qualitative case study was conducted in Justa Trama, a Brazilian-based organic cotton production network formed by six members…

A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

ERIC Educational Resources Information Center

Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

2016-01-01

Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

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

PubMed

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.

Evaluation of multi-level social learning for sustainable landscapes: perspective of a development initiative in Bergslagen, Sweden.

PubMed

Axelsson, Robert; Angelstam, Per; Myhrman, Lennart; Sädbom, Stefan; Ivarsson, Milis; Elbakidze, Marine; Andersson, Kenneth; Cupa, Petr; Diry, Christian; Doyon, Frederic; Drotz, Marcus K; Hjorth, Arne; Hermansson, Jan Olof; Kullberg, Thomas; Lickers, F Henry; McTaggart, Johanna; Olsson, Anders; Pautov, Yurij; Svensson, Lennart; Törnblom, Johan

2013-03-01

To implement policies about sustainable landscapes and rural development necessitates social learning about states and trends of sustainability indicators, norms that define sustainability, and adaptive multi-level governance. We evaluate the extent to which social learning at multiple governance levels for sustainable landscapes occur in 18 local development initiatives in the network of Sustainable Bergslagen in Sweden. We mapped activities over time, and interviewed key actors in the network about social learning. While activities resulted in exchange of experiences and some local solutions, a major challenge was to secure systematic social learning and make new knowledge explicit at multiple levels. None of the development initiatives used a systematic approach to secure social learning, and sustainability assessments were not made systematically. We discuss how social learning can be improved, and how a learning network of development initiatives could be realized.

A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

Unsupervised learning of contextual constraints in neural networks for simultaneous visual processing of multiple objects

NASA Astrophysics Data System (ADS)

Marshall, Jonathan A.

1992-12-01

A simple self-organizing neural network model, called an EXIN network, that learns to process sensory information in a context-sensitive manner, is described. EXIN networks develop efficient representation structures for higher-level visual tasks such as segmentation, grouping, transparency, depth perception, and size perception. Exposure to a perceptual environment during a developmental period serves to configure the network to perform appropriate organization of sensory data. A new anti-Hebbian inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners), while maintaining contextual consistency constraints, instead of forcing winner-take-all pattern classifications. The activations can represent multiple patterns simultaneously and can represent uncertainty. The network performs parallel parsing, credit attribution, and simultaneous constraint satisfaction. EXIN networks can learn to represent multiple oriented edges even where they intersect and can learn to represent multiple transparently overlaid surfaces defined by stereo or motion cues. In the case of stereo transparency, the inhibitory learning implements both a uniqueness constraint and permits coactivation of cells representing multiple disparities at the same image location. Thus two or more disparities can be active simultaneously without interference. This behavior is analogous to that of Prazdny's stereo vision algorithm, with the bonus that each binocular point is assigned a unique disparity. In a large implementation, such a NN would also be able to represent effectively the disparities of a cloud of points at random depths, like human observers, and unlike Prazdny's method

Dynamic functional connectivity shapes individual differences in associative learning.

PubMed

Fatima, Zainab; Kovacevic, Natasha; Misic, Bratislav; McIntosh, Anthony Randal

2016-11-01

Current neuroscientific research has shown that the brain reconfigures its functional interactions at multiple timescales. Here, we sought to link transient changes in functional brain networks to individual differences in behavioral and cognitive performance by using an active learning paradigm. Participants learned associations between pairs of unrelated visual stimuli by using feedback. Interindividual behavioral variability was quantified with a learning rate measure. By using a multivariate statistical framework (partial least squares), we identified patterns of network organization across multiple temporal scales (within a trial, millisecond; across a learning session, minute) and linked these to the rate of change in behavioral performance (fast and slow). Results indicated that posterior network connectivity was present early in the trial for fast, and later in the trial for slow performers. In contrast, connectivity in an associative memory network (frontal, striatal, and medial temporal regions) occurred later in the trial for fast, and earlier for slow performers. Time-dependent changes in the posterior network were correlated with visual/spatial scores obtained from independent neuropsychological assessments, with fast learners performing better on visual/spatial subtests. No relationship was found between functional connectivity dynamics in the memory network and visual/spatial test scores indicative of cognitive skill. By using a comprehensive set of measures (behavioral, cognitive, and neurophysiological), we report that individual variations in learning-related performance change are supported by differences in cognitive ability and time-sensitive connectivity in functional neural networks. Hum Brain Mapp 37:3911-3928, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

PubMed

Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

2016-11-01

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Networked Learning: Design Considerations for Online Instructors

ERIC Educational Resources Information Center

Czerkawski, Betul C.

2016-01-01

The considerable increase in web-based knowledge networks in the past two decades is strongly influencing learning environments. Learning entails information retrieval, use, communication, and production, and is strongly enriched by socially mediated discussions, debates, and collaborative activities. It is becoming critical for educators to…

Using smart mobile devices in social-network-based health education practice: a learning behavior analysis.

PubMed

Wu, Ting-Ting

2014-06-01

Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

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 data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Bridging Cognitive And Neural Aspects Of Classroom Learning

NASA Astrophysics Data System (ADS)

Posner, Michael I.

2009-11-01

A major achievement of the first twenty years of neuroimaging is to reveal the brain networks that underlie fundamental aspects of attention, memory and expertise. We examine some principles underlying the activation of these networks. These networks represent key constraints for the design of teaching. Individual differences in these networks reflect a combination of genes and experiences. While acquiring expertise is easier for some than others the importance of effort in its acquisition is a basic principle. Networks are strengthened through exercise, but maintaining interest that produces sustained attention is key to making exercises successful. The state of the brain prior to learning may also represent an important constraint on successful learning and some interventions designed to investigate the role of attention state in learning are discussed. Teaching remains a creative act between instructor and student, but an understanding of brain mechanisms might improve opportunity for success for both participants.

Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; O'Brien, George

2009-11-01

We describe our initial efforts at implementing social network analysis to visualize and quantify student interactions in Florida International University's Physics Learning Center. Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at FIU. Our implementation of a research and learning community, embedded within a course reform effort, has led to increased recruitment and retention of physics majors. Finn and Rock [1997] link the academic and social integration of students to increased rates of retention. To identify these interactions, we have initiated an investigation that utilizes social network analysis to identify primary community participants. Community interactions are then characterized through the network's density and connectivity, shedding light on learning communities and participation. Preliminary results, further research questions, and future directions utilizing social network analysis are presented.

A review of active learning approaches to experimental design for uncovering biological networks

PubMed Central

2017-01-01

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

CA1 cell activity sequences emerge after reorganization of network correlation structure during associative learning

PubMed Central

Modi, Mehrab N; Dhawale, Ashesh K; Bhalla, Upinder S

2014-01-01

Animals can learn causal relationships between pairs of stimuli separated in time and this ability depends on the hippocampus. Such learning is believed to emerge from alterations in network connectivity, but large-scale connectivity is difficult to measure directly, especially during learning. Here, we show that area CA1 cells converge to time-locked firing sequences that bridge the two stimuli paired during training, and this phenomenon is coupled to a reorganization of network correlations. Using two-photon calcium imaging of mouse hippocampal neurons we find that co-time-tuned neurons exhibit enhanced spontaneous activity correlations that increase just prior to learning. While time-tuned cells are not spatially organized, spontaneously correlated cells do fall into distinct spatial clusters that change as a result of learning. We propose that the spatial re-organization of correlation clusters reflects global network connectivity changes that are responsible for the emergence of the sequentially-timed activity of cell-groups underlying the learned behavior. DOI: http://dx.doi.org/10.7554/eLife.01982.001 PMID:24668171

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

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.

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

PubMed

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.

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

PubMed Central

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

2017-01-01

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. PMID:28394270

Machine Learning Topological Invariants with Neural Networks

NASA Astrophysics Data System (ADS)

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.

Local Area Networks and the Learning Lab of the Future.

ERIC Educational Resources Information Center

Ebersole, Dennis C.

1987-01-01

Considers educational applications of local area computer networks and discusses industry standards for design established by the International Standards Organization (ISO) and Institute of Electrical and Electronic Engineers (IEEE). A futuristic view of a learning laboratory using a local area network is presented. (Author/LRW)

Networked Teaching and Learning.

ERIC Educational Resources Information Center

Benson, Chris, Ed.

2002-01-01

This theme issue on networked teaching and learning contains 11 articles written by teachers of English and language arts in Bread Loaf's primarily rural, teacher networks. Most of these narratives describe how teachers have taught writing and literature using online exchanges or teleconferencing involving students in different locations and grade…

Experiences of Pioneers Facilitating Teacher Networks for Professional Development

ERIC Educational Resources Information Center

Hanraets, Irene; Hulsebosch, Joitske; de Laat, Maarten

2011-01-01

This study presents an exploration into facilitation practices of teacher professional development networks. Stimulating networked learning amongst teachers is a powerful way of creating an informal practice-based learning space driven by teacher needs. As such, it presents an additional channel (besides more formal traditional professional…

Transfer of Learning Relates to Intrinsic Connectivity between Hippocampus, Ventromedial Prefrontal Cortex, and Large-Scale Networks

PubMed Central

Gerraty, Raphael T.; Davidow, Juliet Y.; Wimmer, G. Elliott; Kahn, Itamar

2014-01-01

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions. PMID:25143610

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

PubMed

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.

Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.

2017-08-01

The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.

Stochastic competitive learning in complex networks.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..

Learning and optimization with cascaded VLSI neural network building-block chips

NASA Technical Reports Server (NTRS)

Duong, T.; Eberhardt, S. P.; Tran, M.; Daud, T.; Thakoor, A. P.

1992-01-01

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.

Deep learning of orthographic representations in baboons.

PubMed

Hannagan, Thomas; Ziegler, Johannes C; Dufau, Stéphane; Fagot, Joël; Grainger, Jonathan

2014-01-01

What is the origin of our ability to learn orthographic knowledge? We use deep convolutional networks to emulate the primate's ventral visual stream and explore the recent finding that baboons can be trained to discriminate English words from nonwords. The networks were exposed to the exact same sequence of stimuli and reinforcement signals as the baboons in the experiment, and learned to map real visual inputs (pixels) of letter strings onto binary word/nonword responses. We show that the networks' highest levels of representations were indeed sensitive to letter combinations as postulated in our previous research. The model also captured the key empirical findings, such as generalization to novel words, along with some intriguing inter-individual differences. The present work shows the merits of deep learning networks that can simulate the whole processing chain all the way from the visual input to the response while allowing researchers to analyze the complex representations that emerge during the learning process.

Distributed synaptic weights in a LIF neural network and learning rules

NASA Astrophysics Data System (ADS)

Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

2017-09-01

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

1993-01-01

A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

An architecture for designing fuzzy logic controllers using neural networks

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Social Inclusion and Learning Networks: A "Wider Notion of Learning" or Taking Things in a Different Direction?

ERIC Educational Resources Information Center

O'Brien, Mark; Atkinson, Amanda; Burton, Diana; Campbell, Anne; Qualter, Anne; Varga-Atkins, Tunde

2009-01-01

This article has been produced from the work of a research project conducted in the context of a city-wide education service in the United Kingdom. This was the Liverpool Learning Networks Research Project, which began in July 2005. The researchers carried out semi-structured interviews with education practitioners--learning network…

Let's Face(book) It: Analyzing Interactions in Social Network Groups for Chemistry Learning

ERIC Educational Resources Information Center

Rap, Shelley; Blonder, Ron

2016-01-01

We examined how social network (SN) groups contribute to the learning of chemistry. The main goal was to determine whether chemistry learning could occur in the group discourse. The emphasis was on groups of students in the 11th and 12th grades who learn chemistry in preparation for their final external examination. A total of 1118 discourse…

A Networked Learning Model for Construction of Personal Learning Environments in Seventh Grade Life Science

ERIC Educational Resources Information Center

Drexler, Wendy

2010-01-01

The purpose of this design-based research case study was to apply a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. API widgets were…

Analysis of the “naming game” with learning errors in communications

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong

2015-07-01

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Analysis of the "naming game" with learning errors in communications.

PubMed

Lou, Yang; Chen, Guanrong

2015-07-16

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Informal Learning and Identity Formation in Online Social Networks

ERIC Educational Resources Information Center

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…

What Drives Nurses' Blended e-Learning Continuance Intention?

ERIC Educational Resources Information Center

Cheng, Yung-Ming

2014-01-01

This study's purpose was to synthesize the user network (including subjective norm and network externality), task-technology fit (TTF), and expectation-confirmation model (ECM) to explain nurses' intention to continue using the blended electronic learning (e-learning) system within medical institutions. A total of 450 questionnaires were…

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

The CIRTL Network: A Professional Development Network for Future STEM Faculty

NASA Astrophysics Data System (ADS)

Herbert, B. E.

2011-12-01

The Center for the Integration of Research, Teaching, and Learning (CIRTL) is an NSF Center for Learning and Teaching in higher education using the professional development of graduate students and post-doctoral scholars as the leverage point to develop a national STEM faculty committed to implementing and advancing effective teaching practices for diverse student audiences as part of successful professional careers. The goal of CIRTL is to improve the STEM learning of all students at every college and university, and thereby to increase the diversity in STEM fields and the STEM literacy of the nation. The CIRTL network seeks to support change at a number of levels to support its goals: individual, classroom, institutional, and national. To bring about change, which is never easy, the CIRTL network has developed a conceptual model or change model that is thought to support the program objectives. Three central concepts, Teaching-as-Research, Learning Communities, and Learning-through-Diversity, underlie the design of all CIRTL activities. STEM faculty use research methods to systematically and reflectively improve learning outcomes. This work is done within a community of shared learning and discovery, and explicitly recognizes that effective teaching capitalizes on the rich array of experiences, backgrounds, and skills among the students and instructors to enhance the learning of all. This model is being refined and tested through a networked-design experiment, where the model is tested in diverse settings. Established in fall 2006, the CIRTL Network comprises the University of Colorado at Boulder (CU), Howard University, Michigan State University, Texas A&M University, Vanderbilt University, and the University of Wisconsin-Madison. The diversity of these institutions is by design: private/public; large/moderate size; majority-/minority-serving; geographic location. This talk will describe the theoretical constructs and efficacy of Teaching-as Research as a central design element of the CIRTL network model. Teaching-as-Research involves the deliberate, systematic, and reflective use of research methods to develop and implement teaching practices that advance the learning experiences and outcomes of students. CIRTL envision three types of learning outcomes for CIRTL participants: CIRTL Fellow, CIRTL Practitioner, and CIRTL Scholar. These three, tiered learning outcomes recognize the role of the CIRTL pillars in effective teaching (Fellow), scholarly teaching that builds on the CIRTL pillars to demonstrably improve learning and make the results public (Practitioner), and finally scholarship that advances teaching and learning under peer review (Scholar). CIRTL program outcomes conceived in this way permit anyone to enter the CIRTL Network learning community from a wide variety of disciplines, needs, and past experiences, and to achieve success as an instructor in diverse contexts.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

Synchronized Pair Configuration in Virtualization-Based Lab for Learning Computer Networks

ERIC Educational Resources Information Center

Kongcharoen, Chaknarin; Hwang, Wu-Yuin; Ghinea, Gheorghita

2017-01-01

More studies are concentrating on using virtualization-based labs to facilitate computer or network learning concepts. Some benefits are lower hardware costs and greater flexibility in reconfiguring computer and network environments. However, few studies have investigated effective mechanisms for using virtualization fully for collaboration.…

Viable Global Networked Learning. JSRI Occasional Paper No. 23. Latino Studies Series.

ERIC Educational Resources Information Center

Arias, Armando A., Jr.

This paper discusses an innovative paradigm for looking at computer mediated/networked teaching, learning, and research known as BESTNET (Binational English and Spanish Telecommunications Network). BESTNET is functionally defined as an international community of universities and institutions linked by common educational goals and processes,…

Neural network applications in telecommunications

NASA Technical Reports Server (NTRS)

Alspector, Joshua

1994-01-01

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Networked Environments that Create Hybrid Spaces for Learning Science

ERIC Educational Resources Information Center

Otrel-Cass, Kathrin; Khoo, Elaine; Cowie, Bronwen

2014-01-01

Networked learning environments that embed the essence of the Community of Inquiry (CoI) framework utilise pedagogies that encourage dialogic practices. This can be of significance for classroom teaching across all curriculum areas. In science education, networked environments are thought to support student investigations of scientific problems,…

The Role of the Australian Open Learning Information Network.

ERIC Educational Resources Information Center

Bishop, Robin; And Others

Three documents are presented which describe the Australian Open Learning Information Network (AOLIN)--a national, independent, and self-supporting network of educational researchers with a common interest in the use of information technology for open and distance education--and discuss two evaluative studies undertaken by the organization. The…

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

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

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.

Frame prediction using recurrent convolutional encoder with residual learning

NASA Astrophysics Data System (ADS)

Yue, Boxuan; Liang, Jun

2018-05-01

The prediction for the frame of a video is difficult but in urgent need in auto-driving. Conventional methods can only predict some abstract trends of the region of interest. The boom of deep learning makes the prediction for frames possible. In this paper, we propose a novel recurrent convolutional encoder and DE convolutional decoder structure to predict frames. We introduce the residual learning in the convolution encoder structure to solve the gradient issues. The residual learning can transform the gradient back propagation to an identity mapping. It can reserve the whole gradient information and overcome the gradient issues in Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Besides, compared with the branches in CNNs and the gated structures in RNNs, the residual learning can save the training time significantly. In the experiments, we use UCF101 dataset to train our networks, the predictions are compared with some state-of-the-art methods. The results show that our networks can predict frames fast and efficiently. Furthermore, our networks are used for the driving video to verify the practicability.