Machine learning of parameters for accurate semiempirical quantum chemical calculations

International Nuclear Information System (INIS)

Dral, Pavlo O.; Lilienfeld, O. Anatole von; Thiel, Walter

2015-01-01

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C 7 H 10 O 2 , for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions.

Science.gov (United States)

Tamosiunaite, Minija; Asfour, Tamim; Wörgötter, Florentin

2009-03-01

Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult.

Reaching Consensus on Essential Biomedical Science Learning Objectives in a Dental Curriculum.

Science.gov (United States)

Best, Leandra; Walton, Joanne N; Walker, Judith; von Bergmann, HsingChi

2016-04-01

This article describes how the University of British Columbia Faculty of Dentistry reached consensus on essential basic biomedical science objectives for DMD students and applied the information to the renewal of its DMD curriculum. The Delphi Method was used to build consensus among dental faculty members and students regarding the relevance of over 1,500 existing biomedical science objectives. Volunteer panels of at least three faculty members (a basic scientist, a general dentist, and a dental specialist) and a fourth-year dental student were formed for each of 13 biomedical courses in the first two years of the program. Panel members worked independently and anonymously, rating each course objective as "need to know," "nice to know," "irrelevant," or "don't know." Panel members were advised after each round which objectives had not yet achieved a 75% consensus and were asked to reconsider their ratings. After a maximum of three rounds to reach consensus, a second group of faculty experts reviewed and refined the results to establish the biomedical science objectives for the renewed curriculum. There was consensus on 46% of the learning objectives after round one, 80% after round two, and 95% after round three. The second expert group addressed any remaining objectives as part of its review process. Only 47% of previous biomedical science course objectives were judged to be essential or "need to know" for the general dentist. The consensus reached by participants in the Delphi Method panels and a second group of faculty experts led to a streamlined, better integrated DMD curriculum to prepare graduates for future practice.

Effect of tonic pain on motor acquisition and retention while learning to reach in a force field.

Science.gov (United States)

Lamothe, Mélanie; Roy, Jean-Sébastien; Bouffard, Jason; Gagné, Martin; Bouyer, Laurent J; Mercier, Catherine

2014-01-01

Most patients receiving intensive rehabilitation to improve their upper limb function experience pain. Despite this, the impact of pain on the ability to learn a specific motor task is still unknown. The aim of this study was to determine whether the presence of experimental tonic pain interferes with the acquisition and retention stages of motor learning associated with training in a reaching task. Twenty-nine healthy subjects were randomized to either a Control or Pain Group (receiving topical capsaicin cream on the upper arm during training on Day 1). On two consecutive days, subjects made ballistic movements towards two targets (NEAR/FAR) using a robotized exoskeleton. On Day 1, the task was performed without (baseline) and with a force field (adaptation). The adaptation task was repeated on Day 2. Task performance was assessed using index distance from the target at the end of the reaching movement. Motor planning was assessed using initial angle of deviation of index trajectory from a straight line to the target. Results show that tonic pain did not affect baseline reaching. Both groups improved task performance across time (pControl group for the FAR target (p = 0.030) during both acquisition and retention. Moreover, a Group x Time interaction (p = 0.028) was observed on initial angle of deviation, suggesting that subjects with Pain made larger adjustments in the feedforward component of the movement over time. Interestingly, behaviour of the Pain group was very stable from the end of Day 1 (with pain) to the beginning of Day 2 (pain-free), indicating that the differences observed could not solely be explained by the impact of pain on immediate performance. This suggests that if people learn to move differently in the presence of pain, they might maintain this altered strategy over time.

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

Science.gov (United States)

Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

2016-03-24

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

Science.gov (United States)

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Effect of tonic pain on motor acquisition and retention while learning to reach in a force field.

Directory of Open Access Journals (Sweden)

Mélanie Lamothe

Full Text Available Most patients receiving intensive rehabilitation to improve their upper limb function experience pain. Despite this, the impact of pain on the ability to learn a specific motor task is still unknown. The aim of this study was to determine whether the presence of experimental tonic pain interferes with the acquisition and retention stages of motor learning associated with training in a reaching task. Twenty-nine healthy subjects were randomized to either a Control or Pain Group (receiving topical capsaicin cream on the upper arm during training on Day 1. On two consecutive days, subjects made ballistic movements towards two targets (NEAR/FAR using a robotized exoskeleton. On Day 1, the task was performed without (baseline and with a force field (adaptation. The adaptation task was repeated on Day 2. Task performance was assessed using index distance from the target at the end of the reaching movement. Motor planning was assessed using initial angle of deviation of index trajectory from a straight line to the target. Results show that tonic pain did not affect baseline reaching. Both groups improved task performance across time (p<0.001, but the Pain group showed a larger final error (under-compensation than the Control group for the FAR target (p = 0.030 during both acquisition and retention. Moreover, a Group x Time interaction (p = 0.028 was observed on initial angle of deviation, suggesting that subjects with Pain made larger adjustments in the feedforward component of the movement over time. Interestingly, behaviour of the Pain group was very stable from the end of Day 1 (with pain to the beginning of Day 2 (pain-free, indicating that the differences observed could not solely be explained by the impact of pain on immediate performance. This suggests that if people learn to move differently in the presence of pain, they might maintain this altered strategy over time.

Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

Science.gov (United States)

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning

Science.gov (United States)

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Science.gov (United States)

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

Podiatry Ankle Duplex Scan: Readily Learned and Accurate in Diabetes.

Science.gov (United States)

Normahani, Pasha; Powezka, Katarzyna; Aslam, Mohammed; Standfield, Nigel J; Jaffer, Usman

2018-03-01

We aimed to train podiatrists to perform a focused duplex ultrasound scan (DUS) of the tibial vessels at the ankle in diabetic patients; podiatry ankle (PodAnk) duplex scan. Thirteen podiatrists underwent an intensive 3-hour long simulation training session. Participants were then assessed performing bilateral PodAnk duplex scans of 3 diabetic patients with peripheral arterial disease. Participants were assessed using the duplex ultrasound objective structured assessment of technical skills (DUOSATS) tool and an "Imaging Score". A total of 156 vessel assessments were performed. All patients had abnormal waveforms with a loss of triphasic flow. Loss of triphasic flow was accurately detected in 145 (92.9%) vessels; the correct waveform was identified in 139 (89.1%) cases. Participants achieved excellent DUOSATS scores (median 24 [interquartile range: 23-25], max attainable score of 26) as well as "Imaging Scores" (8 [8-8], max attainable score of 8) indicating proficiency in technical skills. The mean time taken for each bilateral ankle assessment was 20.4 minutes (standard deviation ±6.7). We have demonstrated that a focused DUS for the purpose of vascular assessment of the diabetic foot is readily learned using intensive simulation training.

A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

Directory of Open Access Journals (Sweden)

Meysam Bastani

Full Text Available BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. RESULTS: This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. CONCLUSIONS: Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction

Science.gov (United States)

2013-01-01

Background Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case–control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. Results We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual’s continental and sub-continental ancestry. To predict an individual’s continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of ≥ 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control’s λ from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

Science.gov (United States)

Dral, Pavlo O.; Owens, Alec; Yurchenko, Sergei N.; Thiel, Walter

2017-06-01

We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). We introduce structure-based sampling to automatically assign nuclear configurations from a pre-defined grid to the training and prediction sets, respectively. Accurate high-level ab initio energies are required only for the points in the training set, while the energies for the remaining points are provided by the ML model with negligible computational cost. The proposed sampling procedure is shown to be superior to random sampling and also eliminates the need for training several ML models. Self-correcting machine learning has been implemented such that each additional layer corrects errors from the previous layer. The performance of our approach is demonstrated in a case study on a published high-level ab initio PES of methyl chloride with 44 819 points. The ML model is trained on sets of different sizes and then used to predict the energies for tens of thousands of nuclear configurations within seconds. The resulting datasets are utilized in variational calculations of the vibrational energy levels of CH3Cl. By using both structure-based sampling and self-correction, the size of the training set can be kept small (e.g., 10% of the points) without any significant loss of accuracy. In ab initio rovibrational spectroscopy, it is thus possible to reduce the number of computationally costly electronic structure calculations through structure-based sampling and self-correcting KRR-based machine learning by up to 90%.

Role of limb and target vision in the online control of memory-guided reaches.

Science.gov (United States)

Heath, Matthew

2005-07-01

This investigation tested the proposal that a "highly accurate" and temporally unstable stored target representation is available to the motor system for the online control of memory-guided reaches. Participants reached to a target that was: (a) visible during the response, (b) extinguished at movement onset, and (c) occluded for 0, 500, 1,500 and 2,500 ms in advance of response cueing. Additionally, trials were performed with (i.e., limb visible) and without (i.e., limb occluded) vision of the reaching limb. Results showed that limb occluded trials undershot the target location in each target condition, and were characterized by a primarily offline mode of control. In contrast, limb visible trials showed a consistent level of endpoint accuracy for each target condition and elicited more online reaching corrections than limb occluded trials. It is therefore proposed that a reasonably accurate and temporally stable stored target representation can be combined with vision of the moving limb for the online control of memory-guided reaches.

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

Science.gov (United States)

Fernandez, Michael; Boyd, Peter G; Daff, Thomas D; Aghaji, Mohammad Zein; Woo, Tom K

2014-09-04

In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.

Digital Immigrants, Digital Learning: Reaching Adults through Information Literacy Instruction Online

Science.gov (United States)

Rapchak, Marcia; Behary, Robert

2013-01-01

As information literacy programs become more robust, finding methods of reaching students beyond the traditional undergraduate has become a priority for many institutions. At Duquesne University, efforts have been made to reach adult learners in an accelerated program targeted to nontraditional students, much of which is provided online. This…

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

Science.gov (United States)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

On stiffening cables of a long reach manipulator

International Nuclear Information System (INIS)

Wang, S.L.; Santiago, P.

1996-01-01

A long reach manipulator will be used for waste remediation in large underground storage tanks. The manipulator's slenderness makes it flexible and difficult to control. A low-cost and effective method to enhance the manipulator's stiffness is proposed in this research by using suspension cables. These cables can also be used to accurately measure the position of the manipulator's wrist

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

Science.gov (United States)

Versace, Amelia; Sharma, Vinod; Bertocci, Michele A; Bebko, Genna; Iyengar, Satish; Dwojak, Amanda; Bonar, Lisa; Perlman, Susan B; Schirda, Claudiu; Travis, Michael; Gill, Mary Kay; Diwadkar, Vaibhav A; Sunshine, Jeffrey L; Holland, Scott K; Kowatch, Robert A; Birmaher, Boris; Axelson, David; Frazier, Thomas W; Arnold, L Eugene; Fristad, Mary A; Youngstrom, Eric A; Horwitz, Sarah M; Findling, Robert L; Phillips, Mary L

2017-01-01

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with different (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

Directory of Open Access Journals (Sweden)

Amelia Versace

Full Text Available Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5. Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18 from those with lower (n = 34 trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7. Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03 youth with different (higher vs lower trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots.

Science.gov (United States)

Srinivasa, Narayan; Bhattacharyya, Rajan; Sundareswara, Rashmi; Lee, Craig; Grossberg, Stephen

2012-11-01

This paper describes a redundant robot arm that is capable of learning to reach for targets in space in a self-organized fashion while avoiding obstacles. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle-free space using the Direction-to-Rotation Transform (DIRECT). Unlike prior DIRECT models, the learning process in this work was realized using an online Fuzzy ARTMAP learning algorithm. The DIRECT-based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not having experienced them during learning. The DIRECT model was extended based on a novel reactive obstacle avoidance direction (DIRECT-ROAD) model to enable redundant robots to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevented the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, a self-organized process of mental rehearsals of movements was modeled, inspired by human and animal experiments on reaching, to generate plans for movement execution using DIRECT-ROAD in complex environments. These mental rehearsals or plans are self-generated by using the Fuzzy ARTMAP algorithm to retrieve multiple solutions for reaching each target while accounting for all the obstacles in its environment. The key aspects of the proposed novel controller were illustrated first using simple examples. Experiments were then performed on real robot platforms to demonstrate successful obstacle avoidance during reaching tasks in real-world environments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Applying attachment theory to effective practice with hard-to-reach youth: the AMBIT approach.

Science.gov (United States)

Bevington, Dickon; Fuggle, Peter; Fonagy, Peter

2015-01-01

Adolescent Mentalization-Based Integrative Treatment (AMBIT) is a developing approach to working with "hard-to-reach" youth burdened with multiple co-occurring morbidities. This article reviews the core features of AMBIT, exploring applications of attachment theory to understand what makes young people "hard to reach," and provide routes toward increased security in their attachment to a worker. Using the theory of the pedagogical stance and epistemic ("pertaining to knowledge") trust, we show how it is the therapeutic worker's accurate mentalizing of the adolescent that creates conditions for new learning, including the establishment of alternative (more secure) internal working models of helping relationships. This justifies an individual keyworker model focused on maintaining a mentalizing stance toward the adolescent, but simultaneously emphasizing the critical need for such keyworkers to remain well connected to their wider team, avoiding activation of their own attachment behaviors. We consider the role of AMBIT in developing a shared team culture (shared experiences, shared language, shared meanings), toward creating systemic contexts supportive of such relationships. We describe how team training may enhance the team's ability to serve as a secure base for keyworkers, and describe an innovative approach to treatment manualization, using a wiki format as one way of supporting this process.

Musicians Reaching out to People with Dementia : Perspectives of Learning

NARCIS (Netherlands)

Smilde, Rineke; Herzberg, H.; Kammler, E.

2011-01-01

Article on the emergence of the community musician in particular the project Music for Life of Wigmore Hall in London is described. The biographical learning and the learning processes are examined in detail and examples of the interactions between musicians and people with dementia are given as

Media and Information Literacy (MIL) in journalistic learning: strategies for accurately engaging with information and reporting news

Science.gov (United States)

Inayatillah, F.

2018-01-01

In the era of digital technology, there is abundant information from various sources. This ease of access needs to be accompanied by the ability to engage with the information wisely. Thus, information and media literacy is required. From the results of preliminary observations, it was found that the students of Universitas Negeri Surabaya, whose major is Indonesian Literature, and they take journalistic course lack of the skill of media and information literacy (MIL). Therefore, they need to be equipped with MIL. The method used is descriptive qualitative, which includes data collection, data analysis, and presentation of data analysis. Observation and documentation techniques were used to obtain data of MIL’s impact on journalistic learning for students. This study aims at describing the important role of MIL for students of journalistic and its impact on journalistic learning for students of Indonesian literature batch 2014. The results of this research indicate that journalistic is a science that is essential for students because it affects how a person perceives news report. Through the reinforcement of the course, students can avoid a hoax. MIL-based journalistic learning makes students will be more skillful at absorbing, processing, and presenting information accurately. The subject influences students in engaging with information so that they can report news credibly.

The Internet and the Global Reach of EU law

DEFF Research Database (Denmark)

Kuner, Christopher Barth

and learning; international negotiation; coercion and conditionality; and blocking recognition of third country legal measures. The EU’s actions in exercising its global reach implicate important normative issues, such as distinguishing between the furtherance of core EU legal values and the advancement...

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Science.gov (United States)

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.

Science.gov (United States)

Liang, Liang; Liu, Minliang; Martin, Caitlin; Sun, Wei

2018-01-01

Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. © 2018 The Author(s).

Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning

Directory of Open Access Journals (Sweden)

Tanel Pärnamaa

2017-05-01

Full Text Available High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.

Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.

Science.gov (United States)

Pärnamaa, Tanel; Parts, Leopold

2017-05-05

High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy. Copyright © 2017 Parnamaa and Parts.

Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

Directory of Open Access Journals (Sweden)

Niklas Berliner

Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

Restoring Maximum Vertical Browsing Reach in Sauropod Dinosaurs.

Science.gov (United States)

Paul, Gregory S

2017-10-01

The ongoing controversy centered on neck posture and function in sauropod dinosaurs is misplaced for a number of reasons. Because of an absence of pertinent data it is not possible to accurately restore the posture and range of motion in long necked fossil animals, only gross approximations are possible. The existence of a single "neutral posture" in animals with long, slender necks may not exist, and its relationship to feeding habits is weak. Restorations of neutral osteological neck posture based on seemingly detailed diagrams of cervical articulations are not reliable because the pictures are not sufficiently accurate due to a combination of illustration errors, and distortion of the fossil cervicals. This is all the more true because fossil cervical series lack the critical inter-centra cartilage. Maximum vertical reach is more readily restorable and biologically informative for long necked herbivores. Modest extension of 10° between each caudal cervical allowed high shouldered sauropods to raise the cranial portion of their necks to vertical postures that allowed them to reach floral resources far higher than seen in the tallest mammals. This hypothesis is supported by the dorsally extended articulation of the only known co-fused sauropod cervicals. Many sauropods appear to have been well adapted for rearing in order to boost vertical reach, some possessed retroverted pelves that may have allowed them to walk slowly while bipedal. A combination of improved high browsing abilities and sexual selection probably explains the unusually long necks of tall ungulates and super tall sauropods. Anat Rec, 2017. © 2017 Wiley Periodicals, Inc. Anat Rec, 300:1802-1825, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Hydraulic alterations resulting from hydropower development in the Bonneville Reach of the Columbia River

Science.gov (United States)

Hatten, James R.; Batt, Thomas R.

2010-01-01

We used a two-dimensional (2D) hydrodynamic model to simulate and compare the hydraulic characteristics in a 74-km reach of the Columbia River (the Bonneville Reach) before and after construction of Bonneville Dam. For hydrodynamic modeling, we created a bathymetric layer of the Bonneville Reach from single-beam and multi-beam echo-sounder surveys, digital elevation models, and navigation surveys. We calibrated the hydrodynamic model at 100 and 300 kcfs with a user-defined roughness layer, a variable-sized mesh, and a U.S. Army Corps of Engineers backwater curve. We verified the 2D model with acoustic Doppler current profiler (ADCP) data at 14 transects and three flows. The 2D model was 88% accurate for water depths, and 77% accurate for velocities. We verified a pre-dam 2D model run at 126 kcfs using pre-dam aerial photos from September 1935. Hydraulic simulations indicated that mean water depths in the Bonneville Reach increased by 34% following dam construction, while mean velocities decreased by 58%. There are numerous activities that would benefit from data output from the 2D model, including biological sampling, bioenergetics, and spatially explicit habitat modeling.

Preparing to reach: selecting an adaptive long-latency feedback controller

OpenAIRE

Ahmadi-Pajouh, Mohammad Ali; Towhidkhah, Farzad; Shadmehr, Reza

2012-01-01

In a voluntary movement, the nervous system specifies not only the motor commands, but also the gains associated with reaction to sensory feedback. For example, suppose that during reaching a perturbation tends to push the hand to the left. With practice, the brain not only learns to produce commands that predictively compensate for the perturbation, but also increases the long-latency reflex gain associated with leftward displacements of the arm. That is, the brain learns a feedback controll...

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Science.gov (United States)

Ramakrishnan, Raghunathan; Dral, Pavlo O; Rupp, Matthias; von Lilienfeld, O Anatole

2015-05-12

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

Can Social Learning Increase Learning Speed, Performance or Both?

NARCIS (Netherlands)

Heinerman, J.V.; Stork, J.; Rebolledo Coy, M.A.; Hubert, J.G.; Eiben, A.E.; Bartz-Beielstein, Thomas; Haasdijk, Evert

2017-01-01

Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning

When Is Network Lasso Accurate?

Directory of Open Access Journals (Sweden)

Alexander Jung

2018-01-01

Full Text Available The “least absolute shrinkage and selection operator” (Lasso method has been adapted recently for network-structured datasets. In particular, this network Lasso method allows to learn graph signals from a small number of noisy signal samples by using the total variation of a graph signal for regularization. While efficient and scalable implementations of the network Lasso are available, only little is known about the conditions on the underlying network structure which ensure network Lasso to be accurate. By leveraging concepts of compressed sensing, we address this gap and derive precise conditions on the underlying network topology and sampling set which guarantee the network Lasso for a particular loss function to deliver an accurate estimate of the entire underlying graph signal. We also quantify the error incurred by network Lasso in terms of two constants which reflect the connectivity of the sampled nodes.

Google Hangouts: Leveraging Social Media to Reach the Education Community

Science.gov (United States)

Eisenhamer, Bonnie; Summers, Frank; McCallister, Dan; Ryer, Holly

2015-01-01

Research shows that educator professional development is most effective when it is sustained and/or when a follow-on component is included to support the learning process. In order to create more comprehensive learning experiences for our workshop participants, the education team at the Space Telescope Science Institute is working collaboratively with scientific staff and other experts to create a follow-on component for our professional development program. The new component utilizes video conferencing platforms, such as Google's Hangouts On Air, to provide educators with content updates and extended learning opportunities in between in-person professional development experiences. The goal is to enhance our professional development program in a cost-effective way while reaching a greater cross-section of educators. Video broadcasts go live on Google+, YouTube, and our website - thus providing access to any user with a web browser. Additionally, the broadcasts are automatically recorded and archived for future viewing on our YouTube channel. This provides educators with anywhere, anytime training that best suits their needs and schedules. This poster will highlight our new Hangouts for educators as well as our cross-departmental efforts to expand the reach of our Hubble Hangouts for the public through a targeted recruitment strategy.

Generalization of unconstrained reaching with hand-weight changes.

Science.gov (United States)

Yan, Xiang; Wang, Qining; Lu, Zhengchuan; Stevenson, Ian H; Körding, Konrad; Wei, Kunlin

2013-01-01

Studies of motor generalization usually perturb hand reaches by distorting visual feedback with virtual reality or by applying forces with a robotic manipulandum. Whereas such perturbations are useful for studying how the central nervous system adapts and generalizes to novel dynamics, they are rarely encountered in daily life. The most common perturbations that we experience are changes in the weights of objects that we hold. Here, we use a center-out, free-reaching task, in which we can manipulate the weight of a participant's hand to examine adaptation and generalization following naturalistic perturbations. In both trial-by-trial paradigms and block-based paradigms, we find that learning converges rapidly (on a timescale of approximately two trials), and this learning generalizes mostly to movements in nearby directions with a unimodal pattern. However, contrary to studies using more artificial perturbations, we find that the generalization has a strong global component. Furthermore, the generalization is enhanced with repeated exposure of the same perturbation. These results suggest that the familiarity of a perturbation is a major factor in movement generalization and that several theories of the neural control of movement, based on perturbations applied by robots or in virtual reality, may need to be extended by incorporating prior influence that is characterized by the familiarity of the perturbation.

Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture

Directory of Open Access Journals (Sweden)

Zhiquan Gao

2015-09-01

Full Text Available Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain.

Towards accurate de novo assembly for genomes with repeats

NARCIS (Netherlands)

Bucur, Doina

2017-01-01

De novo genome assemblers designed for short k-mer length or using short raw reads are unlikely to recover complex features of the underlying genome, such as repeats hundreds of bases long. We implement a stochastic machine-learning method which obtains accurate assemblies with repeats and

Accurate estimation of indoor travel times

DEFF Research Database (Denmark)

Prentow, Thor Siiger; Blunck, Henrik; Stisen, Allan

2014-01-01

The ability to accurately estimate indoor travel times is crucial for enabling improvements within application areas such as indoor navigation, logistics for mobile workers, and facility management. In this paper, we study the challenges inherent in indoor travel time estimation, and we propose...... the InTraTime method for accurately estimating indoor travel times via mining of historical and real-time indoor position traces. The method learns during operation both travel routes, travel times and their respective likelihood---both for routes traveled as well as for sub-routes thereof. InTraTime...... allows to specify temporal and other query parameters, such as time-of-day, day-of-week or the identity of the traveling individual. As input the method is designed to take generic position traces and is thus interoperable with a variety of indoor positioning systems. The method's advantages include...

Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

DEFF Research Database (Denmark)

Kobayashi, Sofie; Rump, Camilla Østerberg

The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunitie...

Memory-guided reaching in a patient with visual hemiagnosia.

Science.gov (United States)

Cornelsen, Sonja; Rennig, Johannes; Himmelbach, Marc

2016-06-01

The two-visual-systems hypothesis (TVSH) postulates that memory-guided movements rely on intact functions of the ventral stream. Its particular importance for memory-guided actions was initially inferred from behavioral dissociations in the well-known patient DF. Despite of rather accurate reaching and grasping movements to visible targets, she demonstrated grossly impaired memory-guided grasping as much as impaired memory-guided reaching. These dissociations were later complemented by apparently reversed dissociations in patients with dorsal damage and optic ataxia. However, grasping studies in DF and optic ataxia patients differed with respect to the retinotopic position of target objects, questioning the interpretation of the respective findings as a double dissociation. In contrast, the findings for reaching errors in both types of patients came from similar peripheral target presentations. However, new data on brain structural changes and visuomotor deficits in DF also questioned the validity of a double dissociation in reaching. A severe visuospatial short-term memory deficit in DF further questioned the specificity of her memory-guided reaching deficit. Therefore, we compared movement accuracy in visually-guided and memory-guided reaching in a new patient who suffered a confined unilateral damage to the ventral visual system due to stroke. Our results indeed support previous descriptions of memory-guided movements' inaccuracies in DF. Furthermore, our data suggest that recently discovered optic-ataxia like misreaching in DF is most likely caused by her parieto-occipital and not by her ventral stream damage. Finally, multiple visuospatial memory measurements in HWS suggest that inaccuracies in memory-guided reaching tasks in patients with ventral damage cannot be explained by visuospatial short-term memory or perceptual deficits, but by a specific deficit in visuomotor processing. Copyright © 2016 Elsevier Ltd. All rights reserved.

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

Science.gov (United States)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various

Homebound Learning Opportunities: Reaching Out to Older Shut-ins and Their Caregivers.

Science.gov (United States)

Penning, Margaret; Wasyliw, Douglas

1992-01-01

Describes Homebound Learning Opportunities, innovative health promotion and educational outreach service for homebound older adults and their caregivers. Notes that program provides over 125 topics for individualized learning programs delivered to participants in homes, audiovisual lending library, educational television programing, and peer…

Can donated media placements reach intended audiences?

Science.gov (United States)

Cooper, Crystale Purvis; Gelb, Cynthia A; Chu, Jennifer; Polonec, Lindsey

2013-09-01

Donated media placements for public service announcements (PSAs) can be difficult to secure, and may not always reach intended audiences. Strategies used by the Centers for Disease Control and Prevention's (CDC) Screen for Life: National Colorectal Cancer Action Campaign (SFL) to obtain donated media placements include producing a diverse mix of high-quality PSAs, co-branding with state and tribal health agencies, securing celebrity involvement, monitoring media trends to identify new distribution opportunities, and strategically timing the release of PSAs. To investigate open-ended recall of PSAs promoting colorectal cancer screening, CDC conducted 12 focus groups in three U.S. cities with men and women either nearing age 50 years, when screening is recommended to begin, or aged 50-75 years who were not in compliance with screening guidelines. In most focus groups, multiple participants recalled exposure to PSAs promoting colorectal cancer screening, and most of these individuals reported having seen SFL PSAs on television, in transit stations, or on the sides of public buses. Some participants reported exposure to SFL PSAs without prompting from the moderator, as they explained how they learned about the disease. Several participants reported learning key campaign messages from PSAs, including that colorectal cancer screening should begin at age 50 years and screening can find polyps so they can be removed before becoming cancerous. Donated media placements can reach and educate mass audiences, including millions of U.S. adults who have not been screened appropriately for colorectal cancer.

How infants' reaches reveal principles of sensorimotor decision making

Science.gov (United States)

Dineva, Evelina; Schöner, Gregor

2018-01-01

In Piaget's classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Science.gov (United States)

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

E-Learning in Malaysia: Moving forward in Open Distance Learning

Science.gov (United States)

Abas, Zoraini Wati

2009-01-01

Many higher education institutions have embarked on e-learning as a means to support their learning and teaching activities. In distance learning institutions, e-learning has enabled them to reach out to students dispersed over a wide geographical area, locally and internationally. In some countries, e-learning has also given students the…

A fast and accurate online sequential learning algorithm for feedforward networks.

Science.gov (United States)

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Changes in Purkinje cell simple spike encoding of reach kinematics during adaption to a mechanical perturbation.

Science.gov (United States)

Hewitt, Angela L; Popa, Laurentiu S; Ebner, Timothy J

2015-01-21

The cerebellum is essential in motor learning. At the cellular level, changes occur in both the simple spike and complex spike firing of Purkinje cells. Because simple spike discharge reflects the main output of the cerebellar cortex, changes in simple spike firing likely reflect the contribution of the cerebellum to the adapted behavior. Therefore, we investigated in Rhesus monkeys how the representation of arm kinematics in Purkinje cell simple spike discharge changed during adaptation to mechanical perturbations of reach movements. Monkeys rapidly adapted to a novel assistive or resistive perturbation along the direction of the reach. Adaptation consisted of matching the amplitude and timing of the perturbation to minimize its effect on the reach. In a majority of Purkinje cells, simple spike firing recorded before and during adaptation demonstrated significant changes in position, velocity, and acceleration sensitivity. The timing of the simple spike representations change within individual cells, including shifts in predictive versus feedback signals. At the population level, feedback-based encoding of position increases early in learning and velocity decreases. Both timing changes reverse later in learning. The complex spike discharge was only weakly modulated by the perturbations, demonstrating that the changes in simple spike firing can be independent of climbing fiber input. In summary, we observed extensive alterations in individual Purkinje cell encoding of reach kinematics, although the movements were nearly identical in the baseline and adapted states. Therefore, adaption to mechanical perturbation of a reaching movement is accompanied by widespread modifications in the simple spike encoding. Copyright © 2015 the authors 0270-6474/15/351106-19$15.00/0.

Distance Learning: A Way of Life-Long Learning

National Research Council Canada - National Science Library

Belanich, James; Moses, Franklin L; Orvis, Kara L

2005-01-01

... the predominant form of distance learning today, and will likely continue to be in the future. The instructional approach of distance learning - or DL - has many benefits but has yet to reach its full potential...

Teach Beyond Your Reach: An instructor’s guide to developing and running successful distance learning classes, workshops, training sessions and more

Directory of Open Access Journals (Sweden)

Reviewed by Yavuz AKBULUT

2008-01-01

Full Text Available 237―Teach Beyond Your Reach: An instructor‘s guide to developing and running successful distance learning classes, workshops, training sessions and more‖ serves as a guide for novice andexperienced distance educators to develop anddeliver their own training sessions. The book isconsisted of 234 pages (+xi covering eightinterdependent chapters followed by a usefulappendix of further reading resources, sampleintroductory materials for distance learning and asample lecture. The author, Robin Neidorf,teaches communications and writing through theonline campus of the Univeristy of Phoenix andco-teaches a creative writing course through theUniversity of Gävle in Sweden in addition to herThe book serves as a terrific resource for bothnovice distance educators and as a reference forthose who are more experienced. It lays out mostof the things needed to teach online throughcoaching the readers to understand the currentsituation and pass onto next levels of sophistication in e-learning practices. Two critical things that are emphasized in thebook are interaction as the core of learning, and collaboration among the distance education practitioners. The focus is not on developing Web pages, troubleshooting specific software or providing student support services. Rather, it focuses on therequirements for instruction and underlines where we might need to collaborate with Chapter 1 discusses the tools available for distance learning along with suggestions on how they may be used. Chapter 2 describes the distance population addressingdifferent learning styles, attitudes and generational differences all of which might affect the way students enter the class, and work with the teacher and materials.Chapter 3 and 4 focus on instructional design and development with a particular emphasis on creating content, which is both interactive and in line with learning objectives. Chapter 5 provides ideas on managing the distance classroom with conciseand thoughtful

Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

Science.gov (United States)

Masso, Majid; Vaisman, Iosif I

2008-09-15

Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

Science.gov (United States)

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Optical fiber reach extended FMCW radar for remote respiratory tracking

DEFF Research Database (Denmark)

Suhr, Lau Frejstrup; Tafur Monroy, Idelfonso; Vegas Olmos, Juan José

2017-01-01

Wireless monitoring of human vital signs such as breathing rate is a nonintrusive alternative to contemporary solutions relying on physical contact. To ease the installment, fiber optical transmission is used to extend the reach from the transmitter and receiver circuitry to the antenna subsystem....... In this paper, a frequency modulated carrier wave radar, operating at 25.7–26.6 GHz and utilizing optical fiber extension, was experimentally demonstrated to accurately recover the breathing rate of a human placed 1 m away from the radar antennas....

Sub-processes of motor learning revealed by a robotic manipulandum for rodents.

Science.gov (United States)

Lambercy, O; Schubring-Giese, M; Vigaru, B; Gassert, R; Luft, A R; Hosp, J A

2015-02-01

Rodent models are widely used to investigate neural changes in response to motor learning. Usually, the behavioral readout of motor learning tasks used for this purpose is restricted to a binary measure of performance (i.e. "successful" movement vs. "failure"). Thus, the assignability of research in rodents to concepts gained in human research - implying diverse internal models that constitute motor learning - is still limited. To solve this problem, we recently introduced a three-degree-of-freedom robotic platform designed for rats (the ETH-Pattus) that combines an accurate behavioral readout (in the form of kinematics) with the possibility to invasively assess learning related changes within the brain (e.g. by performing immunohistochemistry or electrophysiology in acute slice preparations). Here, we validate this platform as a tool to study motor learning by establishing two forelimb-reaching paradigms that differ in degree of skill. Both conditions can be precisely differentiated in terms of their temporal pattern and performance levels. Based on behavioral data, we hypothesize the presence of several sub-processes contributing to motor learning. These share close similarities with concepts gained in humans or primates. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterizing and predicting submovements during human three-dimensional arm reaches.

Directory of Open Access Journals (Sweden)

James Y Liao

Full Text Available We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.

Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift diffusion model

Directory of Open Access Journals (Sweden)

Jiaxiang eZhang

2014-04-01

Full Text Available Two phenomena are commonly observed in decision-making. First, there is a speed-accuracy tradeoff such that decisions are slower and more accurate when instructions emphasize accuracy over speed, and vice versa. Second, decision performance improves with practice, as a task is learnt. The speed-accuracy tradeoff and learning effects have been explained under a well-established evidence-accumulation framework for decision-making, which suggests that evidence supporting each choice is accumulated over time, and a decision is committed to when the accumulated evidence reaches a decision boundary. This framework suggests that changing the decision boundary creates the tradeoff between decision speed and accuracy, while increasing the rate of accumulation leads to more accurate and faster decisions after learning. However, recent studies challenged the view that speed-accuracy tradeoff and learning are associated with changes in distinct, single decision parameters. Further, the influence of speed-accuracy instructions over the course of learning remains largely unknown. Here, we used a hierarchical drift-diffusion model to examine the speed-accuracy tradeoff during learning of a coherent motion discrimination task across multiple training sessions, and a transfer test session. The influence of speed-accuracy instructions was robust over training and generalized across untrained stimulus features. Emphasizing decision accuracy rather than speed was associated with increased boundary separation, drift rate and non-decision time at the beginning of training. However, after training, an emphasis on decision accuracy was only associated with increased boundary separation. In addition, faster and more accurate decisions after learning were due to a gradual decrease in boundary separation and an increase in drift rate. The results suggest that speed-accuracy instructions and learning differentially shape decision-making processes at different time scales.

Accurate lithography simulation model based on convolutional neural networks

Science.gov (United States)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

Proximal versus distal control of two-joint planar reaching movements in the presence of neuromuscular noise.

Science.gov (United States)

Nguyen, Hung P; Dingwell, Jonathan B

2012-06-01

Determining how the human nervous system contends with neuro-motor noise is vital to understanding how humans achieve accurate goal-directed movements. Experimentally, people learning skilled tasks tend to reduce variability in distal joint movements more than in proximal joint movements. This suggests that they might be imposing greater control over distal joints than proximal joints. However, the reasons for this remain unclear, largely because it is not experimentally possible to directly manipulate either the noise or the control at each joint independently. Therefore, this study used a 2 degree-of-freedom torque driven arm model to determine how different combinations of noise and/or control independently applied at each joint affected the reaching accuracy and the total work required to make the movement. Signal-dependent noise was simultaneously and independently added to the shoulder and elbow torques to induce endpoint errors during planar reaching. Feedback control was then applied, independently and jointly, at each joint to reduce endpoint error due to the added neuromuscular noise. Movement direction and the inertia distribution along the arm were varied to quantify how these biomechanical variations affected the system performance. Endpoint error and total net work were computed as dependent measures. When each joint was independently subjected to noise in the absence of control, endpoint errors were more sensitive to distal (elbow) noise than to proximal (shoulder) noise for nearly all combinations of reaching direction and inertia ratio. The effects of distal noise on endpoint errors were more pronounced when inertia was distributed more toward the forearm. In contrast, the total net work decreased as mass was shifted to the upper arm for reaching movements in all directions. When noise was present at both joints and joint control was implemented, controlling the distal joint alone reduced endpoint errors more than controlling the proximal joint

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

Science.gov (United States)

Clark, Alex M; Bunin, Barry A; Litterman, Nadia K; Schürer, Stephan C; Visser, Ubbo

2014-01-01

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Directory of Open Access Journals (Sweden)

Alex M. Clark

2014-08-01

Full Text Available Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

Robust and accurate vectorization of line drawings.

Science.gov (United States)

Hilaire, Xavier; Tombre, Karl

2006-06-01

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

Deep learning versus traditional machine learning methods for aggregated energy demand prediction

NARCIS (Netherlands)

Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

2018-01-01

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

Supporting the development of interpersonal skills in nursing, in an undergraduate mental health curriculum: reaching the parts other strategies do not reach through action learning.

Science.gov (United States)

Waugh, Anna; McNay, Lisa; Dewar, Belinda; McCaig, Marie

2014-09-01

The centrality of therapeutic relationships is considered to be the cornerstone of effective mental health nursing practice. Strategies that support the development of these skills and the emotional aspects of learning need to be developed. Action learning is one such strategy. This article reports on a qualitative research study on the introduction of Action Learning Sets (ALS) into a Pre-registration Mental Health Nursing Programme. This teaching and learning methodology was chosen to support the emotional aspects of learning and mental health nursing skills. Four themes were identified: developing skills of listening and questioning in 'real time', enhanced self-awareness, being with someone in the moment--there is no rehearsal and doing things differently in practice. Students and lecturers found the experience positive and advocate for other Pre-registration Mental Health Nursing Programmes to consider the use of ALS within the curriculum. © 2013.

Learning Outcomes Report

NARCIS (Netherlands)

Stoyanov, Slavi; Spoelstra, Howard; Burgoyne, Louise; O’Tuathaigh, Colm

2018-01-01

Aim of the study The learning outcomes study, conducted as part of WP3 of the BioApp project, has as objectives: (a) generating a comprehensive list of the learning outcomes; (b) reaching an agreement on the scope and priority of the learning outcomes, and (c) making suggestions for the further

Rapid feedback responses correlate with reach adaptation and properties of novel upper limb loads.

Science.gov (United States)

Cluff, Tyler; Scott, Stephen H

2013-10-02

A hallmark of voluntary motor control is the ability to adjust motor patterns for novel mechanical or visuomotor contexts. Recent work has also highlighted the importance of feedback for voluntary control, leading to the hypothesis that feedback responses should adapt when we learn new motor skills. We tested this prediction with a novel paradigm requiring that human subjects adapt to a viscous elbow load while reaching to three targets. Target 1 required combined shoulder and elbow motion, target 2 required only elbow motion, and target 3 (probe target) required shoulder but no elbow motion. This simple approach controlled muscle activity at the probe target before, during, and after the application of novel elbow loads. Our paradigm allowed us to perturb the elbow during reaching movements to the probe target and identify several key properties of adapted stretch responses. Adapted long-latency responses expressed (de-) adaptation similar to reaching errors observed when we introduced (removed) the elbow load. Moreover, reaching errors during learning correlated with changes in the long-latency response, showing subjects who adapted more to the elbow load displayed greater modulation of their stretch responses. These adapted responses were sensitive to the size and direction of the viscous training load. Our results highlight an important link between the adaptation of feedforward and feedback control and suggest a key part of motor adaptation is to adjust feedback responses to the requirements of novel motor skills.

Superior cognitive mapping through single landmark-related learning than through boundary-related learning.

Science.gov (United States)

Zhou, Ruojing; Mou, Weimin

2016-08-01

Cognitive mapping is assumed to be through hippocampus-dependent place learning rather than striatum-dependent response learning. However, we proposed that either type of spatial learning, as long as it involves encoding metric relations between locations and reference points, could lead to a cognitive map. Furthermore, the fewer reference points to specify individual locations, the more accurate a cognitive map of these locations will be. We demonstrated that participants have more accurate representations of vectors between 2 locations and of configurations among 3 locations when locations are individually encoded in terms of a single landmark than when locations are encoded in terms of a boundary. Previous findings have shown that learning locations relative to a boundary involve stronger place learning and higher hippocampal activation whereas learning relative to a single landmark involves stronger response learning and higher striatal activation. Recognizing this, we have provided evidence challenging the cognitive map theory but favoring our proposal. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Effect of Cooperative Learning on the Learning Approaches of Students with Different Learning Styles

Science.gov (United States)

Çolak, Esma

2015-01-01

Problem Statement: For this study, a cooperative learning process was designed in which students with different learning styles could help each other in heterogeneous groups to perform teamwork-based activities. One aspect deemed important in this context was whether the instructional environment designed to reach students with different learning…

The importance of accurate meteorological input fields and accurate planetary boundary layer parameterizations, tested against ETEX-1

International Nuclear Information System (INIS)

Brandt, J.; Ebel, A.; Elbern, H.; Jakobs, H.; Memmesheimer, M.; Mikkelsen, T.; Thykier-Nielsen, S.; Zlatev, Z.

1997-01-01

Atmospheric transport of air pollutants is, in principle, a well understood process. If information about the state of the atmosphere is given in all details (infinitely accurate information about wind speed, etc.) and infinitely fast computers are available then the advection equation could in principle be solved exactly. This is, however, not the case: discretization of the equations and input data introduces some uncertainties and errors in the results. Therefore many different issues have to be carefully studied in order to diminish these uncertainties and to develop an accurate transport model. Some of these are e.g. the numerical treatment of the transport equation, accuracy of the mean meteorological input fields and parameterizations of sub-grid scale phenomena (as e.g. parameterizations of the 2 nd and higher order turbulence terms in order to reach closure in the perturbation equation). A tracer model for studying transport and dispersion of air pollution caused by a single but strong source is under development. The model simulations from the first ETEX release illustrate the differences caused by using various analyzed fields directly in the tracer model or using a meteorological driver. Also different parameterizations of the mixing height and the vertical exchange are compared. (author)

Reaching Girls

Science.gov (United States)

Jacobs, Charlotte E.; Kuriloff, Peter J.; Cox, Amanda B.

2014-01-01

If educators want to engage girls in learning, they must align teaching practices with girls' specific needs. In a study modeled after Reichert and Hawley's study of boys, the authors learned that lessons with hands-on learning, elements of creativity, multimodal projects, and class discussions all worked to stimulate girls'…

Digital Technology in Teaching International Business: Is a Tradeoff between Richness and Reach Required?

Science.gov (United States)

Wymbs, Cliff; Kijne, Hugo

2003-01-01

This analysis extends the traditional marketing tradeoffs between richness (depth of knowledge) and reach (geographic area coverage) to the emerging technology-mediated education industry, and then specifically evaluates their effect on the teaching of international business. It asserts that interactive learning, particularly as it applies to team…

Kinesthetically guided reaching accuracy in individuals with a history of traumatic anterior shoulder dislocation

Directory of Open Access Journals (Sweden)

Hung Y

2013-05-01

Full Text Available You-jou Hung,1 Warren G Darling2 1Doctor of Physical Therapy Program, Department of Nursing and Rehabilitation Sciences, Angelo State University, San Angelo, TX, USA; 2Department of Health and Human Physiology, The University of Iowa, Iowa City, IA, USA Background: The purpose of the study was to investigate whether individuals with a history of traumatic anterior shoulder dislocation show larger reaching errors than those with healthy shoulders and to determine if they implement different reaching strategies to protect the injured shoulder. Methods: Ten people with a history of traumatic anterior shoulder dislocation and 15 with healthy shoulders volunteered for this study. After viewing targets in space, participants pointed with the unconstrained arm to remembered target locations in space without visual guidance. Nine different targets were located in various planes and heights. Endpoint reaching errors were determined by comparing the finger endpoint position without visual guidance to the target location. Shoulder rotation angle at the endpoint was also compared between groups. Results: Participants with injured shoulders were able to point voluntarily to visually specified targets as accurately as participants with healthy shoulders (1 cm difference. However, participants with injured shoulders showed less shoulder external rotation (average 12° difference at the target location when compared with healthy shoulders. This difference was consistent over a large range of target locations. Conclusion: Individuals with a history of traumatic anterior shoulder dislocation have sufficient kinesthetic information about their upper limb orientation to point accurately to visually specified targets in space. However, individuals with injured shoulders acquired a new motor strategy to reach with less shoulder external rotation, presumably to protect the injured shoulder from recurrent injuries. Keywords: shoulder injuries, physiotherapy, shoulder

CircleBoard-Pro: Concrete manipulative-based learning cycle unit for learning geometry

Science.gov (United States)

Jamhari, Wongkia, Wararat

2018-01-01

Currently, a manipulative is commonly used in mathematics education as a supported tool for teaching and learning. With engaging natural interaction of a concrete manipulative and advantages of a learning cycle approach, we proposed the concrete manipulative-based learning cycle unit to promote mathematics learning. Our main objectives are to observe possibilities on the use of a concrete manipulative in learning geometry, and to assess students' understanding of a specific topic, angle properties in a circle, of secondary level students. To meet the first objective, the concrete manipulative, called CricleBoard-Pro, was designed. CircleBoard-Pro is built for easy to writing on or deleting from, accurate angle measurement, and flexible movement. Besides, learning activities and worksheets were created for helping students to learn angle properties in a circle. Twenty eighth graders on a lower secondary school in Indonesia were voluntarily involved to learn mathematics using CircleBoard-Pro with the designed learning activities and worksheets. We informally observed students' performance by focusing on criteria of using manipulative tools in learning mathematics while the learning activities were also observed in terms of whether they work and which step of activities need to be improved. The results of this part showed that CircleBoard-Pro complied the criteria of the use of the manipulative in learning mathematics. Nevertheless, parts of learning activities and worksheets need to be improved. Based on the results of the observation, CircleBoard-Pro, learning activities, and worksheets were merged together and became the CircleBoardPro embedded on 5E (Engage - Explore - Explain - Elaborate - Evaluate) learning cycle unit. Then, students understanding were assessed to reach the second objective. Six ninth graders from an Indonesian school in Thailand were recruited to participate in this study. Conceptual tests for both pre-and post-test, and semi

Democratic learning in the Aalborg Model

DEFF Research Database (Denmark)

Qvist, Palle

A democratic learning system can be defined as a system where decisions, processes and behaviour related to learning are established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) between those affected by the decision simultaneously...... reaching the learning outcomes, the technical and professional knowledge and insight. In principle the participants must be equal with equal rights and feel committed to the values of rationality and impartiality. The Aalborg Model is an example of a democratic learning system although not 100% democratic......, processes and behaviour related to learning can be established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) within the group simultaneously reaching the learning outcomes, the technical and professional knowledge and insight. This article...

Partnerships: The Key to Sustainability and Reach for E/PO

Science.gov (United States)

Eisenhamer, Bonnie; McCallister, D.; Ryer, H.

2013-06-01

The Space Telescope Science Institute (STScI) is the home institution for the E/PO activities of the Hubble and future James Webb space telescopes. Over time, STScI’s Office of Public Outreach has established the infrastructure needed for an E/PO program that reaches various audiences at the local, regional, and national levels. Partnerships are a critical element of this infrastructure, and sustainability of our E/PO program is ensured through our ongoing partnerships with organizations and institutions with staying power and reach. We have learned from past efforts that strategic partnerships can foster innovation, support diversity initiatives, and increase impact in a cost-effective way while providing target audiences with greater access to NASA SMD science and resources. Partnerships are utilized to field-test educational products and programs, disseminate materials and initiatives, and support professional development activities. Partners are selected based upon specific criteria such as potential for reach, the percentage of underrepresented educators and students served, complementary program goals, and willingness to collect and share evaluation data and results with us. This poster will highlight examples and benefits of strategic partnerships over time.

Using NLM exhibits and events to engage library users and reach the community.

Science.gov (United States)

Auten, Beth; Norton, Hannah F; Tennant, Michele R; Edwards, Mary E; Stoyan-Rosenzweig, Nina; Daley, Matthew

2013-01-01

In an effort to reach out to library users and make the library a more relevant, welcoming place, the University of Florida's Health Science Center Library hosted exhibits from the National Library of Medicine's (NLM) Traveling Exhibition Program. From 2010 through 2012, the library hosted four NLM exhibits and created event series for each. Through reflection and use of a participant survey, lessons were learned concerning creating relevant programs, marketing events, and forming new partnerships. Each successive exhibit added events and activities to address different audiences. A survey of libraries that have hosted NLM exhibits highlights lessons learned at those institutions.

LHC Run 2 – reaching the top of the learning curve

CERN Multimedia

2015-01-01

As the LHC Physics conference gets underway in St Petersburg, it’s a good time to take stock of where things stand with Run 2.    For all those involved with operating the LHC and its experiments in this new energy and intensity regime, 2015 was always going to be a learning curve. And learning we most certainly are. The main objective for this year has always been to set up the machine and experiments for production running at high energy and high intensity in 2016, 17 and 18.  That said, the experiments have all been able to collect quality data at 13 TeV, with the first Run 2 papers and conference presentations being written and delivered this summer. It would be unfair of me, however, to give the impression that it’s all been plain sailing. As well as the highs: smooth recommissioning of the machine, physics getting underway, and a successful transition to 25-nanosecond bunch spacing, we’ve also had our fair share of lows. There have been no sho...

Action without awareness: reaching to an object you do not remember seeing.

Directory of Open Access Journals (Sweden)

Matthew Heath

Full Text Available BACKGROUND: Previous work by our group has shown that the scaling of reach trajectories to target size is independent of obligatory awareness of that target property and that "action without awareness" can persist for up to 2000 ms of visual delay. In the present investigation we sought to determine if the ability to scale reaching trajectories to target size following a delay is related to the pre-computing of movement parameters during initial stimulus presentation or the maintenance of a sensory (i.e., visual representation for on-demand response parameterization. METHODOLOGY/PRINCIPAL FINDINGS: Participants completed immediate or delayed (i.e., 2000 ms perceptual reports and reaching responses to different sized targets under non-masked and masked target conditions. For the reaching task, the limb associated with a trial (i.e., left or right was not specified until the time of response cuing: a manipulation that prevented participants from pre-computing the effector-related parameters of their response. In terms of the immediate and delayed perceptual tasks, target size was accurately reported during non-masked trials; however, for masked trials only a chance level of accuracy was observed. For the immediate and delayed reaching tasks, movement time as well as other temporal kinematic measures (e.g., times to peak acceleration, velocity and deceleration increased in relation to decreasing target size across non-masked and masked trials. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that speed-accuracy relations were observed regardless of whether participants were aware (i.e., non-masked trials or unaware (i.e., masked trials of target size. Moreover, the equivalent scaling of immediate and delayed reaches during masked trials indicates that a persistent sensory-based representation supports the unconscious and metrical scaling of memory-guided reaching.

THE IMPLEMENTATION OF JOBSHEET-BASED STUDENT TEAMS ACHIEVEMENT DIVISION LEARNING MODEL TO IMPROVE STUDENTS LEARNING OUTCOMES

Directory of Open Access Journals (Sweden)

Kadek Dodi Permana

2016-09-01

Full Text Available This study aims to improve the Information and Communications Technology (ICT learning outcomes of the students in SMA N 2 Singaraja through the learning model of Job sheet-based Student Team Achievement Division (STAD. This is a classroom action research. The data analysis reveals that learning outcomes in cycle I gain a mean score of 80. 51 and a classical provisions of 15%. There are three students who pass with a minimum score of 85 in cycle I. From these categories, the students’ learning outcomes in the first cycle have not met the criterion of 85%. The mean score of cycle II is 88. 57 and the classical provisions is 90%. In the second cycle, there are 18 students who gain a minimum score of 85. Based on the success criterion, a research study is successful if the minimum completeness criterion reaches 85 and the minimum classical completeness criterion reaches 85%. From the categories, the students’ learning outcomes have been successfully improved since the percentage of classical completeness in the second cycle has reached its expected results.

Learning from Errors

OpenAIRE

Martínez-Legaz, Juan Enrique; Soubeyran, Antoine

2003-01-01

We present a model of learning in which agents learn from errors. If an action turns out to be an error, the agent rejects not only that action but also neighboring actions. We find that, keeping memory of his errors, under mild assumptions an acceptable solution is asymptotically reached. Moreover, one can take advantage of big errors for a faster learning.

Category learning in the color-word contingency learning paradigm.

Science.gov (United States)

Schmidt, James R; Augustinova, Maria; De Houwer, Jan

2018-04-01

In the typical color-word contingency learning paradigm, participants respond to the print color of words where each word is presented most often in one color. Learning is indicated by faster and more accurate responses when a word is presented in its usual color, relative to another color. To eliminate the possibility that this effect is driven exclusively by the familiarity of item-specific word-color pairings, we examine whether contingency learning effects can be observed also when colors are related to categories of words rather than to individual words. To this end, the reported experiments used three categories of words (animals, verbs, and professions) that were each predictive of one color. Importantly, each individual word was presented only once, thus eliminating individual color-word contingencies. Nevertheless, for the first time, a category-based contingency effect was observed, with faster and more accurate responses when a category item was presented in the color in which most of the other items of that category were presented. This finding helps to constrain episodic learning models and sets the stage for new research on category-based contingency learning.

Intelligent navigation and accurate positioning of an assist robot in indoor environments

Science.gov (United States)

Hua, Bin; Rama, Endri; Capi, Genci; Jindai, Mitsuru; Tsuri, Yosuke

2017-12-01

Intact robot's navigation and accurate positioning in indoor environments are still challenging tasks. Especially in robot applications, assisting disabled and/or elderly people in museums/art gallery environments. In this paper, we present a human-like navigation method, where the neural networks control the wheelchair robot to reach the goal location safely, by imitating the supervisor's motions, and positioning in the intended location. In a museum similar environment, the mobile robot starts navigation from various positions, and uses a low-cost camera to track the target picture, and a laser range finder to make a safe navigation. Results show that the neural controller with the Conjugate Gradient Backpropagation training algorithm gives a robust response to guide the mobile robot accurately to the goal position.

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Directory of Open Access Journals (Sweden)

Muhammad Ali

2017-11-01

Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

Adaptation of reach-to-grasp movement in response to force perturbations.

Science.gov (United States)

Rand, M K; Shimansky, Y; Stelmach, G E; Bloedel, J R

2004-01-01

This study examined how reach-to-grasp movements are modified during adaptation to external force perturbations applied on the arm during reach. Specifically, we examined whether the organization of these movements was dependent upon the condition under which the perturbation was applied. In response to an auditory signal, all subjects were asked to reach for a vertical dowel, grasp it between the index finger and thumb, and lift it a short distance off the table. The subjects were instructed to do the task as fast as possible. The perturbation was an elastic load acting on the wrist at an angle of 105 deg lateral to the reaching direction. The condition was modified by changing the predictability with which the perturbation was applied in a given trial. After recording unperturbed control trials, perturbations were applied first on successive trials (predictable perturbations) and then were applied randomly (unpredictable perturbations). In the early predictable perturbation trials, reach path length became longer and reaching duration increased. As more predictable perturbations were applied, the reach path length gradually decreased and became similar to that of control trials. Reaching duration also decreased gradually as the subjects adapted by exerting force against the perturbation. In addition, the amplitude of peak grip aperture during arm transport initially increased in response to repeated perturbations. During the course of learning, it reached its maximum and thereafter slightly decreased. However, it did not return to the normal level. The subjects also adapted to the unpredictable perturbations through changes in both arm transport and grasping components, indicating that they can compensate even when the occurrence of the perturbation cannot be predicted during the inter-trial interval. Throughout random perturbation trials, large grip aperture values were observed, suggesting that a conservative aperture level is set regardless of whether the

Inactivation of Parietal Reach Region Affects Reaching But Not Saccade Choices in Internally Guided Decisions.

Science.gov (United States)

Christopoulos, Vassilios N; Bonaiuto, James; Kagan, Igor; Andersen, Richard A

2015-08-19

The posterior parietal cortex (PPC) has traditionally been considered important for awareness, spatial perception, and attention. However, recent findings provide evidence that the PPC also encodes information important for making decisions. These findings have initiated a running argument of whether the PPC is critically involved in decision making. To examine this issue, we reversibly inactivated the parietal reach region (PRR), the area of the PPC that is specialized for reaching movements, while two monkeys performed a memory-guided reaching or saccade task. The task included choices between two equally rewarded targets presented simultaneously in opposite visual fields. Free-choice trials were interleaved with instructed trials, in which a single cue presented in the peripheral visual field defined the reach and saccade target unequivocally. We found that PRR inactivation led to a strong reduction of contralesional choices, but only for reaches. On the other hand, saccade choices were not affected by PRR inactivation. Importantly, reaching and saccade movements to single instructed targets remained largely intact. These results cannot be explained as an effector-nonspecific deficit in spatial attention or awareness, since the temporary "lesion" had an impact only on reach choices. Hence, the PPR is a part of a network for reach decisions and not just reach planning. There has been an ongoing debate on whether the posterior parietal cortex (PPC) represents only spatial awareness, perception, and attention or whether it is also involved in decision making for actions. In this study we explore whether the parietal reach region (PRR), the region of the PPC that is specialized for reaches, is involved in the decision process. We inactivated the PRR while two monkeys performed reach and saccade choices between two targets presented simultaneously in both hemifields. We found that inactivation affected only the reach choices, while leaving saccade choices intact

Gaze anchoring guides real but not pantomime reach-to-grasp: support for the action-perception theory.

Science.gov (United States)

Kuntz, Jessica R; Karl, Jenni M; Doan, Jon B; Whishaw, Ian Q

2018-04-01

Reach-to-grasp movements feature the integration of a reach directed by the extrinsic (location) features of a target and a grasp directed by the intrinsic (size, shape) features of a target. The action-perception theory suggests that integration and scaling of a reach-to-grasp movement, including its trajectory and the concurrent digit shaping, are features that depend upon online action pathways of the dorsal visuomotor stream. Scaling is much less accurate for a pantomime reach-to-grasp movement, a pretend reach with the target object absent. Thus, the action-perception theory proposes that pantomime movement is mediated by perceptual pathways of the ventral visuomotor stream. A distinguishing visual feature of a real reach-to-grasp movement is gaze anchoring, in which a participant visually fixates the target throughout the reach and disengages, often by blinking or looking away/averting the head, at about the time that the target is grasped. The present study examined whether gaze anchoring is associated with pantomime reaching. The eye and hand movements of participants were recorded as they reached for a ball of one of three sizes, located on a pedestal at arms' length, or pantomimed the same reach with the ball and pedestal absent. The kinematic measures for real reach-to-grasp movements were coupled to the location and size of the target, whereas the kinematic measures for pantomime reach-to-grasp, although grossly reflecting target features, were significantly altered. Gaze anchoring was also tightly coupled to the target for real reach-to-grasp movements, but there was no systematic focus for gaze, either in relation with the virtual target, the previous location of the target, or the participant's reaching hand, for pantomime reach-to-grasp. The presence of gaze anchoring during real vs. its absence in pantomime reach-to-grasp supports the action-perception theory that real, but not pantomime, reaches are online visuomotor actions and is discussed in

Challenges Encountered in Creating Personalised Learning Activities to Suit Students Learning Preferences

OpenAIRE

O'Donnell, Eileen; Wade, Vincent; Sharp, Mary; O'Donnell, Liam

2013-01-01

This book chapter reviews some of the challenges encountered by educators in creating personalised e-learning activities to suit students learning preferences. Technology-enhanced learning (TEL) alternatively known as e-learning has not yet reached its full potential in higher education. There are still many potential uses as yet undiscovered and other discovered uses which are not yet realisable by many educators. TEL is still predominantly used for e-dissemination and e-administration. This...

Learning Markov models for stationary system behaviors

DEFF Research Database (Denmark)

Chen, Yingke; Mao, Hua; Jaeger, Manfred

2012-01-01

to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using......Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate...... the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model....

Control of a long reach manipulator with suspension cables for waste storage tank remediation. Final report

International Nuclear Information System (INIS)

Wang, S.L.

1994-01-01

A long reach manipulator will be used for waste remediation in large underground storage tanks. The manipulator's slenderness makes it flexible and difficult to control. A low-cost and effective method to enhance the manipulator's stiffness is proposed in this research by using suspension cables. These cables can also be used to accurately measure the position of the manipulator's wrist

Sensorimotor Reorganizations of Arm Kinematics and Postural Strategy for Functional Whole-Body Reaching Movements in Microgravity

Directory of Open Access Journals (Sweden)

Thomas Macaluso

2017-10-01

Full Text Available Understanding the impact of weightlessness on human behavior during the forthcoming long-term space missions is of critical importance, especially when considering the efficiency of goal-directed movements in these unusual environments. Several studies provided a large set of evidence that gravity is taken into account during the planning stage of arm reaching movements to optimally anticipate its consequence upon the moving limbs. However, less is known about sensorimotor changes required to face weightless environments when individuals have to perform fast and accurate goal-directed actions with whole-body displacement. We thus aimed at characterizing kinematic features of whole-body reaching movements in microgravity, involving high spatiotemporal constraints of execution, to question whether and how humans are able to maintain the performance of a functional behavior in the standards of normogravity execution. Seven participants were asked to reach as fast and as accurately as possible visual targets while standing during microgravity episodes in parabolic flight. Small and large targets were presented either close or far from the participants (requiring, in the latter case, additional whole-body displacement. Results reported that participants successfully performed the reaching task with general temporal features of movement (e.g., movement speed close to land observations. However, our analyses also demonstrated substantial kinematic changes related to the temporal structure of focal movement and the postural strategy to successfully perform -constrained- whole-body reaching movements in microgravity. These immediate reorganizations are likely achieved by rapidly taking into account the absence of gravity in motor preparation and execution (presumably from cues about body limbs unweighting. Specifically, when compared to normogravity, the arm deceleration phase substantially increased. Furthermore, greater whole-body forward displacements

Evaluating Regime Change of Sediment Transport in the Jingjiang River Reach, Yangtze River, China

Directory of Open Access Journals (Sweden)

Li He

2018-03-01

Full Text Available The sediment regime in the Jingjiang river reach of the middle Yangtze River has been significantly changed from quasi-equilibrium to unsaturated since the impoundment of the Three Gorges Dam (TGD. Vertical profiles of suspended sediment concentration (SSC and sediment flux can be adopted to evaluate the sediment regime at the local and reach scale, respectively. However, the connection between the vertical concentration profiles and the hydrologic conditions of the sub-saturated channel has rarely been examined based on field data. Thus, vertical concentration data at three hydrological stations in the reach (Zhicheng, Shashi, and Jianli are collected. Analyses show that the near-bed concentration (within 10% of water depth from the riverbed may reach up to 15 times that of the vertical average concentration. By comparing the fractions of the suspended sediment and bed material before and after TGD operation, the geomorphic condition under which the distinct large near-bed concentrations occur have been examined. Based on daily discharge-sediment hydrographs, the reach scale sediment regime and availability of sediment sources are analyzed. In total, remarkable large near-bed concentrations may respond to the combination of wide grading suspended particles and bed material. Finally, several future challenges caused by the anomalous vertical concentration profiles in the unsaturated reach are discussed. This indicates that more detailed measurements or new measuring technologies may help us to provide accurate measurements, while a fractional dispersion equation may help us in describing. The present study aims to gain new insights into regime change of sediment suspension in the river reaches downstream of a very large reservoir.

Reach Address Database (RAD)

Data.gov (United States)

U.S. Environmental Protection Agency — The Reach Address Database (RAD) stores the reach address of each Water Program feature that has been linked to the underlying surface water features (streams,...

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

Science.gov (United States)

Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan-Liu; Marcu, Laura

2018-01-01

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Improving Posthospital Discharge Telephone Reach Rates Through Prehospital Discharge Face-to-Face Meetings.

Science.gov (United States)

Vergara, Franz H; Sheridan, Daniel J; Sullivan, Nancy J; Budhathoki, Chakra

The purpose of this study was to determine whether a face-to-face meeting with patients by a telephonic case manager prehospital discharge would result in increased telephone follow-up (TFU) reach rates posthospital discharge. Acute care adult medicine inpatient units. A quasiexperimental design was utilized. Two adult inpatient medicine units were selected as the intervention and comparison groups. The framework of the study is the transitions theory. A convenience sampling technique was used, whereby 88 eligible patients on the intervention unit received face-to-face meetings prehospital discharge whereas 123 patients on the comparison unit received standard care (no face-to-face meetings). Cross-tabulation and chi-square tests were employed to examine the association of face-to-face meeting intervention and TFU reach rates. Implementing brief (face-to-face meetings by a telephonic case manager prehospital discharge resulted in a TFU reach rate of 87% on the intervention unit, whereas the comparison unit only had a 58% TFU reach rate (p communication with more patients posthospital discharge. A brief prehospital discharge face-to-face meeting with patients assisted them to understand the reasons for a posthospital discharge telephone call, identified the best times to call using accurate telephone numbers, and taught patients how best to prepare for the call. In addition, by meeting patients face-to-face, the telephonic case manager was no longer an unknown person on the telephone asking them questions about their medical condition. These factors combined may have significantly helped to increase TFU reach rates.

Accurate or assumed: visual learning in children with ASD.

Science.gov (United States)

Trembath, David; Vivanti, Giacomo; Iacono, Teresa; Dissanayake, Cheryl

2015-10-01

Children with autism spectrum disorder (ASD) are often described as visual learners. We tested this assumption in an experiment in which 25 children with ASD, 19 children with global developmental delay (GDD), and 17 typically developing (TD) children were presented a series of videos via an eye tracker in which an actor instructed them to manipulate objects in speech-only and speech + pictures conditions. We found no group differences in visual attention to the stimuli. The GDD and TD groups performed better when pictures were available, whereas the ASD group did not. Performance of children with ASD and GDD was positively correlated with visual attention and receptive language. We found no evidence of a prominent visual learning style in the ASD group.

Infants with Down Syndrome and Their Interactions with Objects: Development of Exploratory Actions after Reaching Onset

Science.gov (United States)

de Campos, Ana Carolina; da Costa, Carolina Souza Neves; Savelsbergh, Geert J. P.; Rocha, Nelci Adriana Cicuto Ferreira

2013-01-01

During infant development, objects and their functions are learned by means of active exploration. Factors that may influence exploration include reaching and grasping ability, object properties and the presence of developmental disorders. We assessed the development of exploratory actions in 16 typically-developing (TD) infants and 9 infants with…

The planning illusion: Does active planning of a learning route support learning as well as learners think it does?

NARCIS (Netherlands)

Bonestroo, W.J.; de Jong, Anthonius J.M.

2012-01-01

Is actively planning one’s learning route through a learning domain beneficial for learning? Moreover, can learners accurately judge the extent to which planning has been beneficial for them? This study examined the effects of active planning on learning. Participants received a tool in which they

Compensatory Processing During Rule-Based Category Learning in Older Adults

Science.gov (United States)

Bharani, Krishna L.; Paller, Ken A.; Reber, Paul J.; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G.

2016-01-01

Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex. PMID:26422522

Balancing the Assessment "of" Learning and "for" Learning in Support of Student Literacy Achievement

Science.gov (United States)

Edwards, Patricia A.; Turner, Jennifer D.; Mokhtari, Kouider

2008-01-01

There is a delicate balance between the assessment of learning and assessment for learning. The recommendations included in this Assessment department may be useful for teachers working to achieve this balance and find a more accurate and complete understandings of students' literacy strengths and needs.

Accuracy and Feasibility of Video Analysis for Assessing Hamstring Flexibility and Validity of the Sit-and-Reach Test

Science.gov (United States)

Mier, Constance M.

2011-01-01

The accuracy of video analysis of the passive straight-leg raise test (PSLR) and the validity of the sit-and-reach test (SR) were tested in 60 men and women. Computer software measured static hip-joint flexion accurately. High within-session reliability of the PSLR was demonstrated (R greater than 0.97). Test-retest (separate days) reliability for…

Defining Allowable Physical Property Variations for High Accurate Measurements on Polymer Parts

DEFF Research Database (Denmark)

Mohammadi, Ali; Sonne, Mads Rostgaard; Madruga, Daniel González

2015-01-01

Measurement conditions and material properties have a significant impact on the dimensions of a part, especially for polymers parts. Temperature variation causes part deformations that increase the uncertainty of the measurement process. Current industrial tolerances of a few micrometres demand...... high accurate measurements in non-controlled ambient. Most of polymer parts are manufactured by injection moulding and their inspection is carried out after stabilization, around 200 hours. The overall goal of this work is to reach ±5μm in uncertainty measurements a polymer products which...

Do Voters Learn? Evidence that Voters Respond Accurately to Changes in Political Parties’ Policy Positions

DEFF Research Database (Denmark)

Seeberg, Henrik Bech; Slothuus, Rune; Stubager, Rune

2017-01-01

A premise of the mass–elite linkage at the heart of representative democracy is that voters notice changes in political parties’ policy positions and update their party perceptions accordingly. However, recent studies question the ability of voters accurately to perceive changes in parties...... attention to parties when they visibly change policy position. Second, voters update their perceptions of the party positions much more accurately than would have been expected if they merely relied on a ‘coalition heuristic’ as a rule-of-thumb. These findings imply that under some conditions voters...

The Implementation of Discovery Learning Model with Scientific Learning Approach to Improve Students’ Critical Thinking in Learning History

Directory of Open Access Journals (Sweden)

Edi Nurcahyo

2018-03-01

Full Text Available Historical learning has not reached optimal in the learning process. It is caused by the history teachers’ learning model has not used the innovative learning models. Furthermore, it supported by the perception of students to the history subject because it does not become final exam (UN subject so it makes less improvement and builds less critical thinking in students’ daily learning. This is due to the lack of awareness of historical events and the availability of history books for students and teachers in the library are still lacking. Discovery learning with scientific approach encourages students to solve problems actively and able to improve students' critical thinking skills with scientific approach so student can build scientific thinking include observing, asking, reasoning, trying, and networking   Keywords: discovery learning, scientific, critical thinking

Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke

OpenAIRE

Park, Hyeshin; Schweighofer, Nicolas

2017-01-01

Background We recently showed that individuals with chronic stroke who completed two sessions of intensive unassisted arm reach training exhibited improvements in movement times up to one month post-training. Here, we study whether changes in movement times during training can predict long-term changes. Methods Sixteen participants with chronic stroke and ten non-disabled age-matched participants performed two sessions of reach training with 600 movements per session. Movement time data durin...

An accurate determination of the flux within a slab

International Nuclear Information System (INIS)

Ganapol, B.D.; Lapenta, G.

1993-01-01

During the past decade, several articles have been written concerning accurate solutions to the monoenergetic neutron transport equation in infinite and semi-infinite geometries. The numerical formulations found in these articles were based primarily on the extensive theoretical investigations performed by the open-quotes transport greatsclose quotes such as Chandrasekhar, Busbridge, Sobolev, and Ivanov, to name a few. The development of numerical solutions in infinite and semi-infinite geometries represents an example of how mathematical transport theory can be utilized to provide highly accurate and efficient numerical transport solutions. These solutions, or analytical benchmarks, are useful as open-quotes industry standards,close quotes which provide guidance to code developers and promote learning in the classroom. The high accuracy of these benchmarks is directly attributable to the rapid advancement of the state of computing and computational methods. Transport calculations that were beyond the capability of the open-quotes supercomputersclose quotes of just a few years ago are now possible at one's desk. In this paper, we again build upon the past to tackle the slab problem, which is of the next level of difficulty in comparison to infinite media problems. The formulation is based on the monoenergetic Green's function, which is the most fundamental transport solution. This method of solution requires a fast and accurate evaluation of the Green's function, which, with today's computational power, is now readily available

AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.

Science.gov (United States)

Ma, Jianzhu; Wang, Sheng

2015-01-01

The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

Production of Accurate Skeletal Models of Domestic Animals Using Three-Dimensional Scanning and Printing Technology

Science.gov (United States)

Li, Fangzheng; Liu, Chunying; Song, Xuexiong; Huan, Yanjun; Gao, Shansong; Jiang, Zhongling

2018-01-01

Access to adequate anatomical specimens can be an important aspect in learning the anatomy of domestic animals. In this study, the authors utilized a structured light scanner and fused deposition modeling (FDM) printer to produce highly accurate animal skeletal models. First, various components of the bovine skeleton, including the femur, the…

An efficient flow-based botnet detection using supervised machine learning

DEFF Research Database (Denmark)

Stevanovic, Matija; Pedersen, Jens Myrup

2014-01-01

Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

Suspended-sediment trapping in the tidal reach of an estuarine tributary channel

Science.gov (United States)

Downing-Kunz, Maureen; Schoellhamer, David H.

2015-01-01

Evidence of decreasing sediment supply to estuaries and coastal oceans worldwide illustrates the need for accurate and updated estimates. In the San Francisco Estuary (Estuary), recent research suggests a decrease in supply from its largest tributaries, implying the increasing role of smaller, local tributaries in sediment supply to this estuary. Common techniques for estimating supply from tributaries are based on gages located above head of tide, which do not account for trapping processes within the tidal reach. We investigated the effect of a tidal reach on suspended-sediment discharge for Corte Madera Creek, a small tributary of the Estuary. Discharge of water (Q) and suspended-sediment (SSD) were observed for 3 years at two locations along the creek: upstream of tidal influence and at the mouth. Comparison of upstream and mouth gages showed nearly 50 % trapping of upstream SSD input within the tidal reach over this period. At the storm time scale, suspended-sediment trapping efficiency varied greatly (range −31 to 93 %); storms were classified as low- or high-yield based on upstream SSD. As upstream peak Q increased, high-yield storms exhibited significantly decreased trapping. Tidal conditions at the mouth—ebb duration and peak ebb velocity—during storms had a minor effect on sediment trapping, suggesting fluvial processes dominate. Comparison of characteristic fluvial and tidal discharges at the storm time scale demonstrated longitudinal differences in the regulating process for SSD. These results suggest that SSD from gages situated above head of tide overestimate sediment supply to the open waters beyond tributary mouths and thus trapping processes within the tidal reach should be considered.

Learning through Teaching: A Microbiology Service-Learning Experience

Directory of Open Access Journals (Sweden)

Ginny Webb

2015-11-01

Full Text Available Service learning is defined as a strategy in which students apply what they have learned in the classroom to a community service project. Many educators would agree that students often learn best through teaching others. This premise was the motivation for a new service-learning project in which undergraduate microbiology students developed and taught hands-on microbiology lessons to local elementary school children. The lessons included teaching basic information about microbes, disease transmission, antibiotics, vaccines, and methods of disease prevention. This service-learning project benefitted the college students by enforcing their knowledge of microbiology and provided them an opportunity to reach out to children within their community. This project also benefitted the local schools by teaching the younger students about microbes, infections, and handwashing. In this paper, I discuss the development and implementation of this new microbiology service-learning project, as well as the observed impact it had on everyone involved.

Training self-assessment and task-selection skills : A cognitive approach to improving self-regulated learning

NARCIS (Netherlands)

Kostons, Danny; van Gog, Tamara; Paas, Fred

For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task

Functional reach and lateral reach tests adapted for aquatic physical therapy

Directory of Open Access Journals (Sweden)

Ana Angélica Ribeiro de Lima

Full Text Available Abstract Introduction: Functional reach (FR and lateral reach (LR tests are widely used in scientific research and clinical practice. Assessment tools are useful in assessing subjects with greater accuracy and are usually adapted according to the limitations of each condition. Objective: To adapt FR and LR tests for use in an aquatic environment and assess the performance of healthy young adults. Methods: We collected anthropometric data and information on whether the participant exercised regularly or not. The FR and LR tests were adapted for use in an aquatic environment and administered to 47 healthy subjects aged 20-30 years. Each test was repeated three times. Results: Forty-one females and six males were assessed. The mean FR test score for men was 24.06 cm, whereas the mean value for right lateral reach (RLR was 10.94 cm and for left lateral reach (LLR was 9.78 cm. For females, the mean FR score was 17.57 cm, while the mean values for RLR was 8.84cm and for LLR was 7.76 cm. Men performed better in the FR (p < 0.001 and RLR tests than women (p = 0.037. Individuals who exercised regularly showed no differences in performance level when compared with their counterparts. Conclusion: The FR and LR tests were adapted for use in an aquatic environment. Males performed better on the FR and RLR tests, when compared to females. There was no correlation between the FR and LR tests and weight, height, Body Mass Index (BMI, foot length or length of the dominant upper limb.

Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury

Directory of Open Access Journals (Sweden)

Elaine Anna Corbett

2014-05-01

Full Text Available Cervical spinal cord injury (SCI paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such

The Scientific Status of Learning Styles Theories

Science.gov (United States)

Willingham, Daniel T.; Hughes, Elizabeth M.; Dobolyi, David G.

2015-01-01

Theories of learning styles suggest that individuals think and learn best in different ways. These are not differences of ability but rather preferences for processing certain types of information or for processing information in certain types of way. If accurate, learning styles theories could have important implications for instruction because…

Prevention of Learned Helplessness in Humans.

Science.gov (United States)

Klee, Steven; Meyer, Robert G.

1979-01-01

Explored prevention of learned helplessness through the use of thermal biofeedback training and varied explanations of performance. It was found that only in the biofeedback group receiving accurate feedback was there any prevention of the subsequent development of learned helplessness behavior. (Author)

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models.

Science.gov (United States)

Oguz, Ozgur S; Zhou, Zhehua; Glasauer, Stefan; Wollherr, Dirk

2018-04-03

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.

Two different motor learning mechanisms contribute to learning reaching movements in a rotated visual environment [version 2; referees: 2 approved, 1 approved with reservations

Directory of Open Access Journals (Sweden)

Virginia Way Tong Chu

2014-12-01

Full Text Available Practice of movement in virtual-reality and other artificially altered environments has been proposed as a method for rehabilitation following neurological injury and for training new skills in healthy humans.  For such training to be useful, there must be transfer of learning from the artificial environment to the performance of desired skills in the natural environment.  Therefore an important assumption of such methods is that practice in the altered environment engages the same learning and plasticity mechanisms that are required for skill performance in the natural environment.  We test the hypothesis that transfer of learning may fail because the learning and plasticity mechanism that adapts to the altered environment is different from the learning mechanism required for improvement of motor skill.  In this paper, we propose that a model that separates skill learning and environmental adaptation is necessary to explain the learning and aftereffects that are observed in virtual reality experiments.  In particular, we studied the condition where practice in the altered environment should lead to correct skill performance in the original environment. Our 2-mechanism model predicts that aftereffects will still be observed when returning to the original environment, indicating a lack of skill transfer from the artificial environment to the original environment. To illustrate the model prediction, we tested 10 healthy participants on the interaction between a simple overlearned motor skill (straight hand movements to targets in different directions and an artificially altered visuomotor environment (rotation of visual feedback of the results of movement.  As predicted by the models, participants show adaptation to the altered environment and after-effects on return to the baseline environment even when practice in the altered environment should have led to correct skill performance.  The presence of aftereffect under all conditions that

Kids Know Their Schools Best: Reaching out to Them Can Improve Designs and Build Community Good Will

Science.gov (United States)

Carlson, Michael

2010-01-01

More now than ever, our schools need to reach out and engage students. Dropout rates are high, achievement lags and increasingly students view schools as out of touch with their lives and their futures. Solutions to these problems are complex but I believe that making learning environments reflect student attitudes and perspectives plays an…

Reciprocity within Biochemistry and Biology Service-Learning

Science.gov (United States)

Santas, Amy J.

2009-01-01

Service-learning has become a popular pedagogy because of its numerous and far-reaching benefits (e.g. student interest, engagement, and retention). In part, the benefits are a result of the student learning while providing a service that reflects a true need--not simply an exercise. Although service-learning projects have been developed in the…

Different strategy of hand choice after learning of constant and incremental dynamical perturbation in arm reaching

Directory of Open Access Journals (Sweden)

Chie eHabagishi

2014-02-01

Full Text Available In daily life, we encounter situations where we must quickly decide which hand to use for a motor action. Here, we investigated whether the hand chosen for a motor action varied over a short timescale (i.e., hours with changes in arm dynamics. Participants performed a reaching task in which they moved a specified hand to reach a target on a virtual reality display. During the task, a resistive viscous force field was abruptly applied to only the dominant hand. To evaluate changes in hand choice caused by this perturbation, participants performed an interleaved choice test in which they could freely choose either hand for reaching. Furthermore, to investigate the effect of temporal changes on arm dynamics and hand choice, we exposed the same participants to another condition in which the force field was introduced gradually. When the abrupt force was applied, use of the perturbed hand significantly decreased and not changed during the training. In contrast, when the incremental force was applied, use of the perturbed hand gradually decreased as force increased. Surprisingly, even though the final amount of force was identical between the two conditions, hand choice was significantly biased toward the unperturbed hand in the gradual condition. These results suggest that time-varying changes in arm dynamics may have a greater influence on hand choice than the amplitude of the resistant force itself.

Integration of egocentric and allocentric information during memory-guided reaching to images of a natural environment

Directory of Open Access Journals (Sweden)

Katja eFiehler

2014-08-01

Full Text Available When interacting with our environment we generally make use of egocentric and allocentric object information by coding object positions relative to the observer or relative to the environment, respectively. Bayesian theories suggest that the brain integrates both sources of information optimally for perception and action. However, experimental evidence for egocentric and allocentric integration is sparse and has only been studied using abstract stimuli lacking ecological relevance. Here, we investigated the use of egocentric and allocentric information during memory-guided reaching to images of naturalistic scenes. Participants encoded a breakfast scene containing six objects on a table (local objects and three objects in the environment (global objects. After a 2s delay, a visual test scene reappeared for 1s in which one local object was missing (=target and of the remaining, one, three or five local objects or one of the global objects were shifted to the left or to the right. The offset of the test scene prompted participants to reach to the target as precisely as possible. Only local objects served as potential reach targets and thus were task-relevant. When shifting objects we predicted accurate reaching if participants only used egocentric coding of object position and systematic shifts of reach endpoints if allocentric information were used for movement planning. We found that reaching movements were largely affected by allocentric shifts showing an increase in endpoint errors in the direction of object shifts with the number of local objects shifted. No effect occurred when one local or one global object was shifted. Our findings suggest that allocentric cues are indeed used by the brain for memory-guided reaching towards targets in naturalistic visual scenes. Moreover, the integration of egocentric and allocentric object information seems to depend on the extent of changes in the scene.

Adults Learn in a Different Way

Directory of Open Access Journals (Sweden)

Ema Perme

1996-12-01

Full Text Available Due to demand of praxis a new programme on a field of adult education has been created. The advisers at Job Centre in Maribor have namely established the fact that there is a great number of unemployed who take part in different educational programmes to become more competitive on labour market and whose motivation for further learning/education is on a very low level. The presence of fear in them can also be connected with the lack of knowledge of different learning techniques. • Adults Learn in a Different Way' is a programme designed to help those with motivation problems and/or problems with using appropriate learning techniques. During the 16 hour programme participants work on the following topics: • ways the adults learn, • the significance of different learning types, • importance of music for more successful learning, • strategies for making learning plan, • learning techniques with an emphasis on mindmaping, • how to define concrete learning goals, • how to reach goals concerning our own personal significance and abilities. Seven experimental realisations in the past year showed some very encouraging results. With the help of anonymous questionnaires and personal talks with participants 6 months after they had attended the programme we got first feedback information. All the participants find the programme useful and the content of it helpful for making their own learning plan and strategies. They are able to concentrate better, they are able to reach their learning goals step by step as planned and they would all recommend the programme to their friends and acquaintances.

Rural Embedded Assistants for Community Health (REACH) network: first-person accounts in a community-university partnership.

Science.gov (United States)

Brown, Louis D; Alter, Theodore R; Brown, Leigh Gordon; Corbin, Marilyn A; Flaherty-Craig, Claire; McPhail, Lindsay G; Nevel, Pauline; Shoop, Kimbra; Sterner, Glenn; Terndrup, Thomas E; Weaver, M Ellen

2013-03-01

Community research and action projects undertaken by community-university partnerships can lead to contextually appropriate and sustainable community improvements in rural and urban localities. However, effective implementation is challenging and prone to failure when poorly executed. The current paper seeks to inform rural community-university partnership practice through consideration of first-person accounts from five stakeholders in the Rural Embedded Assistants for Community Health (REACH) Network. The REACH Network is a unique community-university partnership aimed at improving rural health services by identifying, implementing, and evaluating innovative health interventions delivered by local caregivers. The first-person accounts provide an insider's perspective on the nature of collaboration. The unique perspectives identify three critical challenges facing the REACH Network: trust, coordination, and sustainability. Through consideration of the challenges, we identified several strategies for success. We hope readers can learn their own lessons when considering the details of our partnership's efforts to improve the delivery infrastructure for rural healthcare.

Learning against the Clock: Examining Learning and Development Concepts in "The Curious Case of Benjamin Button"

Science.gov (United States)

Koenig, Allison L.; Smith, Amber R.

2013-01-01

Media and popular culture reach broad audiences and have the potential to be an invaluable teaching resource in terms of promoting adult education and learning. Human resource development instructors can use media artifacts (e.g., films, television, novels, and cartoons) as useful methods to demonstrate learning theory and adult development…

Reaching Beyond The Stars

Science.gov (United States)

Baker, Mariah; Rosenthal, L.; Gaughan, A.; Hopkins, E.

2014-01-01

Strawbridge Observatory at Haverford College is home to a undergraduate-led public observing program. Our program holds ~once monthly public events throughout the academic year that take advantage of eyepiece observing on our 16-inch and 12-inch telescopes as well as of the classroom, library, and projection system. These resources allow us to organize a variety of astronomy related activities that are engaging for individuals of all ages: accessible student talks, current film screenings and even arts and crafts for the families who attend with young children. These events aim to spark curiosity in others about scientific discovery and about the remarkable nature of the world in which we live. In addition to exciting local families about astronomy, this program has excited Haverford students from a range of disciplines about both science and education. Being entirely student led means that we are able to take the initiative in planning, coordinating and running all events, fostering an atmosphere of collaboration, experimentation and commitment amongst our volunteers. Additionally, this program is one of the few at Haverford that regularly reaches beyond the campus walls to promote and build relationships with the outside community. In light of this, our program presents a distinctive and enlightening opportunity for student volunteers: we get to use our scientific backgrounds to educate a general audience, while also learning from them about how to communicate and inspire in others the excitement we feel about the subject of astronomy. The work on this project has been supported by NSF AST-1151462.

An investigation of the neural circuits underlying reaching and reach-to-grasp movements: from planning to execution.

Directory of Open Access Journals (Sweden)

Chiara eBegliomini

2014-09-01

Full Text Available Experimental evidence suggests the existence of a sophisticated brain circuit specifically dedicated to reach-to-grasp planning and execution, both in human and non human primates (Castiello, 2005. Studies accomplished by means of neuroimaging techniques suggest the hypothesis of a dichotomy between a reach-to-grasp circuit, involving the intraparietal area (AIP, the dorsal and ventral premotor cortices (PMd and PMv - Castiello and Begliomini, 2008; Filimon, 2010 and a reaching circuit involving the medial intraparietal area (mIP and the Superior Parieto-Occipital Cortex (SPOC (Culham et al., 2006. However, the time course characterizing the involvement of these regions during the planning and execution of these two types of movements has yet to be delineated. A functional magnetic resonance imaging (fMRI study has been conducted, including reach-to grasp and reaching only movements, performed towards either a small or a large stimulus, and Finite Impulse Response model (FIR - Henson, 2003 was adopted to monitor activation patterns from stimulus onset for a time window of 10 seconds duration. Data analysis focused on brain regions belonging either to the reaching or to the grasping network, as suggested by Castiello & Begliomini (2008.Results suggest that reaching and grasping movements planning and execution might share a common brain network, providing further confirmation to the idea that the neural underpinnings of reaching and grasping may overlap in both spatial and temporal terms (Verhagen et al., 2013.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

Science.gov (United States)

Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

2018-05-10

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

Science.gov (United States)

Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

2015-06-07

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modulating peripersonal and extrapersonal reach space via tool use: a comparison between 6- to 12-year-olds and young adults.

Science.gov (United States)

Caçola, Priscila; Gabbard, Carl

2012-04-01

This study examined age-related characteristics associated with tool use in the perception and modulation of peripersonal and extrapersonal space. Seventy-six (76) children representing age groups 7-, 9-, 11 years and 36 adults were presented with two experiments using an estimation of reach paradigm involving arm and tool conditions and a switch-block of the opposite condition. Experiment 1 tested Arm and Tool (20 cm length) estimation and found a significant effect for Age, Space, and an Age × Space interaction (ps < 0.05). Both children and adults were less accurate in extrapersonal space, indicating an overestimation bias. Interestingly, the adjustment period during the switch-block condition was immediate and similar across age. Experiment 2 was similar to Experiment 1 with the exception of using a 40-cm-length tool. Results also revealed an age effect and a difference in Space (ps < 0.05), however, participants underestimated. Speculatively, participants were less confident when presented with a longer tool, even though the adjustment period with both tool lengths was similar. Considered together, these results hint that: (1) children as young as 6 years of age are capable of being as accurate when estimating reach with a tool as they are with their arm, (2) the adjustment period associated with extending and retracting spaces is immediate rather than gradual, and (3) tool length influences estimations of reach.

REACH: impact on the US cosmetics industry?

Science.gov (United States)

Pouillot, Anne; Polla, Barbara; Polla, Ada

2009-03-01

The Registration, Evaluation, Authorization and restriction of Chemicals (REACH) is a recent European regulation on chemical substances meant to protect human health and the environment. REACH imposes the "precautionary principle" where additional data and definitive action are required when uncertainty is identified. The cosmetics industry is only partially concerned by REACH: while the stages of registration and evaluation apply to cosmetics, those of authorization and restriction most likely will not, as cosmetic ingredients are already subject to regulation by various agencies and directives. REACH has potential benefits to the industry including the possibility of reassuring consumers and improving their image of chemicals and cosmetics. However, REACH also has potential disadvantages, mainly with regard to impeding innovation. The American cosmetics industry will be affected by REACH, because all US manufacturers who export substances to Europe will have to fully comply with REACH.

On-line compensation for perturbations of a reaching movement is cerebellar dependent: support for the task dependency hypothesis.

Science.gov (United States)

Shimansky, Yury; Wang, Jian-Jun; Bauer, Richard A; Bracha, Vlastislav; Bloedel, James R

2004-03-01

Although the cerebellum has been shown to be critical for the acquisition and retention of adaptive modifications in certain reflex behaviors, this structure's role in the learning of motor skills required to execute complex voluntary goal-directed movements still is unclear. This study explores this issue by analyzing the effects of inactivating the interposed and dentate cerebellar nuclei on the adaptation required to compensate for an external elastic load applied during a reaching movement. We show that cats with these nuclei inactivated can adapt to predictable perturbations of the forelimb during a goal-directed reach by including a compensatory component in the motor plan prior to movement initiation. In contrast, when comparable compensatory modifications must be triggered on-line because the perturbations are applied in randomized trials (i.e., unpredictably), such adaptive responses cannot be executed or reacquired after the interposed and dentate nuclei are inactivated. These findings provide the first demonstration of the condition-dependent nature of the cerebellum's contribution to the learning of a specific volitional task.

Self-learning Monte Carlo (dynamical biasing)

International Nuclear Information System (INIS)

Matthes, W.

1981-01-01

In many applications the histories of a normal Monte Carlo game rarely reach the target region. An approximate knowledge of the importance (with respect to the target) may be used to guide the particles more frequently into the target region. A Monte Carlo method is presented in which each history contributes to update the importance field such that eventually most target histories are sampled. It is a self-learning method in the sense that the procedure itself: (a) learns which histories are important (reach the target) and increases their probability; (b) reduces the probabilities of unimportant histories; (c) concentrates gradually on the more important target histories. (U.K.)

AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model

Directory of Open Access Journals (Sweden)

Jianzhu Ma

2015-01-01

Full Text Available Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

'Learn the signs. Act early': a campaign to help every child reach his or her full potential.

Science.gov (United States)

Daniel, K L; Prue, C; Taylor, M K; Thomas, J; Scales, M

2009-09-01

To examine the application of a social marketing approach to increase the early identification and treatment of autism and other developmental disorders. The intervention used formative research, behaviour change theory and traditional social marketing techniques to develop a campaign targeting parents, healthcare professionals and early educators to increase awareness of autism and other developmental delays, and to prompt action if a developmental delay was suspected. Using social marketing principles, the Centers for Disease Control and Prevention applied baseline research with the target audiences to understand the barriers and motivators to behaviour change, which included a lack of knowledge and resources (barriers), along with a willingness to learn and do more (motivators). Focus group testing of potential campaign concepts led to one particular approach and accompanying images, which together increased perceived severity of the problem and encouraged taking action. The audience research also helped to shape the marketing mix (product, price, place and promotion). Three-year follow-up research in this case study indicates a significant change in parent target behaviours, particularly among parents aware of the campaign, and substantially more healthcare professionals believe that they have the resources to educate parents about monitoring their child's cognitive, social and physical development. Qualitative results from early educators and childcare professional associations have been positive about products developed for daycare settings. The application of social marketing principles, behavior change theory and audience research was an effective approach to changing behaviours in this case. Understanding what the target audiences want and need, looking beyond parents to engage healthcare professionals and early educators, and engaging many strategic partners to extend the reach of the message helped campaign planners to develop a campaign that resonated

Designing Technology-Enabled Instruction to Utilize Learning Analytics

Science.gov (United States)

Davies, Randall; Nyland, Robert; Bodily, Robert; Chapman, John; Jones, Brian; Young, Jay

2017-01-01

A key notion conveyed by those who advocate for the use of data to enhance instruction is an awareness that learning analytics has the potential to improve instruction and learning but is not currently reaching that potential. Gibbons (2014) suggested that a lack of learning facilitated by current technology-enabled instructional systems may be…

Ultra-accurate collaborative information filtering via directed user similarity

Science.gov (United States)

Guo, Q.; Song, W.-J.; Liu, J.-G.

2014-07-01

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.

The secret adventures of order: globalization, education and transformative social justice learning

Directory of Open Access Journals (Sweden)

Carlos Alberto Torres

Full Text Available There are many definitions of globalization, or perhaps more accurately, there are many globalizations. Discussing the four faces of globalization - globalization from above, globalization from below, the globalization of human rights, and the globalization of the war against terrorism - and their impacts on education and learning, this article offers an analysis of neoliberal globalization and how "competition-based reforms" affected educational policy in K-12 and higher education. These reforms are characterized by efforts to create measurable performance standards through extensive standardized testing (the new standards and accountability movement, introduction of new teaching and learning methods leading to the expectation of better performance at low cost (e.g., universalization of textbooks, and improvements in the selection and training of teachers. Competition-based reforms in higher education tend to adopt a vocational orientation and to reflect the point of view that colleges and universities exist largely to serve the economic well-being of a society. Privatization is the final major reform effort linked to neoliberal globalization and perhaps the most dominant. As an alternative, the article provides insights into the possibilities of employing the concept of marginality as a central construct for a model of transformative social justice learning. Following the inspiration of Paulo Freire, I argue that transformative social justice learning is a social, political and pedagogical practice which will take place when people reach a deeper, richer, more textured and nuanced understanding of themselves and their world.

The economic value of accurate wind power forecasting to utilities

Energy Technology Data Exchange (ETDEWEB)

Watson, S J [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G; Joensen, A [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

Machine learning systems

Energy Technology Data Exchange (ETDEWEB)

Forsyth, R

1984-05-01

With the dramatic rise of expert systems has come a renewed interest in the fuel that drives them-knowledge. For it is specialist knowledge which gives expert systems their power. But extracting knowledge from human experts in symbolic form has proved arduous and labour-intensive. So the idea of machine learning is enjoying a renaissance. Machine learning is any automatic improvement in the performance of a computer system over time, as a result of experience. Thus a learning algorithm seeks to do one or more of the following: cover a wider range of problems, deliver more accurate solutions, obtain answers more cheaply, and simplify codified knowledge. 6 references.

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis

NARCIS (Netherlands)

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-01-01

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require

Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

2017-01-01

of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show...

A distributed algorithm for machine learning

Science.gov (United States)

Chen, Shihong

2018-04-01

This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

Generalist genes and learning disabilities.

Science.gov (United States)

Plomin, Robert; Kovas, Yulia

2005-07-01

The authors reviewed recent quantitative genetic research on learning disabilities that led to the conclusion that genetic diagnoses differ from traditional diagnoses in that the effects of relevant genes are largely general rather than specific. This research suggests that most genes associated with common learning disabilities--language impairment, reading disability, and mathematics disability--are generalists in 3 ways. First, genes that affect common learning disabilities are largely the same genes responsible for normal variation in learning abilities. Second, genes that affect any aspect of a learning disability affect other aspects of the disability. Third, genes that affect one learning disability are also likely to affect other learning disabilities. These quantitative genetic findings have far-reaching implications for molecular genetics and neuroscience as well as psychology. Copyright 2005 APA, all rights reserved.

On the organizational learning work process

International Nuclear Information System (INIS)

Weil, Richard; Apostolakis, George

2000-01-01

This paper presents an organizational learning work process for use at nuclear power plants or other high-risk industries. Relying on insights gained from surveying organizational learning activities at nuclear power plants, the proposed work process synthesizes distributed learning activities and improves upon existing organizational learning processes. A root-cause analysis that targets organizational factors is presented. Additionally, a more accurate and objective methodology for prioritizing operating experience is presented. This methodology was applied to a case study during a workshop with utility personnel held at MIT. (author)

Accurate and stable equal-pressure measurements of water vapor transmission rate reaching the 10-6 g m-2 day-1 range

Science.gov (United States)

Nakano, Yoichiro; Yanase, Takashi; Nagahama, Taro; Yoshida, Hajime; Shimada, Toshihiro

2016-10-01

The water vapor transmission rate (WVTR) of a gas barrier coating is a critically important parameter for flexible organic device packaging, but its accurate measurement without mechanical stress to ultrathin films has been a significant challenge in instrumental analysis. At the current stage, no reliable results have been reported in the range of 10-6 g m-2 day-1 that is required for organic light emitting diodes (OLEDs). In this article, we describe a solution for this difficult, but important measurement, involving enhanced sensitivity by a cold trap, stabilized temperature system, pumped sealing and calibration by a standard conductance element.

Exploring hypothetical learning progressions for the chemistry of nitrogen and nuclear processes

Science.gov (United States)

Henry, Deborah McKern

Chemistry is a bridge that connects a number of scientific disciplines. High school students should be able to determine whether scientific information is accurate, how chemistry applies to daily life, and the mechanism by which systems operate (NRC, 2012). This research focuses on describing hypothetical learning progressions for student understanding of the chemical reactions of nitrogen and nuclear processes and examines whether there is consistency in scientific reasoning between these two distinct conceptual areas. The constant comparative method was used to analyze the written products of students including homework, formative and summative tests, laboratory notebooks, reflective journals, written presentations, and discussion board contributions via Edmodo (an online program). The ten participants were 15 and 16 year old students enrolled in a general high school chemistry course. Instruction took place over a ten week period. The learning progression levels ranged from 0 to 4 and were described as missing, novice, intermediate, proficient, and expert. The results were compared to the standards set by the NRC with a lower anchor (expectations for grade 8) and upper anchor (expectations for grade 12). The results indicate that, on average, students were able to reach an intermediate level of understanding for these concepts.

Active learning of neuron morphology for accurate automated tracing of neurites

Science.gov (United States)

Gala, Rohan; Chapeton, Julio; Jitesh, Jayant; Bhavsar, Chintan; Stepanyants, Armen

2014-01-01

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users. PMID

Active learning of neuron morphology for accurate automated tracing of neurites

Directory of Open Access Journals (Sweden)

Rohan eGala

2014-05-01

Full Text Available Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by

A deep learning approach for pose estimation from volumetric OCT data.

Science.gov (United States)

Gessert, Nils; Schlüter, Matthias; Schlaefer, Alexander

2018-05-01

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Computer-based personality judgments are more accurate than those made by humans.

Science.gov (United States)

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-27

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Opportunities for Socioemotional Learning in Music Classrooms

Science.gov (United States)

Jacobi, Bonnie S.

2012-01-01

The elementary music class is an ideal setting for building socioemotional skills in children. These skills can assist children in their early music learning through brain development, and they become increasingly important as students reach higher levels of musicianship. Socioemotional learning programs are currently being used to reduce at-risk…

Deep-Learning-Based Drug-Target Interaction Prediction.

Science.gov (United States)

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Using computer-assisted learning to engage diverse learning styles in understanding business management principles.

Science.gov (United States)

Frost, Mary E; Derby, Dustin C; Haan, Andrea G

2013-01-01

Objective : Changes in small business and insurance present challenges for newly graduated chiropractors. Technology that reaches identified, diverse learning styles may assist the chiropractic student in business classes to meet course outcomes better. Thus, the purpose of our study is to determine if the use of technology-based instructional aids enhance students' mastery of course learning outcomes. Methods : Using convenience sampling, 86 students completed a survey assessing course learning outcomes, learning style, and the helpfulness of lecture and computer-assisted learning related to content mastery. Quantitative analyses occurred. Results : Although respondents reported not finding the computer-assisted learning as helpful as the lecture, significant relationships were found between pre- and post-assisted learning measures of the learning outcomes 1 and 2 for the visual and kinesthetic groups. Surprisingly, however, all learning style groups exhibited significant pre- and post-assisted learning appraisal relationships with learning outcomes 3 and 4. Conclusion : While evidence exists within the current study of a relationship between students' learning of the course content corollary to the use of technologic instructional aids, the exact nature of the relationship remains unclear.

The database for reaching experiments and models.

Directory of Open Access Journals (Sweden)

Ben Walker

Full Text Available Reaching is one of the central experimental paradigms in the field of motor control, and many computational models of reaching have been published. While most of these models try to explain subject data (such as movement kinematics, reaching performance, forces, etc. from only a single experiment, distinct experiments often share experimental conditions and record similar kinematics. This suggests that reaching models could be applied to (and falsified by multiple experiments. However, using multiple datasets is difficult because experimental data formats vary widely. Standardizing data formats promises to enable scientists to test model predictions against many experiments and to compare experimental results across labs. Here we report on the development of a new resource available to scientists: a database of reaching called the Database for Reaching Experiments And Models (DREAM. DREAM collects both experimental datasets and models and facilitates their comparison by standardizing formats. The DREAM project promises to be useful for experimentalists who want to understand how their data relates to models, for modelers who want to test their theories, and for educators who want to help students better understand reaching experiments, models, and data analysis.

A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

Science.gov (United States)

Yan, Wang; Jiajin, Le; Yun, Zhang

2014-01-01

The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results' evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer's obvious improvement of mapping error rate. PMID:25250372

Reach and get capability in a computing environment

Science.gov (United States)

Bouchard, Ann M [Albuquerque, NM; Osbourn, Gordon C [Albuquerque, NM

2012-06-05

A reach and get technique includes invoking a reach command from a reach location within a computing environment. A user can then navigate to an object within the computing environment and invoke a get command on the object. In response to invoking the get command, the computing environment is automatically navigated back to the reach location and the object copied into the reach location.

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

Virtual screening for cytochromes p450: successes of machine learning filters.

Science.gov (United States)

Burton, Julien; Ijjaali, Ismail; Petitet, François; Michel, André; Vercauteren, Daniel P

2009-05-01

Cytochromes P450 (CYPs) are crucial targets when predicting the ADME properties (absorption, distribution, metabolism, and excretion) of drugs in development. Particularly, CYPs mediated drug-drug interactions are responsible for major failures in the drug design process. Accurate and robust screening filters are thus needed to predict interactions of potent compounds with CYPs as early as possible in the process. In recent years, more and more 3D structures of various CYP isoforms have been solved, opening the gate of accurate structure-based studies of interactions. Nevertheless, the ligand-based approach still remains popular. This success can be explained by the growing number of available data and the satisfying performances of existing machine learning (ML) methods. The aim of this contribution is to give an overview of the recent achievements in ML applications to CYP datasets. Particularly, popular methods such as support vector machine, decision trees, artificial neural networks, k-nearest neighbors, and partial least squares will be compared as well as the quality of the datasets and the descriptors used. Consensus of different methods will also be discussed. Often reaching 90% of accuracy, the models will be analyzed to highlight the key descriptors permitting the good prediction of CYPs binding.

A Machine LearningFramework to Forecast Wave Conditions

Science.gov (United States)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

Effect of lead exposure on spatial learning and running speed in the short-tailed opossum, Monodelphis domestica (Didelphidae).

Science.gov (United States)

Punzo, F; Farmer, C

2004-01-01

Studies were conducted to assess the spatial learning ability in adult males of the short-tailed opossum, Monodelphis domestica using a T-maze, complex maze and elevated radial 8-arm maze. This is the first study of maze learning in opossums. In the T-maze, the performance of these animals improved over an 8-day training period. Eighty percent of the subjects initially trained to turn to the right for food reinforcement reached criterion (80% correct responses) by day 3 and all reached criterion by day 4. Reversal training (subjects then trained to turn to the left) was more difficult and required 8 days for all subjects to reach criterion. In the complex maze, 89% of the animals achieved the criterion level of performance (3 consecutive trials with 5 or fewer errors) on the eighth day of training and all reached criterion by day 10. The relative importance of intramaze vs. extramaze cues in directing choice behavior was investigated in the radial arm maze. A discrimination procedure was used which selectively rewarded subjects for following only one set of cues. Animals in the intramaze group obtained a food pellet from a cup at the end of each arm. In the extramaze group, the food cups were placed on a small platform just beyond the end of each arm. All subjects were initially trained to visit each arm with the maze in a fixed position (controls) and did so within 15 test sessions. Following these initial trials, the maze was rotated to a different position after each choice. For subjects in the intramaze group, the food moved in conjunction with the rotation of the arms thereby increasing the relevance of intramaze cues. In the extramaze group, extramaze cues became more important because the food remained on the platforms in the same position in the room. Animals in the extramaze group performed significantly better than chance whereas the intramaze subjects did not. This indicates that intramaze cues are not as important as extramaze cues for accurate choice

Beyond the Factory Model: Oakland Teachers Learn How to Blend

Science.gov (United States)

Jacobs, Joanne

2014-01-01

This article describes an Oakland Unified schools program of "blended learning" that is designed to reach students who are academically all over the map. Blended learning combines brick-and-mortar schooling with online education "with some element of student control over time, place, path, and/or pace" of learning. The program…

Reaching the unreached.

Science.gov (United States)

Ariyaratne, A T

1989-01-01

Embodied in the child survival revolution are ideological, methodological, and organizational innovations aimed at radical change in the condition of the world's children as rapidly as possible. In countries such as Sri Lanka, child survival and health for all by the year 2000 often seem to be impossible goals, given the tumultuous socioeconomic and political conditions. In Sri Lanka, the quality of life has been eroded, not enhanced, by the importation of Western technology and managerial capitalism and the destruction of indigenous processes. The chaos and violence that have been brought into the country have made it difficult to reach the poor children, women, and refugees in rural areas with primary health care interventions. Sri Lanka's unreachable--the decision making elites--have blocked access to the unreached--the urban and rural poor. If governments are to reach the unreached, they must remove the obstacles to a people-centered, community development process. It is the people themselves, and the institutions of their creation, that can reach the children amidst them in greatest need. To achieve this task, local communities must be provided with basic human rights, the power to make decisions that affect their lives, necessary resources, and appropriate technologies. Nongovernmental organizations can play a crucial role as bridges between the unreached and the unreachable by promoting community empowerment, aiding in the formation of networks of community organizations, and establishing linkages with government programs. If the ruling elites in developing countries can be persuaded to accommodate the needs and aspirations of those who, to date, have been excluded from the development process, the child survival revolution can be a nonviolent one.

Aligning Theory and Design: The Development of an Online Learning Intervention to Teach Evidence-based Practice for Maximal Reach.

Science.gov (United States)

Delagran, Louise; Vihstadt, Corrie; Evans, Roni

2015-09-01

Online educational interventions to teach evidence-based practice (EBP) are a promising mechanism for overcoming some of the barriers to incorporating research into practice. However, attention must be paid to aligning strategies with adult learning theories to achieve optimal outcomes. We describe the development of a series of short self-study modules, each covering a small set of learning objectives. Our approach, informed by design-based research (DBR), involved 6 phases: analysis, design, design evaluation, redesign, development/implementation, and evaluation. Participants were faculty and students in 3 health programs at a complementary and integrative educational institution. We chose a reusable learning object approach that allowed us to apply 4 main learning theories: events of instruction, cognitive load, dual processing, and ARCS (attention, relevance, confidence, satisfaction). A formative design evaluation suggested that the identified theories and instructional approaches were likely to facilitate learning and motivation. Summative evaluation was based on a student survey (N=116) that addressed how these theories supported learning. Results suggest that, overall, the selected theories helped students learn. The DBR approach allowed us to evaluate the specific intervention and theories for general applicability. This process also helped us define and document the intervention at a level of detail that covers almost all the proposed Guideline for Reporting Evidence-based practice Educational intervention and Teaching (GREET) items. This thorough description will facilitate the interpretation of future research and implementation of the intervention. Our approach can also serve as a model for others considering online EBP intervention development.

Machine learning of molecular properties: Locality and active learning

Science.gov (United States)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Science.gov (United States)

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

The effects of video modeling with voiceover instruction on accurate implementation of discrete-trial instruction.

Science.gov (United States)

Vladescu, Jason C; Carroll, Regina; Paden, Amber; Kodak, Tiffany M

2012-01-01

The present study replicates and extends previous research on the use of video modeling (VM) with voiceover instruction to train staff to implement discrete-trial instruction (DTI). After staff trainees reached the mastery criterion when teaching an adult confederate with VM, they taught a child with a developmental disability using DTI. The results showed that the staff trainees' accurate implementation of DTI remained high, and both child participants acquired new skills. These findings provide additional support that VM may be an effective method to train staff members to conduct DTI.

Mild Contralesional Hypothermia Reduces Use of the Unimpaired Forelimb in a Skilled Reaching Task After Motor Cortex Injury in Rats.

Science.gov (United States)

Klahr, Ana C; Fagan, Kelly; Aziz, Jasmine R; John, Roseleen; Colbourne, Frederick

2018-06-01

Therapeutic hypothermia (TH) mitigates neuronal injury in models of ischemic stroke. Although this therapy is meant for injured tissue, most protocols cool the whole body, including the contralesional hemisphere. Neuroplasticity responses within this hemisphere can affect functional outcome. Thus, cooling the contralesional hemisphere serves no clear neuroprotective function and may instead be detrimental. In this study, we cooled the contralesional hemisphere to determine whether this harms behavioral recovery after cortical injury in rats. All rats were trained on skilled reaching and walking tasks. Rats then received a motor cortex insult contralateral to their dominant paw after which they were randomly assigned to focal contralesional TH (∼33°C) for 1-48, 1-97, or 48-96 hours postinjury, or to a normothermic control group. Contralesional cooling did not impact lesion volume (p = 0.371) and had minimal impact on neurological outcome of the impaired limb. However, rats cooled early were significantly less likely to shift paw preference to the unimpaired paw (p ≤ 0.043), suggesting that cooling reduced learned nonuse. In a second experiment, we tested whether cooling impaired learning of the skilled reaching task in naive rats. Localized TH applied to the hemisphere contralateral or ipsilateral to the preferred paw did not impair learning (p ≥ 0.677) or dendritic branching/length in the motor cortex (p ≥ 0.105). In conclusion, localized TH did not impair learning or plasticity in the absence of neural injury, but contralesional TH may reduce unwanted shifts in limb preference after stroke.

Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

Science.gov (United States)

Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

2016-06-01

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

Sub-micron accurate track navigation method ''Navi'' for the analysis of Nuclear Emulsion

International Nuclear Information System (INIS)

Yoshioka, T; Yoshida, J; Kodama, K

2011-01-01

Sub-micron accurate track navigation in Nuclear Emulsion is realized by using low energy signals detected by automated Nuclear Emulsion read-out systems. Using those much dense ''noise'', about 10 4 times larger than the real tracks, the accuracy of the track position navigation reaches to be sub micron only by using the information of a microscope field of view, 200 micron times 200 micron. This method is applied to OPERA analysis in Japan, i.e. support of human eye checks of the candidate tracks, confirmation of neutrino interaction vertexes and to embed missing track segments to the track data read-out by automated systems.

Sub-micron accurate track navigation method ``Navi'' for the analysis of Nuclear Emulsion

Science.gov (United States)

Yoshioka, T.; Yoshida, J.; Kodama, K.

2011-03-01

Sub-micron accurate track navigation in Nuclear Emulsion is realized by using low energy signals detected by automated Nuclear Emulsion read-out systems. Using those much dense ``noise'', about 104 times larger than the real tracks, the accuracy of the track position navigation reaches to be sub micron only by using the information of a microscope field of view, 200 micron times 200 micron. This method is applied to OPERA analysis in Japan, i.e. support of human eye checks of the candidate tracks, confirmation of neutrino interaction vertexes and to embed missing track segments to the track data read-out by automated systems.

The effectiveness of nurses' ability to interpret basic electrocardiogram strips accurately using different learning modalities.

Science.gov (United States)

Spiva, LeeAnna; Johnson, Kimberly; Robertson, Bethany; Barrett, Darcy T; Jarrell, Nicole M; Hunter, Donna; Mendoza, Inocencia

2012-02-01

Historically, the instructional method of choice has been traditional lecture or face-to-face education; however, changes in the health care environment, including resource constraints, have necessitated examination of this practice. A descriptive pre-/posttest method was used to determine the effectiveness of alternative teaching modalities on nurses' knowledge and confidence in electrocardiogram (EKG) interpretation. A convenience sample of 135 nurses was recruited in an integrated health care system in the Southeastern United States. Nurses attended an instructor-led course, an online learning (e-learning) platform with no study time or 1 week of study time, or an e-learning platform coupled with a 2-hour post-course instructor-facilitated debriefing with no study time or 1 week of study time. Instruments included a confidence scale, an online EKG test, and a course evaluation. Statistically significant differences in knowledge and confidence were found for individual groups after nurses participated in the intervention. Statistically significant differences were found in pre-knowledge and post-confidence when groups were compared. Organizations that use various instructional methods to educate nurses in EKG interpretation can use different teaching modalities without negatively affecting nurses' knowledge or confidence in this skill. Copyright 2012, SLACK Incorporated.

Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

Science.gov (United States)

Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

2006-01-01

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

Theory of mind selectively predicts preschoolers' knowledge-based selective word learning.

Science.gov (United States)

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-11-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory-of-mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children's preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children's developing social cognition and early learning. © 2015 The British Psychological Society.

Theory of mind selectively predicts preschoolers’ knowledge-based selective word learning

Science.gov (United States)

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-01-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory of mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children’s preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children’s developing social cognition and early learning. PMID:26211504

A COMPARISON OF THE SIT-AND-REACH TEST AND THE BACK-SAVER SIT-AND-REACH TEST IN UNIVERSITY STUDENTS

Directory of Open Access Journals (Sweden)

Pedro A. López-Miñarro

2009-03-01

Full Text Available This study compares the forward reach score, spine and pelvis postures, and hamstring criterion-related validity (concurrent validity between the sit-and-reach test (SR and the back-saver sit-and-reach test (BS. Seventy-six men (mean age ± SD: 23.45 ± 3.96 years and 67 women (mean age ± SD: 23.85 ± 5.36 years were asked to perform three trials of SR, BS left (BSl, right (BSr, and passive straight leg raise (PSLR right and left (hamstring criterion measure in a randomized order. The thoracic, lumbar, and pelvis angles (measured with a Uni-level inclinometer and forward reach scores were recorded once the subjects reached forward as far as possible without flexing the knees. A repeated measure ANOVA was performed followed by Bonferroni´s post hoc test. Pearson correlation coefficients were used to define the relationships between SR and BS scores with respect to PSLR. In both men and women, the thoracic angle in BS was significantly greater than in SR (p<0.016. However, no significant differences were found between the tests in lumbar angle, pelvic angle, and forward reach scores. The concurrent validity of the forward reach score as a measure of hamstring extensibility was moderate in women (0.66 0. 76 and weak to moderate in men (0.51 0.59. The concurrent validity was slightly higher in SR than in BS, although no significant differences between the correlation values were observed. There were significant differences in the thoracic angle between the SR and BS, but not in the forward reach score. There was no difference in concurrent validity between the two tests. However, the traditional SR was preferred because it reached better concurrent validity than the BS

Upper Limb Rehabilitation Robot Powered by PAMs Cooperates with FES Arrays to Realize Reach-to-Grasp Trainings

Science.gov (United States)

Su, Chen; Jiang, Xiaobo

2017-01-01

The reach-to-grasp activities play an important role in our daily lives. The developed RUPERT for stroke patients with high stiffness in arm flexor muscles is a low-cost lightweight portable exoskeleton rehabilitation robot whose joints are unidirectionally actuated by pneumatic artificial muscles (PAMs). In order to expand the useful range of RUPERT especially for patients with flaccid paralysis, functional electrical stimulation (FES) is taken to activate paralyzed arm muscles. As both the exoskeleton robot driven by PAMs and the neuromuscular skeletal system under FES possess the highly nonlinear and time-varying characteristics, iterative learning control (ILC) is studied and is taken to control this newly designed hybrid rehabilitation system for reaching trainings. Hand function rehabilitation refers to grasping. Because of tiny finger muscles, grasping and releasing are realized by FES array electrodes and matrix scan method. By using the surface electromyography (EMG) technique, the subject's active intent is identified. The upper limb rehabilitation robot powered by PAMs cooperates with FES arrays to realize active reach-to-grasp trainings, which was verified through experiments. PMID:29065566

Upper Limb Rehabilitation Robot Powered by PAMs Cooperates with FES Arrays to Realize Reach-to-Grasp Trainings

Directory of Open Access Journals (Sweden)

Xikai Tu

2017-01-01

Full Text Available The reach-to-grasp activities play an important role in our daily lives. The developed RUPERT for stroke patients with high stiffness in arm flexor muscles is a low-cost lightweight portable exoskeleton rehabilitation robot whose joints are unidirectionally actuated by pneumatic artificial muscles (PAMs. In order to expand the useful range of RUPERT especially for patients with flaccid paralysis, functional electrical stimulation (FES is taken to activate paralyzed arm muscles. As both the exoskeleton robot driven by PAMs and the neuromuscular skeletal system under FES possess the highly nonlinear and time-varying characteristics, iterative learning control (ILC is studied and is taken to control this newly designed hybrid rehabilitation system for reaching trainings. Hand function rehabilitation refers to grasping. Because of tiny finger muscles, grasping and releasing are realized by FES array electrodes and matrix scan method. By using the surface electromyography (EMG technique, the subject’s active intent is identified. The upper limb rehabilitation robot powered by PAMs cooperates with FES arrays to realize active reach-to-grasp trainings, which was verified through experiments.

Accurate determination of the charge transfer efficiency of photoanodes for solar water splitting.

Science.gov (United States)

Klotz, Dino; Grave, Daniel A; Rothschild, Avner

2017-08-09

The oxygen evolution reaction (OER) at the surface of semiconductor photoanodes is critical for photoelectrochemical water splitting. This reaction involves photo-generated holes that oxidize water via charge transfer at the photoanode/electrolyte interface. However, a certain fraction of the holes that reach the surface recombine with electrons from the conduction band, giving rise to the surface recombination loss. The charge transfer efficiency, η t , defined as the ratio between the flux of holes that contribute to the water oxidation reaction and the total flux of holes that reach the surface, is an important parameter that helps to distinguish between bulk and surface recombination losses. However, accurate determination of η t by conventional voltammetry measurements is complicated because only the total current is measured and it is difficult to discern between different contributions to the current. Chopped light measurement (CLM) and hole scavenger measurement (HSM) techniques are widely employed to determine η t , but they often lead to errors resulting from instrumental as well as fundamental limitations. Intensity modulated photocurrent spectroscopy (IMPS) is better suited for accurate determination of η t because it provides direct information on both the total photocurrent and the surface recombination current. However, careful analysis of IMPS measurements at different light intensities is required to account for nonlinear effects. This work compares the η t values obtained by these methods using heteroepitaxial thin-film hematite photoanodes as a case study. We show that a wide spread of η t values is obtained by different analysis methods, and even within the same method different values may be obtained depending on instrumental and experimental conditions such as the light source and light intensity. Statistical analysis of the results obtained for our model hematite photoanode show good correlation between different methods for

A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

Directory of Open Access Journals (Sweden)

Wang Yan

2014-01-01

Full Text Available The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results’ evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer’s obvious improvement of mapping error rate.

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

Think Pair Share Using Realistic Mathematics Education Approach in Geometry Learning

Science.gov (United States)

Afthina, H.; Mardiyana; Pramudya, I.

2017-09-01

This research aims to determine the impact of mathematics learning applying Think Pair Share (TPS) using Realistic Mathematics Education (RME) viewed from mathematical-logical intelligence in geometry learning. Method that used in this research is quasi experimental research The result of this research shows that (1) mathematics achievement applying TPS using RME approach gives a better result than those applying direct learning model; (2) students with high mathematical-logical intelligence can reach a better mathematics achievement than those with average and low one, whereas students with average mathematical-logical intelligence can reach a better achievement than those with low one; (3) there is no interaction between learning model and the level of students’ mathematical-logical intelligence in giving a mathematics achievement. The impact of this research is that TPS model using RME approach can be applied in mathematics learning so that students can learn more actively and understand the material more, and mathematics learning become more meaningful. On the other hand, internal factors of students must become a consideration toward the success of students’ mathematical achievement particularly in geometry material.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression

OpenAIRE

Sato, Jo?o R.; Moll, Jorge; Green, Sophie; Deakin, John F.W.; Thomaz, Carlos E.; Zahn, Roland

2015-01-01

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the hi...

Planning of the Extended Reach well Dieksand 2; Planung der Extended Reach Bohrung Dieksand 2

Energy Technology Data Exchange (ETDEWEB)

Frank, U.; Berners, H. [RWE-DEA AG, Hamburg (Germany). Drilling Team Mittelplate und Dieksand; Hadow, A.; Klop, G.; Sickinger, W. [Wintershall AG Erdoelwerke, Barnstdorf (Germany); Sudron, K.

1998-12-31

The Mittelplate oil field is located 7 km offshore the town of Friedrichskoog. Reserves are estimated at 30 million tonnes of oil. At a production rate of 2,500 t/d, it will last about 33 years. The transport capacity of the offshore platform is limited, so that attempts were made to enhance production by constructing the extended reach borehole Dieksand 2. Details are presented. (orig.) [Deutsch] Das Erdoelfeld Mittelplate liegt am suedlichen Rand des Nationalparks Schleswig Holsteinisches Wattenmeer, ca. 7000 m westlich der Ortschaft Friedrichskoog. Die gewinnbaren Reserven betragen ca. 30 Millionen t Oel. Bei einer Foerderkapazitaet von 2.500 t/Tag betraegt die Foerderdauer ca. 33 Jahre. Aufgrund der begrenzten Transportkapazitaeten von der Insel, laesst sich durch zusaetzliche Bohrungen von der kuenstlichen Insel Mittelplate keine entscheidende Erhoehung der Foerderkapazitaet erzielen. Ab Sommer 1996 wurde erstmals die Moeglichkeit der Lagerstaettenerschliessung von Land untersucht. Ein im Mai 1997 in Hamburg etabliertes Drilling Team wurde mit der Aufgabe betraut, die Extended Reach Bohrung Dieksand 2 zu planen und abzuteufen. Die Planungsphasen fuer die Extended Reach Bohrung Dieksand 2 wurden aufgezeigt. Die fuer den Erfolg einer Extended Reach Bohrung wichtigen Planungsparameter wurden erlaeutert. Es wurden Wege gezeigt, wie bei diesem Projekt technische und geologische Risiken in der Planung mit beruecksichtigt und nach Beginn der Bohrung weiter bearbeitet werden koennen. (orig.)

Teratology testing under REACH.

Science.gov (United States)

Barton, Steve

2013-01-01

REACH guidelines may require teratology testing for new and existing chemicals. This chapter discusses procedures to assess the need for teratology testing and the conduct and interpretation of teratology tests where required.

Using Digital Photography to Supplement Learning of Biotechnology

Science.gov (United States)

Norflus, Fran

2012-01-01

The author used digital photography to supplement learning of biotechnology by students with a variety of learning styles and educational backgrounds. Because one approach would not be sufficient to reach all the students, digital photography was used to explain the techniques and results to the class instead of having to teach each student…

Computer-based personality judgments are more accurate than those made by humans

Science.gov (United States)

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-01

Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy. PMID:25583507

Technology-Enhanced Learning in Developing Nations: A review

Directory of Open Access Journals (Sweden)

Shalni Gulati

2008-02-01

Full Text Available Learning ‘using’ technologies has become a global phenomenon. The Internet is often seen as a value-neutral tool that potentially allows individuals to overcome the constraints of traditional elitist spaces and gain unhindered access to learning. It is widely suggested that online technologies can help address issues of educational equity and social exclusion, and open up democratic and accessible educational opportunities. The national governments and non-governmental agencies who fund educational endeavours in developing countries have advocated the use of new technologies to reduce the cost of reaching and educating large numbers of children and adults who are currently missing out on education. This paper presents an overview of the educational developments in open, distance, and technology-facilitated learning that aim to reach the educationally deprived populations of the world. It reveals the challenges encountered by children and adults in developing countries as they attempt to access available educational opportunities. The discussion questions whether, in face of these challenges, developing nations should continue to invest money, time, and effort into e-learning developments. Can technology-enhanced learning help address the poverty, literacy, social, and political problems in developing countries?

Observing Animal Behavior at the Zoo: A Learning Laboratory

Science.gov (United States)

Hull, Debra B.

2003-01-01

Undergraduate students in a learning laboratory course initially chose a species to study; researched that species' physical and behavioral characteristics; then learned skills necessary to select, operationalize, observe, and record animal behavior accurately. After their classroom preparation, students went to a local zoo to observe the behavior…

REACH: Evaluation Report and Executive Summary

Science.gov (United States)

Sibieta, Luke

2016-01-01

REACH is a targeted reading support programme designed to improve reading accuracy and comprehension in pupils with reading difficulties in Years 7 and 8. It is based on research by the Centre for Reading and Language at York and is delivered by specially trained teaching assistants (TAs). This evaluation tested two REACH interventions, one based…

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Science.gov (United States)

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution

An Active System for Visually-Guided Reaching in 3D across Binocular Fixations

Directory of Open Access Journals (Sweden)

Ester Martinez-Martin

2014-01-01

Full Text Available Based on the importance of relative disparity between objects for accurate hand-eye coordination, this paper presents a biological approach inspired by the cortical neural architecture. So, the motor information is coded in egocentric coordinates obtained from the allocentric representation of the space (in terms of disparity generated from the egocentric representation of the visual information (image coordinates. In that way, the different aspects of the visuomotor coordination are integrated: an active vision system, composed of two vergent cameras; a module for the 2D binocular disparity estimation based on a local estimation of phase differences performed through a bank of Gabor filters; and a robotic actuator to perform the corresponding tasks (visually-guided reaching. The approach’s performance is evaluated through experiments on both simulated and real data.

Global reach and engagement

Science.gov (United States)

2016-09-01

Popular culture reflects both the interests of and the issues affecting the general public. As concerns regarding climate change and its impacts grow, is it permeating into popular culture and reaching that global audience?

Adaptive Hands-On Control for Reaching and Targeting Tasks in Surgery

Directory of Open Access Journals (Sweden)

Elisa Beretta

2015-05-01

Full Text Available Cooperatively controlled robotic assistants can be used in surgery for the repetitive execution of targeting/reaching tasks, which require smooth motions and accurate placement of a tool inside a working area. A variable damping controller, based on a priori knowledge of the location of the surgical site, is proposed to enhance the physical human-robot interaction experience. The performance of this and of typical constant damping controllers is comparatively assessed using a redundant light-weight robot. Results show that it combines the positive features of both null (acceleration capabilities > 0.8m/s2 and optimal (mean pointing error < 1.5mm constant damping controllers, coupled with smooth and intuitive convergence to the target (direction changes reduced by 30%, which ensures that assisted tool trajectories feel natural to the user. An application scenario is proposed for brain cortex stimulation procedures, where the surgeon's intentions of motion are explicitly defined intra-operatively through an image-guided navigational system.

Learning styles and courseware design

OpenAIRE

Valley, Karen

1997-01-01

In this paper we examine how (courseware) can accommodate differences in preferred learning style. A review of the literature on learning styles is followed by a discussion of the implications of being able to accurately classify learners, and key issues that must be addressed are raised. We then present two courseware design solutions that take into account individual learning‐style preference: the first follows on from traditional research in this area and assumes that learners can be class...

Achievement of learning outcome after implemented physical modules based on problem based learning

Science.gov (United States)

Isna, R.; Masykuri, M.; Sukarmin

2018-03-01

Implementation of Problem BasedLearning (PBL) modules can grow the students' thinking skills to solve the problems in daily life and equip the students into higher education levels. The purpose of this research is to know the achievement of learning outcome after implementation physics module based on PBL in Newton,s Law of Gravity. This research method use the experimental method with posttest only group design. To know the achievement of student learning outcomes was analyzed using t test through application of SPSS 18. Based on research result, it is found that the average of student learning outcomes after appliying physics module based on PBL has reached the minimal exhaustiveness criteria. In addition, students' scientific attitudes also improved at each meeting. Presentation activities which contained at learning sync are also able to practice speaking skills and broaden their knowledge. Looking at some shortcomings during the study, it is suggested the issues raised into learning should be a problem close to the life of students so that, the students are more active and enthusiastic in following the learning of physics.

Why and how to make a REACH registration of combustion ash; Moejligheter vid REACH-registrering av energiaskor

Energy Technology Data Exchange (ETDEWEB)

Loevgren, Linnea; Wik, Ola

2009-10-15

The new chemical regulation, REACH (1997/2006/EC), Registration, Evaluation, Authorization and restriction of Chemicals, took effect the 1st of June 2007. The background to this report was the introduction of REACH and the difficulties to understand the implications for ash. The most important consequence of REACH is that all chemical substances that are manufactured, handled and used above one tonne per annum per legal entity shall be registered according to this regulation. The registration includes specifying the chemical, physical, toxicity and ecotoxicity properties of the substance and risk assessing the identified areas of use. The report describes the use of ash in connection to the waste legislation and its planned end-of-waste-criteria, the chemical legislation and the Construction Products Directive. The target audience of this report is companies producing ashes and having a use or seeing a use for its ash. The report describes how to make a REACH registration of ash independent if a company did or did not pre-register ash during 2008. It describes how to change from one ash registration into another if the pre-registration was done for one type of ash but the company changes opinion during the sameness check, i.e. changing SIEF (Appendix A). Taking part in REACH registration projects during 2009-2010 can be advantageous since knowledge and financing are shared. Ash can be REACH registered also in the future but it is important to know that the registration have to be done prior the production and marketing starts. If ash is consider to be a waste the handling is covered by the community and national waste legislation. In Sweden ashes are by and large being regarded as waste, and recycling is risk assessed and permits are given case by case. End-of-waste criteria for different waste material are being elaborated within the EU. Such criteria will among other details cover chemical safety. When a material fulfils the end-of-waste criteria such material

Near infrared spectroscopy to estimate the temperature reached on burned soils: strategies to develop robust models.

Science.gov (United States)

Guerrero, César; Pedrosa, Elisabete T.; Pérez-Bejarano, Andrea; Keizer, Jan Jacob

2014-05-01

models, since this step is the bottle-neck of this technique. In the first approach, a plot-scale model was used to predict the temperature reached in samples collected in other plots from the same site. In a plot-scale model, all the heated aliquots come from a unique plot-scale sample. As expected, the results obtained with this approach were deceptive, because this approach was assuming that a plot-scale model would be enough to represent the whole variability of the site. The accuracy (measured as the root mean square error of prediction, thereinafter RMSEP) was 86ºC, and the bias was also high (>30ºC). In the second approach, the temperatures predicted through several plot-scale models were averaged. The accuracy was improved (RMSEP=65ºC) respect the first approach, because the variability from several plots was considered and biased predictions were partially counterbalanced. However, this approach implies more efforts, since several plot-scale models are needed. In the third approach, the predictions were obtained with site-scale models. These models were constructed with aliquots from several plots. In this case, the results were accurate, since the RMSEP was around 40ºC, the bias was very small (importance of an adequate strategy to develop robust and accurate models with moderate efforts, and how a wrong strategy can result in deceptive predictions.

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

Directory of Open Access Journals (Sweden)

Yi Sun

2017-12-01

Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

Science.gov (United States)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Stream Habitat Reach Summary - NCWAP [ds158

Data.gov (United States)

California Natural Resource Agency — The Stream Habitat - NCWAP - Reach Summary [ds158] shapefile contains in-stream habitat survey data summarized to the stream reach level. It is a derivative of the...

Active learning of Pareto fronts.

Science.gov (United States)

Campigotto, Paolo; Passerini, Andrea; Battiti, Roberto

2014-03-01

This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.

Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

Science.gov (United States)

Sultan, A. Z.; Hamzah, N.; Rusdi, M.

2018-01-01

The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.

Operational Reach: Is Current Army Doctrine Adequate?

National Research Council Canada - National Science Library

Heintzelman, Scott

2003-01-01

The term operational reach, an element of operational design, is new to U.S. Army doctrine. Operational reach is not found in the previous edition of the Army's basic operational doctrine, Field Manual...

Developing a new experimental system for an undergraduate laboratory exercise to teach theories of visuomotor learning.

Science.gov (United States)

Kasuga, Shoko; Ushiba, Junichi

2014-01-01

Humans have a flexible motor ability to adapt their movements to changes in the internal/external environment. For example, using arm-reaching tasks, a number of studies experimentally showed that participants adapt to a novel visuomotor environment. These results helped develop computational models of motor learning implemented in the central nervous system. Despite the importance of such experimental paradigms for exploring the mechanisms of motor learning, because of the cost and preparation time, most students are unable to participate in such experiments. Therefore, in the current study, to help students better understand motor learning theories, we developed a simple finger-reaching experimental system using commonly used laptop PC components with an open-source programming language (Processing Motor Learning Toolkit: PMLT). We found that compared to a commercially available robotic arm-reaching device, our PMLT accomplished similar learning goals (difference in the error reduction between the devices, P = 0.10). In addition, consistent with previous reports from visuomotor learning studies, the participants showed after-effects indicating an adaptation of the motor learning system. The results suggest that PMLT can serve as a new experimental system for an undergraduate laboratory exercise of motor learning theories with minimal time and cost for instructors.

eLearning Mobile App for Android and Ios "English Grammar Learn & Test"

Directory of Open Access Journals (Sweden)

Anca-Georgiana FODOR

2016-11-01

Full Text Available This article is aiming to present the architecture and few elements from the developing cycle of "English Grammar Learn & Test" app. This is an e-learning tool for people who want to improve their English Grammar and Vocabulary. The app was approved by Google Play and Apple Store and it is available for free on both platforms as following: Android: https://play.google.com/store/apps/details?id=com.labsterzz.english_tests iOS: https://itunes.apple.com/us/app/english-grammar-learn-test/id1126468980 The app already reached350.000 users, it is rated at 4.43out of maximum 5.0 in Google Play Store. Since mid-June 2016, we launched the app also in the Apple Store iOS devices.

Learning Markov Decision Processes for Model Checking

DEFF Research Database (Denmark)

Mao, Hua; Chen, Yingke; Jaeger, Manfred

2012-01-01

. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...

Augmented Reality, the Future of Contextual Mobile Learning

Science.gov (United States)

Sungkur, Roopesh Kevin; Panchoo, Akshay; Bhoyroo, Nitisha Kirtee

2016-01-01

Purpose: This study aims to show the relevance of augmented reality (AR) in mobile learning for the 21st century. With AR, any real-world environment can be augmented by providing users with accurate digital overlays. AR is a promising technology that has the potential to encourage learners to explore learning materials from a totally new…

Behavioural and neural basis of anomalous motor learning in children with autism.

Science.gov (United States)

Marko, Mollie K; Crocetti, Deana; Hulst, Thomas; Donchin, Opher; Shadmehr, Reza; Mostofsky, Stewart H

2015-03-01

Autism spectrum disorder is a developmental disorder characterized by deficits in social and communication skills and repetitive and stereotyped interests and behaviours. Although not part of the diagnostic criteria, individuals with autism experience a host of motor impairments, potentially due to abnormalities in how they learn motor control throughout development. Here, we used behavioural techniques to quantify motor learning in autism spectrum disorder, and structural brain imaging to investigate the neural basis of that learning in the cerebellum. Twenty children with autism spectrum disorder and 20 typically developing control subjects, aged 8-12, made reaching movements while holding the handle of a robotic manipulandum. In random trials the reach was perturbed, resulting in errors that were sensed through vision and proprioception. The brain learned from these errors and altered the motor commands on the subsequent reach. We measured learning from error as a function of the sensory modality of that error, and found that children with autism spectrum disorder outperformed typically developing children when learning from errors that were sensed through proprioception, but underperformed typically developing children when learning from errors that were sensed through vision. Previous work had shown that this learning depends on the integrity of a region in the anterior cerebellum. Here we found that the anterior cerebellum, extending into lobule VI, and parts of lobule VIII were smaller than normal in children with autism spectrum disorder, with a volume that was predicted by the pattern of learning from visual and proprioceptive errors. We suggest that the abnormal patterns of motor learning in children with autism spectrum disorder, showing an increased sensitivity to proprioceptive error and a decreased sensitivity to visual error, may be associated with abnormalities in the cerebellum. © The Author (2015). Published by Oxford University Press on behalf

Fast and accurate algorithm for repeated optical trapping simulations on arbitrarily shaped particles based on boundary element method

International Nuclear Information System (INIS)

Xu, Kai-Jiang; Pan, Xiao-Min; Li, Ren-Xian; Sheng, Xin-Qing

2017-01-01

In optical trapping applications, the optical force should be investigated within a wide range of parameter space in terms of beam configuration to reach the desirable performance. A simple but reliable way of conducting the related investigation is to evaluate optical forces corresponding to all possible beam configurations. Although the optical force exerted on arbitrarily shaped particles can be well predicted by boundary element method (BEM), such investigation is time costing because it involves many repetitions of expensive computation, where the forces are calculated from the equivalent surface currents. An algorithm is proposed to alleviate the difficulty by exploiting our previously developed skeletonization framework. The proposed algorithm succeeds in reducing the number of repetitions. Since the number of skeleton beams is always much less than that of beams in question, the computation can be very efficient. The proposed algorithm is accurate because the skeletonization is accuracy controllable. - Highlights: • A fast and accurate algorithm is proposed in terms of boundary element method to reduce the number of repetitions of computing the optical forces from the equivalent currents. • The algorithm is accuracy controllable because the accuracy of the associated rank-revealing process is well-controlled. • The accelerate rate can reach over one thousand because the number of skeleton beams can be very small. • The algorithm can be applied to other methods, e.g., FE-BI.

On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis

NARCIS (Netherlands)

Melendez Rodriguez, J.C.; Ginneken, B. van; Maduskar, P.; Philipsen, R.H.H.M.; Ayles, H.; Sanchez, C.I.

2016-01-01

The major advantage of multiple-instance learning (MIL) applied to a computer-aided detection (CAD) system is that it allows optimizing the latter with case-level labels instead of accurate lesion outlines as traditionally required for a supervised approach. As shown in previous work, a MIL-based

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

Science.gov (United States)

Feng, Zhichao; Rong, Pengfei; Cao, Peng; Zhou, Qingyu; Zhu, Wenwei; Yan, Zhimin; Liu, Qianyun; Wang, Wei

2018-04-01

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications.

Science.gov (United States)

Corazza, Stefano; Gambaretto, Emiliano; Mündermann, Lars; Andriacchi, Thomas P

2010-04-01

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface. The optimal locations of joint centers in the 3-D mesh were learned through linear regression over a set of nine subjects whose joint centers were known. The model was shown to be sufficiently accurate for both kinematic (joint centers) and morphological (shape of the body) information to allow accurate tracking with MMC systems. The automatic model generation algorithm was applied to 3-D meshes of different quality and resolution such as laser scans and visual hulls. The complete method was tested using nine subjects of different gender, body mass index (BMI), age, and ethnicity. Experimental training error and cross-validation errors were 19 and 25 mm, respectively, on average over the joints of the ten subjects analyzed in the study.

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Science.gov (United States)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

Adaptive mixed reality rehabilitation improves quality of reaching movements more than traditional reaching therapy following stroke.

Science.gov (United States)

Duff, Margaret; Chen, Yinpeng; Cheng, Long; Liu, Sheng-Min; Blake, Paul; Wolf, Steven L; Rikakis, Thanassis

2013-05-01

Adaptive mixed reality rehabilitation (AMRR) is a novel integration of motion capture technology and high-level media computing that provides precise kinematic measurements and engaging multimodal feedback for self-assessment during a therapeutic task. We describe the first proof-of-concept study to compare outcomes of AMRR and traditional upper-extremity physical therapy. Two groups of participants with chronic stroke received either a month of AMRR therapy (n = 11) or matched dosing of traditional repetitive task therapy (n = 10). Participants were right handed, between 35 and 85 years old, and could independently reach to and at least partially grasp an object in front of them. Upper-extremity clinical scale scores and kinematic performances were measured before and after treatment. Both groups showed increased function after therapy, demonstrated by statistically significant improvements in Wolf Motor Function Test and upper-extremity Fugl-Meyer Assessment (FMA) scores, with the traditional therapy group improving significantly more on the FMA. However, only participants who received AMRR therapy showed a consistent improvement in kinematic measurements, both for the trained task of reaching to grasp a cone and the untrained task of reaching to push a lighted button. AMRR may be useful in improving both functionality and the kinematics of reaching. Further study is needed to determine if AMRR therapy induces long-term changes in movement quality that foster better functional recovery.

Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

Directory of Open Access Journals (Sweden)

Julian Zubek

2015-07-01

Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

Towards accurate emergency response behavior

International Nuclear Information System (INIS)

Sargent, T.O.

1981-01-01

Nuclear reactor operator emergency response behavior has persisted as a training problem through lack of information. The industry needs an accurate definition of operator behavior in adverse stress conditions, and training methods which will produce the desired behavior. Newly assembled information from fifty years of research into human behavior in both high and low stress provides a more accurate definition of appropriate operator response, and supports training methods which will produce the needed control room behavior. The research indicates that operator response in emergencies is divided into two modes, conditioned behavior and knowledge based behavior. Methods which assure accurate conditioned behavior, and provide for the recovery of knowledge based behavior, are described in detail

Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

Science.gov (United States)

Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

Outcomes of senior reach gatekeeper referrals: comparison of the Spokane gatekeeper program, Colorado Senior Reach, and Mid-Kansas Senior Outreach.

Science.gov (United States)

Bartsch, David A; Rodgers, Vicki K; Strong, Don

2013-01-01

Outcomes of older adults referred for care management and mental health services through the senior reach gatekeeper model of case finding were examined in this study and compared with the Spokane gatekeeper model Colorado Senior Reach and the Mid-Kansas Senior Outreach (MKSO) programs are the two Senior Reach Gatekeeper programs modeled after the Spokane program, employing the same community education and gatekeeper model and with mental health treatment for elderly adults in need of support. The three mature programs were compared on seniors served isolation, and depression ratings. Nontraditional community gatekeepers were trained and referred seniors in need. Findings indicate that individuals served by the two Senior Reach Gatekeeper programs demonstrated significant improvements. Isolation indicators such as social isolation decreased and depression symptoms and suicide ideation also decreased. These findings for two Senior Reach Gatekeeper programs demonstrate that the gatekeeper approach to training community partners worked in referring at-risk seniors in need in meeting their needs, and in having a positive impact on their lives.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Science.gov (United States)

Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland

2015-08-30

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

IS E-LEARNING NECESSARY FOR UNIVERSITY STUDENTS? A Case From Iran

OpenAIRE

Faranak OMIDIAN; Fatemeh KEYVANIFARD

2012-01-01

Today many claim that e-learning can result in considerable time and cost-savings , such as traveling , work time and etc . This study was conducted to investigate these questions: should e-learning be used to reduce travel related stress? should e-learning be offered fully online to reach students living in remote areas? should e-learning be adopted to allow working students to study from home ? Pressure to use e-learning was developed as a factor to answer above questions. Data was colle...

Active machine learning-driven experimentation to determine compound effects on protein patterns.

Science.gov (United States)

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Less is more: Sampling chemical space with active learning

Science.gov (United States)

Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

2018-06-01

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits.

Science.gov (United States)

Simmonds, Anna J

2015-01-01

Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred from studies on songbirds. Jarvis (2004) proposed the hypothesis that as in songbirds there are two pathways in humans: one for learning speech (the striatal vocal learning pathway), and one for production of previously learnt speech (the motor pathway). Learning new motor sequences necessary for accurate non-native pronunciation is challenging and I argue that in late learners of a foreign language the vocal learning pathway becomes inactive prematurely. The motor pathway is engaged once again and learners maintain their original native motor patterns for producing speech, resulting in speaking with a foreign accent. Further, I argue that variability in neural activity within vocal motor circuitry generates vocal variability that supports accurate non-native pronunciation. Recent theoretical and experimental work on motor learning suggests that variability in the motor movement is necessary for the development of expertise. I propose that there is little trial-by-trial variability when using the motor pathway. When using the vocal learning pathway variability gradually increases, reflecting an exploratory phase in which learners try out different ways of pronouncing words, before decreasing and stabilizing once the "best" performance has been identified. The hypothesis proposed here could be tested using behavioral interventions that optimize variability and engage the vocal learning pathway for longer, with the prediction that this would allow learners to develop new motor

Perceiver as polar planimeter: Direct perception of jumping, reaching, and jump-reaching affordances for the self and others.

Science.gov (United States)

Thomas, Brandon J; Hawkins, Matthew M; Nalepka, Patrick

2017-03-30

Runeson (Scandanavian Journal of Psychology 18:172-179, 1977) suggested that the polar planimeter might serve as an informative model system of perceptual mechanism. The key aspect of the polar planimeter is that it registers a higher order property of the environment without computational mediation on the basis of lower order properties, detecting task-specific information only. This aspect was posited as a hypothesis for the perception of jumping and reaching affordances for the self and another person. The findings supported this hypothesis. The perception of reaching while jumping significantly differed from an additive combination of jump-without-reaching and reach-without-jumping perception. The results are consistent with Gibson's (The senses considered as perceptual systems, Houghton Mifflin, Boston, MA; Gibson, The senses considered as perceptual systems, Houghton Mifflin, Boston, MA, 1966; The ecological approach to visual perception, Houghton Mifflin, Boston, MA; Gibson, The ecological approach to visual perception, Houghton Mifflin, Boston, MA, 1979) theory of information-that aspects of the environment are specified by patterns in energetic media.

Model-Agnostic Interpretability of Machine Learning

OpenAIRE

Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

2016-01-01

Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

Representation learning with deep extreme learning machines for efficient image set classification

KAUST Repository

Uzair, Muhammad

2016-12-09

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

Representation learning with deep extreme learning machines for efficient image set classification

KAUST Repository

Uzair, Muhammad; Shafait, Faisal; Ghanem, Bernard; Mian, Ajmal

2016-01-01

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum.

Science.gov (United States)

Schweighofer, N; Spoelstra, J; Arbib, M A; Kawato, M

1998-01-01

The cerebellum is essential for the control of multijoint movements; when the cerebellum is lesioned, the performance error is more than the summed errors produced by single joints. In the companion paper (Schweighofer et al., 1998), a functional anatomical model for visually guided arm movement was proposed. The model comprised a basic feedforward/feedback controller with realistic transmission delays and was connected to a two-link, six-muscle, planar arm. In the present study, we examined the role of the cerebellum in reaching movements by embedding a novel, detailed cerebellar neural network in this functional control model. We could derive realistic cerebellar inputs and the role of the cerebellum in learning to control the arm was assessed. This cerebellar network learned the part of the inverse dynamics of the arm not provided by the basic feedforward/feedback controller. Despite realistically low inferior olive firing rates and noisy mossy fibre inputs, the model could reduce the error between intended and planned movements. The responses of the different cell groups were comparable to those of biological cell groups. In particular, the modelled Purkinje cells exhibited directional tuning after learning and the parallel fibres, due to their length, provide Purkinje cells with the input required for this coordination task. The inferior olive responses contained two different components; the earlier response, locked to movement onset, was always present and the later response disappeared after learning. These results support the theory that the cerebellum is involved in motor learning.

Spectrally accurate contour dynamics

International Nuclear Information System (INIS)

Van Buskirk, R.D.; Marcus, P.S.

1994-01-01

We present an exponentially accurate boundary integral method for calculation the equilibria and dynamics of piece-wise constant distributions of potential vorticity. The method represents contours of potential vorticity as a spectral sum and solves the Biot-Savart equation for the velocity by spectrally evaluating a desingularized contour integral. We use the technique in both an initial-value code and a newton continuation method. Our methods are tested by comparing the numerical solutions with known analytic results, and it is shown that for the same amount of computational work our spectral methods are more accurate than other contour dynamics methods currently in use

RECORDS REACHING RECORDING DATA TECHNOLOGIES

Directory of Open Access Journals (Sweden)

G. W. L. Gresik

2013-07-01

Full Text Available The goal of RECORDS (Reaching Recording Data Technologies is the digital capturing of buildings and cultural heritage objects in hard-to-reach areas and the combination of data. It is achieved by using a modified crane from film industry, which is able to carry different measuring systems. The low-vibration measurement should be guaranteed by a gyroscopic controlled advice that has been , developed for the project. The data were achieved by using digital photography, UV-fluorescence photography, infrared reflectography, infrared thermography and shearography. Also a terrestrial 3D laser scanner and a light stripe topography scanner have been used The combination of the recorded data should ensure a complementary analysis of monuments and buildings.

Records Reaching Recording Data Technologies

Science.gov (United States)

Gresik, G. W. L.; Siebe, S.; Drewello, R.

2013-07-01

The goal of RECORDS (Reaching Recording Data Technologies) is the digital capturing of buildings and cultural heritage objects in hard-to-reach areas and the combination of data. It is achieved by using a modified crane from film industry, which is able to carry different measuring systems. The low-vibration measurement should be guaranteed by a gyroscopic controlled advice that has been , developed for the project. The data were achieved by using digital photography, UV-fluorescence photography, infrared reflectography, infrared thermography and shearography. Also a terrestrial 3D laser scanner and a light stripe topography scanner have been used The combination of the recorded data should ensure a complementary analysis of monuments and buildings.

Exploring the Use of Electronic Mobile Technologies among Distance Learners in Rural Communities for Safe and Disruptive Learning

Science.gov (United States)

Ntloedibe-Kuswani, Gomang Seratwa

2013-01-01

Several studies indicated the potential of electronic mobile technologies in reaching (safe learning) under-served communities and engaging (disruptive learning) disadvantaged peoples affording them learning experiences. However, the potential benefits of (electronic mobile learning) e-mobile learning have not been well understood from the…

Accurate deuterium spectroscopy for fundamental studies

Science.gov (United States)

Wcisło, P.; Thibault, F.; Zaborowski, M.; Wójtewicz, S.; Cygan, A.; Kowzan, G.; Masłowski, P.; Komasa, J.; Puchalski, M.; Pachucki, K.; Ciuryło, R.; Lisak, D.

2018-07-01

We present an accurate measurement of the weak quadrupole S(2) 2-0 line in self-perturbed D2 and theoretical ab initio calculations of both collisional line-shape effects and energy of this rovibrational transition. The spectra were collected at the 247-984 Torr pressure range with a frequency-stabilized cavity ring-down spectrometer linked to an optical frequency comb (OFC) referenced to a primary time standard. Our line-shape modeling employed quantum calculations of molecular scattering (the pressure broadening and shift and their speed dependencies were calculated, while the complex frequency of optical velocity-changing collisions was fitted to experimental spectra). The velocity-changing collisions are handled with the hard-sphere collisional kernel. The experimental and theoretical pressure broadening and shift are consistent within 5% and 27%, respectively (the discrepancy for shift is 8% when referred not to the speed averaged value, which is close to zero, but to the range of variability of the speed-dependent shift). We use our high pressure measurement to determine the energy, ν0, of the S(2) 2-0 transition. The ab initio line-shape calculations allowed us to mitigate the expected collisional systematics reaching the 410 kHz accuracy of ν0. We report theoretical determination of ν0 taking into account relativistic and QED corrections up to α5. Our estimation of the accuracy of the theoretical ν0 is 1.3 MHz. We observe 3.4σ discrepancy between experimental and theoretical ν0.

Drugs + HIV, Learn the Link

Medline Plus

Full Text Available ... once can have serious health consequences. We also work to reach parents and teachers—influential figures in the lives of young people—with the Learn the Link message. Campaign messages and materials were tested among various groups of young people, guiding the use of technology, ...

Participation in lifelong learning in Portugal and the UK

OpenAIRE

Hilary Ingham; Mike Ingham; Jose Adelino Afonso

2013-01-01

Lifelong learning (LLL) has now been on the agenda of the European Union and other major international organizations for some considerable time, with the European institutions stressing the need that such learning should be available to all, especially hard to reach groups. This paper seeks to explore LLL participation in Portugal and the UK, two countries at opposite ends of the adult learning spectrum and having very different labour market and educational contexts. Using Labour Force Surve...

Reaching Hard-to-Reach Individuals: Nonselective Versus Targeted Outbreak Response Vaccination for Measles

Science.gov (United States)

Minetti, Andrea; Hurtado, Northan; Grais, Rebecca F.; Ferrari, Matthew

2014-01-01

Current mass vaccination campaigns in measles outbreak response are nonselective with respect to the immune status of individuals. However, the heterogeneity in immunity, due to previous vaccination coverage or infection, may lead to potential bias of such campaigns toward those with previous high access to vaccination and may result in a lower-than-expected effective impact. During the 2010 measles outbreak in Malawi, only 3 of the 8 districts where vaccination occurred achieved a measureable effective campaign impact (i.e., a reduction in measles cases in the targeted age groups greater than that observed in nonvaccinated districts). Simulation models suggest that selective campaigns targeting hard-to-reach individuals are of greater benefit, particularly in highly vaccinated populations, even for low target coverage and with late implementation. However, the choice between targeted and nonselective campaigns should be context specific, achieving a reasonable balance of feasibility, cost, and expected impact. In addition, it is critical to develop operational strategies to identify and target hard-to-reach individuals. PMID:24131555

Teaching the science of learning.

Science.gov (United States)

Weinstein, Yana; Madan, Christopher R; Sumeracki, Megan A

2018-01-01

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

Technology-Mediated Learning for Resilience

DEFF Research Database (Denmark)

Sandvik, Kjetil; Majchrzak, Tim; Busch, Peter André

, such as the successive outbreak of a pandemic. Due to the novelty of the topic, research particularly exists on theoretical aspects of resilience. Targeting learning – and thereby the local population – is a rather new emergence. To effectively reach, involve, and engage citizens, technology can play a key role. Based...... on four actual cases from communities we analyse the impact technology has on learning about resilience. We then scrutinize the effectiveness and propose future steps. Thereby, we seek to provide practical advice to local governments and to enrich the theory at the same time....

Different Keystrokes for Different Folks: Addressing Learning Styles in Online Education

Science.gov (United States)

Pinchot, Jamie; Paullet, Karen

2014-01-01

Online learning has become increasingly popular in recent years. This interest in online education has brought about new learning opportunities for both educators and learners. Technology has enabled higher education institutions the ability to provide quality education reaching learners that might otherwise be impossible. When developing online…

Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

Science.gov (United States)

Lin, Jing-Wen

2016-01-01

Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

Seventh meeting of the Global Alliance to Eliminate Lymphatic Filariasis: reaching the vision by scaling up, scaling down, and reaching out

Science.gov (United States)

2014-01-01

This report summarizes the 7th meeting of the Global Alliance to Eliminate Lymphatic Filariasis (GAELF), Washington DC, November 18–19, 2012. The theme, “A Future Free of Lymphatic Filariasis: Reaching the Vision by Scaling Up, Scaling Down and Reaching Out”, emphasized new strategies and partnerships necessary to reach the 2020 goal of elimination of lymphatic filariasis (LF) as a public-health problem. PMID:24450283

Doing physical activity – not learning

DEFF Research Database (Denmark)

Jensen, Jens-Ole

2017-01-01

Introduction In recent years there have been a raising critique concerning PE as a subject which is more concerned with keeping pupils physically active than insuring that they learn something (Annerstedt, 2008). In Denmark, this issue has been actualized in a new sense. In 2014, a new school...... reform with 45 minutes of daily physical activity was introduced to enhance the pupils’ health, well-being and learning capabilities. Instead of focusing on learning bodily skills, physical activities has become an instrument to improve learning in the academic subjects. Physical activities.......g. Biesta, 2010; Standal, 2015) I will argue that the focus on learning outcome and effects on physical activity has gone too far in order to reach the objectives. If the notion of ‘keeping pupils physically active’ is understood as a representation of the core quality of physical activity, it seems...

Deep learning for multi-task plant phenotyping

OpenAIRE

Pound, Michael P.; Atkinson, Jonathan A.; Wells, Darren M.; Pridmore, Tony P.; French, Andrew P.

2017-01-01

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurate...

Distance Learning and Jihad: The Dark Side of the Force

Science.gov (United States)

Bates, Rodger; Mooney, Mara

2014-01-01

The ability to reach a variety of audiences in diverse environments has made distance learning a major form of education and training in the 21st century. Though traditionally encountered in the educational and business communities, distance learning has proven an important resource for a variety of other constituencies. Terrorist groups have…

Accurate Information, Virtual Reality, Good Librarianship Doğru Bilgi, Sanal Gerçeklik, İyi Kütüphanecilik

Directory of Open Access Journals (Sweden)

M. Tayfun Gülle

2010-03-01

Full Text Available Departing from the idea that internet, which has become a deep information tunnel, is causing a problem in access to “accurate information”, it is expressed that societies are imprisoned within the world of “virtual reality” with web 2.0/web 3.0 technologies and social media applications. In order to diagnose this problem correctly, the media used from past to present for accessing information are explained shortly as “social tools.” Furthermore, it is emphasised and summarised with an editorial viewpoint that the means of reaching accurate information can be increased via the freedom of expression channel which will be brought forth by “good librarianship” applications. IFLA Principles of Freedom of Expression and Good Librarianship is referred to at the end of the editorial.

REACH: next step to a sound chemicals management.

Science.gov (United States)

Van der Wielen, Arnold

2007-12-01

REACH is the new European Regulation for Registration, Evaluation, Authorisation and Restriction of Chemicals. It entered into force on 1st June 2007 to streamline and improve the former legislative framework on new and on existing chemical substances of the European Union. Companies which manufacture or import more than 1 tonne of a substance per year will be required to register the substance at the new EU Chemicals Agency located in Helsinki. REACH places greater responsibility on industry to manage the risks that chemicals may pose to the health and the environment and to provide safety information that will be passed down the supply chain. In principle, REACH applies to all chemicals as such, as components in preparations and as used in articles. REACH is a radical step forward in the EU chemicals management. The onus will move from the authorities to industry. In addition, REACH will allow the further evaluation of substances where there are grounds for concern, foresees an authorisation system for the use of substances of very high concern and a system of restrictions, where applicable, for substances of concern. The Authorisation system will require companies to switch progressively to safer alternatives where a suitable alternative exists. Current use restrictions will remain under REACH system.

EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

Directory of Open Access Journals (Sweden)

Abdel Rahman IBRAHIM SULEIMAN

2014-07-01

Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

Peer Support for the Hardly Reached: A Systematic Review.

Science.gov (United States)

Sokol, Rebeccah; Fisher, Edwin

2016-07-01

Health disparities are aggravated when prevention and care initiatives fail to reach those they are intended to help. Groups can be classified as hardly reached according to a variety of circumstances that fall into 3 domains: individual (e.g., psychological factors), demographic (e.g., socioeconomic status), and cultural-environmental (e.g., social network). Several reports have indicated that peer support is an effective means of reaching hardly reached individuals. However, no review has explored peer support effectiveness in relation to the circumstances associated with being hardly reached or across diverse health problems. To conduct a systematic review assessing the reach and effectiveness of peer support among hardly reached individuals, as well as peer support strategies used. Three systematic searches conducted in PubMed identified studies that evaluated peer support programs among hardly reached individuals. In aggregate, the searches covered articles published from 2000 to 2015. Eligible interventions provided ongoing support for complex health behaviors, including prioritization of hardly reached populations, assistance in applying behavior change plans, and social-emotional support directed toward disease management or quality of life. Studies were excluded if they addressed temporally isolated behaviors, were limited to protocol group classes, included peer support as the dependent variable, did not include statistical tests of significance, or incorporated comparison conditions that provided appreciable social support. We abstracted data regarding the primary health topic, categorizations of hardly reached groups, program reach, outcomes, and strategies employed. We conducted a 2-sample t test to determine whether reported strategies were related to reach. Forty-seven studies met our inclusion criteria, and these studies represented each of the 3 domains of circumstances assessed (individual, demographic, and cultural-environmental). Interventions

Learning modalities in artificial intelligence systems: a framework and review

Energy Technology Data Exchange (ETDEWEB)

Araya, A A

1982-01-01

Intelligent systems should possess two fundamental capabilities: problem solving and learning. Problem solving capabilities allow an intelligent system to cope with problems in a given domain. Learning capabilities make possible for an intelligent system to improve the solution to the problems within its current reach or to cope with new problems. This paper examines research in artificial intelligence from the perspective of learning with the purpose of: 1) developing and understanding of the problem of learning from the AI point of view, and II) characterizing the current state of the art on learning in AI. 35 references.

Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses.

Science.gov (United States)

Kazerooni, Anahita Fathi; Malek, Mahrooz; Haghighatkhah, Hamidreza; Parviz, Sara; Nabil, Mahnaz; Torbati, Leila; Assili, Sanam; Saligheh Rad, Hamidreza; Gity, Masoumeh

2017-02-01

To identify the best dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) descriptive parameters in predicting malignancy of complex ovarian masses, and develop an optimal decision tree for accurate classification of benign and malignant complex ovarian masses. Preoperative DCE-MR images of 55 sonographically indeterminate ovarian masses (27 benign and 28 malignant) were analyzed prospectively. Four descriptive parameters of the dynamic curve, namely, time-to-peak (TTP), wash-in-rate (WIR), relative signal intensity (SI rel ), and the initial area under the curve (IAUC 60 ) were calculated on the normalized curves of specified regions-of-interest (ROIs). A two-tailed Student's t-test and two automated classifiers, linear discriminant analysis (LDA) and support vector machines (SVMs), were used to compare the performance of the mentioned parameters individually and in combination with each other. TTP (P = 6.15E-8) and WIR (P = 5.65E-5) parameters induced the highest sensitivity (89% for LDA, and 97% for SVM) and specificity (93% for LDA, and 100% for SVM), respectively. Regarding the high sensitivity of TTP and high specificity of WIR and through their combination, an accurate and simple decision-tree classifier was designed using the line equation obtained by LDA classification model. The proposed classifier achieved an accuracy of 89% and area under the ROC curve of 93%. In this study an accurate decision-tree classifier based on a combination of TTP and WIR parameters was proposed, which provides a clinically flexible framework to aid radiologists/clinicians to reach a conclusive preoperative diagnosis and patient-specific therapy plan for distinguishing malignant from benign complex ovarian masses. 2 J. Magn. Reson. Imaging 2017;45:418-427. © 2016 International Society for Magnetic Resonance in Medicine.

Reaching ignition in the tokamak

International Nuclear Information System (INIS)

Furth, H.P.

1985-06-01

This review covers the following areas: (1) the physics of burning plasmas, (2) plasma physics requirements for reaching ignition, (3) design studies for ignition devices, and (4) prospects for an ignition project

Reaching those most in need: a review of diabetes self-management interventions in disadvantaged populations.

Science.gov (United States)

Eakin, Elizabeth G; Bull, Sheana S; Glasgow, Russell E; Mason, Mondi

2002-01-01

There has been increased recognition of the importance of developing diabetes self-management education (DSME) interventions that are effective with under-served and minority populations. Despite several recent studies in this area, there is to our knowledge no systematic review or synthesis of what has been learned from this research. An electronic literature search identified five formative evaluations and ten controlled DSME intervention trials focused on under-served (low-income, minority or aged) populations. The RE-AIM (Reach, Efficacy, Adoption, Implementation, Maintenance) evaluation framework was used to evaluate the controlled studies on the dimensions of reach, efficacy, adoption, implementation, and maintenance. Fifty percent of the studies identified reported on the percentage of patients who participated, and the percentages were highly variable. The methodological quality of the articles was generally good and the short-term results were encouraging, especially on behavioral outcomes. Data on adoption (representativeness of settings and clinicians who participate) and implementation were almost never reported. Studies of modalities in addition to group meetings are needed to increase the reach of DSME with under-served populations. The promising formative evaluation work that has been conducted needs to be extended for more systematic study of the process of intervention implementation and adaptation with special populations. Studies that explicitly address the community context and that address multiple issues related to public health impact of DSME interventions are recommended to enhance long-term results. Copyright 2002 John Wiley & Sons, Ltd.

Target size matters: target errors contribute to the generalization of implicit visuomotor learning.

Science.gov (United States)

Reichenthal, Maayan; Avraham, Guy; Karniel, Amir; Shmuelof, Lior

2016-08-01

The process of sensorimotor adaptation is considered to be driven by errors. While sensory prediction errors, defined as the difference between the planned and the actual movement of the cursor, drive implicit learning processes, target errors (e.g., the distance of the cursor from the target) are thought to drive explicit learning mechanisms. This distinction was mainly studied in the context of arm reaching tasks where the position and the size of the target were constant. We hypothesize that in a dynamic reaching environment, where subjects have to hit moving targets and the targets' dynamic characteristics affect task success, implicit processes will benefit from target errors as well. We examine the effect of target errors on learning of an unnoticed perturbation during unconstrained reaching movements. Subjects played a Pong game, in which they had to hit a moving ball by moving a paddle controlled by their hand. During the game, the movement of the paddle was gradually rotated with respect to the hand, reaching a final rotation of 25°. Subjects were assigned to one of two groups: The high-target error group played the Pong with a small ball, and the low-target error group played with a big ball. Before and after the Pong game, subjects performed open-loop reaching movements toward static targets with no visual feedback. While both groups adapted to the rotation, the postrotation reaching movements were directionally biased only in the small-ball group. This result provides evidence that implicit adaptation is sensitive to target errors. Copyright © 2016 the American Physiological Society.

Do working environment interventions reach shift workers?

Science.gov (United States)

Nabe-Nielsen, Kirsten; Jørgensen, Marie Birk; Garde, Anne Helene; Clausen, Thomas

2016-01-01

Shift workers are exposed to more physical and psychosocial stressors in the working environment as compared to day workers. Despite the need for targeted prevention, it is likely that workplace interventions less frequently reach shift workers. The aim was therefore to investigate whether the reach of workplace interventions varied between shift workers and day workers and whether such differences could be explained by the quality of leadership exhibited at different times of the day. We used questionnaire data from 5361 female care workers in the Danish eldercare sector. The questions concerned usual working hours, quality of leadership, and self-reported implementation of workplace activities aimed at stress reduction, reorganization of the working hours, and participation in improvements of working procedures or qualifications. Compared with day workers, shift workers were less likely to be reached by workplace interventions. For example, night workers less frequently reported that they had got more flexibility (OR 0.5; 95 % CI 0.3-0.7) or that they had participated in improvements of the working procedures (OR 0.6; 95 % CI 0.5-0.8). Quality of leadership to some extent explained the lack of reach of interventions especially among fixed evening workers. In the light of the evidence of shift workers' stressful working conditions, we suggest that future studies focus on the generalizability of results of the present study and on how to reach this group and meet their needs when designing and implementing workplace interventions.

Error amplification to promote motor learning and motivation in therapy robotics.

Science.gov (United States)

Shirzad, Navid; Van der Loos, H F Machiel

2012-01-01

To study the effects of different feedback error amplification methods on a subject's upper-limb motor learning and affect during a point-to-point reaching exercise, we developed a real-time controller for a robotic manipulandum. The reaching environment was visually distorted by implementing a thirty degrees rotation between the coordinate systems of the robot's end-effector and the visual display. Feedback error amplification was provided to subjects as they trained to learn reaching within the visually rotated environment. Error amplification was provided either visually or through both haptic and visual means, each method with two different amplification gains. Subjects' performance (i.e., trajectory error) and self-reports to a questionnaire were used to study the speed and amount of adaptation promoted by each error amplification method and subjects' emotional changes. We found that providing haptic and visual feedback promotes faster adaptation to the distortion and increases subjects' satisfaction with the task, leading to a higher level of attentiveness during the exercise. This finding can be used to design a novel exercise regimen, where alternating between error amplification methods is used to both increase a subject's motor learning and maintain a minimum level of motivational engagement in the exercise. In future experiments, we will test whether such exercise methods will lead to a faster learning time and greater motivation to pursue a therapy exercise regimen.

Intelligent e-Learning Systems: An Educational Paradigm Shift

Directory of Open Access Journals (Sweden)

Suman Bhattacharya

2016-12-01

Full Text Available Learning is the long process of transforming information as well as experience into knowledge, skills, attitude and behaviors. To make up the wide gap between the demand of increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. Use of online resources not only reduces the cost of education but also meet the needs of society. Intelligent e-learning has become one of the important channels to reach out to students exceeding geographic boundaries. Besides this, the characteristics of e-learning have complicated the process of education, and have brought challenges to both instructors and students. This paper will focus on the discussion of different discipline of intelligent e-learning like scaffolding based e-learning, personalized e-learning, confidence based e-learning, intelligent tutoring system, etc. to illuminate the educational paradigm shift in intelligent e-learning system.

PROJECT BASED LEARNING BERMUATAN ETNOMATEMATIKA DALAM PEMBELAJAR MATEMATIKA

Directory of Open Access Journals (Sweden)

I Wayan Eka Mahendra

2017-03-01

Full Text Available This study aims to determine differences simultaneously in motivation and mathematics learning outcomes between students taking project based learningmodel charged ethnomathematics and students who followed the conventional learning modelon the class VIII SMP Negeri 3 Abiansemalyear 2016/2017. It was a quasi experiment with a sample of 71 student obtain by using simple random sampling. The data were analyzed by one-way multivariate analysis (Manova.The results of this study indicate that there are differences in simultaneously in learning motivation and learning outcomes between students taking mathematics model project based learning charged ethnomathematics and students who followed the conventional learning model on the class VIII SMP Negeri 3 Abiansemal year 2016/2017. Besed on the research findings, junior high school teachers are suggested to improve their student learning outcome for mathematics. Teachers also need to use a learning models accurately and correctly.

Guaranteed performance in reaching mode of sliding mode ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

addresses the design of constant plus proportional rate reaching law-based SMC for second-order ... Reaching mode; sliding mode controlled systems; output tracking ... The uncertainty in the input distribution function g is expressed as.

Accurate Evaluation of Quantum Integrals

Science.gov (United States)

Galant, D. C.; Goorvitch, D.; Witteborn, Fred C. (Technical Monitor)

1995-01-01

Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schrodinger's equation. Important results are that error estimates are provided, and that one can extrapolate expectation values rather than the wavefunctions to obtain highly accurate expectation values. We discuss the eigenvalues, the error growth in repeated Richardson's extrapolation, and show that the expectation values calculated on a crude mesh can be extrapolated to obtain expectation values of high accuracy.

Outcome Mapping Virtual Learning Community - Phase II | IDRC ...

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

The first phase of the project (103520) focused on developing the Outcome ... as distance learning) and strategically communicating Outcome Mapping to key ... an organization based in India with South Asian reach, to facilitate exchange ...

E-learning: Web-based education.

Science.gov (United States)

Sajeva, Marco

2006-12-01

This review introduces state-of-the-art Web-based education and shows how the e-learning model can be applied to an anaesthesia department using Open Source solutions, as well as lifelong learning programs, which is happening in several European research projects. The definition of the term e-learning is still a work in progress due to the fact that technologies are evolving every day and it is difficult to improve teaching methodologies or to adapt traditional methods to a new or already existing educational model. The European Community is funding several research projects to define the new common market place for tomorrow's educational system; this is leading to new frontiers like virtual Erasmus inter-exchange programs based on e-learning. The first step when adapting a course to e-learning is to re-define the educational/learning model adopted: cooperative learning and tutoring are the two key concepts. This means that traditional lecture notes, books and exercises are no longer effective; teaching files must use rich multimedia content and have to be developed using the new media. This can lead to several pitfalls that can be avoided with an accurate design phase.

Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants

International Nuclear Information System (INIS)

Parlos, A.G.; Atiya, Amir; Chong, K.T.

1991-01-01

A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process

Using New Media to Reach Broad Audiences

Science.gov (United States)

Gay, P. L.

2008-06-01

The International Year of Astronomy New Media Working Group (IYA NMWG) has a singular mission: To flood the Internet with ways to learn about astronomy, interact with astronomers and astronomy content, and socially network with astronomy. Within each of these areas, we seek to build lasting programs and partnerships that will continue beyond 2009. Our weapon of choice is New Media. It is often easiest to define New Media by what it is not. Television, radio, print and their online redistribution of content are not New Media. Many forms of New Media start as user provided content and content infrastructures that answer that individual's creative whim in a way that is adopted by a broader audience. Classic examples include Blogs and Podcasts. This media is typically distributed through content specific websites and RSS feeds, which allow syndication. RSS aggregators (iTunes has audio and video aggregation abilities) allow subscribers to have content delivered to their computers automatically when they connect to the Internet. RSS technology is also being used in such creative ways as allowing automatically updating Google-maps that show the location of someone with an intelligent GPS system, and in sharing 100 word microblogs from anyone (Twitters) through a single feed. In this poster, we outline how the IYA NMWG plans to use New Media to reach target primary audiences of astronomy enthusiasts, image lovers, and amateur astronomers, as well as secondary audiences, including: science fiction fans, online gamers, and skeptics.

Evolving autonomous learning in cognitive networks.

Science.gov (United States)

Sheneman, Leigh; Hintze, Arend

2017-12-01

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

Online Learning in a South African Higher Education Institution: Determining the Right Connections for the Student

Science.gov (United States)

Queiros, Dorothy R.; de Villiers, M. R.

2016-01-01

Online learning is a means of reaching marginalised and disadvantaged students within South Africa. Nevertheless, these students encounter obstacles in online learning. This research investigates South African students' opinions regarding online learning, culminating in a model of important connections (facets that connect students to their…

Action plans can interact to hinder or facilitate reach performance.

Science.gov (United States)

Fournier, Lisa R; Wiediger, Matthew D; Taddese, Ezana F

2015-11-01

Executing a reach action can be delayed while retaining another action in working memory (WM) if the two action plans partly overlap rather than do not overlap. This delay (partial repetition cost) occurs when reach responses are under cognitive control. In this study, we investigated whether facilitation (a partial repetition benefit) occurs when reach responses are automatic. We also examined whether the hemisphere controlling the limb or selection of the preferred limb (based on a free-reach task) influences reach performance when the actions partly overlap. Left- and right-handers reached to different stimulus locations to the left and right of body midline with their ipsilateral hand while maintaining an action plan in WM that required the same or the different hand. The results showed a partial repetition benefit for spatially compatible reaches to left and right stimulus locations far from the body midline, but not for those near the body midline. Also, no partial repetition cost was found at any of the stimulus-reach locations. This indicates that automatic reach responses that partly overlap with an action plan maintained in WM are not delayed, but instead can be facilitated (partial repetition benefit). The roles of hemisphere and reach-hand preference in action control and the importance of the degree of feature overlap in obtaining a partial repetition benefit (and cost) are discussed.

Reaching national consensus on the core clinical skill outcomes for family medicine postgraduate training programmes in South Africa.

Science.gov (United States)

Akoojee, Yusuf; Mash, Robert

2017-05-26

Family physicians play a significant role in the district health system and need to be equipped with a broad range of clinical skills in order to meet the needs and expectations of the communities they serve. A previous study in 2007 reached national consensus on the clinical skills that should be taught in postgraduate family medicine training prior to the introduction of the new speciality. Since then, family physicians have been trained, employed and have gained experience of working in the district health services. The national Education and Training Committee of the South African Academy of Family Physicians, therefore, requested a review of the national consensus on clinical skills for family medicine training. A Delphi technique was used to reach national consensus in a panel of 17 experts: family physicians responsible for training, experienced family physicians in practice and managers responsible for employing family physicians. Consensus was reached on 242 skills from which the panel decided on 211 core skills, 28 elective skills and 3 skills to be deleted from the previous list. The panel was unable to reach consensus on 11 skills. The findings will guide training programmes on the skills to be addressed and ensure consistency across training programmes nationally. The consensus will also guide formative assessment as documented in the national portfolio of learning and summative assessment in the national exit examination. The consensus will be of interest to other countries in the region where training programmes in family medicine are developing.

LEARNING ORGANISATION CHALLENGE FOR ROMANIAN PHARMACEUTICAL SMEs

Directory of Open Access Journals (Sweden)

Otilia-Maria BORDEIANU

2014-04-01

Full Text Available The concept of the learning organization has gone through many changes both theoretically and also as practical implementation. Learning organizations do not appear automatically, they require a strong commitment for developing the skills needed in the workplace, and this commitment should start from the top of the organization. The learning process should be managed at different levels within the organization. Learning, therefore, is made up of several different components and requires a special management. Successful companies are the result of carefully cultivated attitudes, commitments and management processes. This paper investigates the learning organization dimensions analysed in case of pharmaceutical SMEs from Romania. The results obtained in this study allow us to draw relevant conclusions, constituting a practical starting point for businesses. The paper highlights the fact that SMEs pharmaceutical companies have taken important steps toward learning organization model, but reaching different levels from one key dimension to another.

What Intellectual Property lawyers can Learn from Barbra Streisand ...

African Journals Online (AJOL)

... lawyers can Learn from Barbra Streisand, Sepp Blatter, and the "Coca-Cola ... rights; Overly-aggressive enforcement; Rights holder over-reach; Litigation strategy; ... 'Trademark extortion'; 'Duty to police'; Streisand effect; Ambush marketing.

Task Demands in OSCEs Influence Learning Strategies.

Science.gov (United States)

Lafleur, Alexandre; Laflamme, Jonathan; Leppink, Jimmie; Côté, Luc

2017-01-01

Models on pre-assessment learning effects confirmed that task demands stand out among the factors assessors can modify in an assessment to influence learning. However, little is known about which tasks in objective structured clinical examinations (OSCEs) improve students' cognitive and metacognitive processes. Research is needed to support OSCE designs that benefit students' metacognitive strategies when they are studying, reinforcing a hypothesis-driven approach. With that intent, hypothesis-driven physical examination (HDPE) assessments ask students to elicit and interpret findings of the physical exam to reach a diagnosis ("Examine this patient with a painful shoulder to reach a diagnosis"). When studying for HDPE, students will dedicate more time to hypothesis-driven discussions and practice than when studying for a part-task OSCE ("Perform the shoulder exam"). It is expected that the whole-task nature of HDPE will lead to a hypothesis-oriented use of the learning resources, a frequent use of adjustment strategies, and persistence with learning. In a mixed-methods study, 40 medical students were randomly paired and filmed while studying together for two hypothetical OSCE stations. Each 25-min study period began with video cues asking to study for either a part-task OSCE or an HDPE. In a crossover design, sequences were randomized for OSCEs and contents (shoulder or spine). Time-on-task for discussions or practice were categorized as "hypothesis-driven" or "sequence of signs and maneuvers." Content analysis of focus group interviews summarized students' perception of learning resources, adjustment strategies, and persistence with learning. When studying for HDPE, students allocate significantly more time for hypothesis-driven discussions and practice. Students use resources contrasting diagnoses and report persistence with learning. When studying for part-task OSCEs, time-on-task is reversed, spent on rehearsing a sequence of signs and maneuvers. OSCEs with

Task-dependent vestibular feedback responses in reaching.

Science.gov (United States)

Keyser, Johannes; Medendorp, W Pieter; Selen, Luc P J

2017-07-01

When reaching for an earth-fixed object during self-rotation, the motor system should appropriately integrate vestibular signals and sensory predictions to compensate for the intervening motion and its induced inertial forces. While it is well established that this integration occurs rapidly, it is unknown whether vestibular feedback is specifically processed dependent on the behavioral goal. Here, we studied whether vestibular signals evoke fixed responses with the aim to preserve the hand trajectory in space or are processed more flexibly, correcting trajectories only in task-relevant spatial dimensions. We used galvanic vestibular stimulation to perturb reaching movements toward a narrow or a wide target. Results show that the same vestibular stimulation led to smaller trajectory corrections to the wide than the narrow target. We interpret this reduced compensation as a task-dependent modulation of vestibular feedback responses, tuned to minimally intervene with the task-irrelevant dimension of the reach. These task-dependent vestibular feedback corrections are in accordance with a central prediction of optimal feedback control theory and mirror the sophistication seen in feedback responses to mechanical and visual perturbations of the upper limb. NEW & NOTEWORTHY Correcting limb movements for external perturbations is a hallmark of flexible sensorimotor behavior. While visual and mechanical perturbations are corrected in a task-dependent manner, it is unclear whether a vestibular perturbation, naturally arising when the body moves, is selectively processed in reach control. We show, using galvanic vestibular stimulation, that reach corrections to vestibular perturbations are task dependent, consistent with a prediction of optimal feedback control theory. Copyright © 2017 the American Physiological Society.

Provision of Distance Learning Services over Interactive Digital TV with MHP

Science.gov (United States)

Pazos-Arias, Jose J.; Lopez-Nores, Martin; Garcia-Duque, Jorge; Diaz-Redondo, Rebeca P.; Blanco-Fernandez, Yolanda; Ramos-Cabrer, Manuel; Gil-Solla, Alberto; Fernandez-Vilas, Ana

2008-01-01

E-learning technologies have developed greatly in recent years, with considerable success. However, there is increasing evidence that web-based learning is not reaching the social sectors which are more reluctant to contact with the new technologies, thus leading to inequalities in the access to education and knowledge in the Information Society.…

Hydroxyurea Therapy for Children With Sickle Cell Anemia in Sub-Saharan Africa: Rationale and Design of the REACH Trial.

Science.gov (United States)

McGann, Patrick T; Tshilolo, Léon; Santos, Brigida; Tomlinson, George A; Stuber, Susan; Latham, Teresa; Aygun, Banu; Obaro, Stephen K; Olupot-Olupot, Peter; Williams, Thomas N; Odame, Isaac; Ware, Russell E

2016-01-01

Sickle cell anemia (SCA) is an inherited hematological disorder that causes a large but neglected global health burden, particularly in Africa. Hydroxyurea represents the only available disease-modifying therapy for SCA, and has proven safety and efficacy in high-resource countries. In sub-Saharan Africa, there is minimal use of hydroxyurea, due to lack of data, absence of evidence-based guidelines, and inexperience among healthcare providers. A partnership was established between investigators in North America and sub-Saharan Africa, to develop a prospective multicenter research protocol designed to provide data on the safety, feasibility, and benefits of hydroxyurea for children with SCA. The Realizing Effectiveness Across Continents with Hydroxyurea (REACH, ClinicalTrials.gov NCT01966731) trial is a prospective, phase I/II open-label dose escalation study of hydroxyurea that will treat a total of 600 children age 1-10 years with SCA: 150 at each of four different clinical sites within sub-Saharan Africa (Angola, Democratic Republic of Congo, Kenya, and Uganda). The primary study endpoint will be severe hematological toxicities that occur during the fixed-dose treatment phase. REACH has an adaptive statistical design that allows for careful assessment of toxicities to accurately identify a safe hydroxyurea dose. REACH will provide data that address critical gaps in knowledge for the treatment of SCA in sub-Saharan Africa. By developing local expertise with the use of hydroxyurea and helping to establish treatment guidelines, the REACH trial results will have the potential to transform care for children with SCA in Africa. © 2015 The Authors. Pediatric Blood & Cancer Published by Wiley Periodicals, Inc.

Approaching system equilibrium with accurate or not accurate feedback information in a two-route system

Science.gov (United States)

Zhao, Xiao-mei; Xie, Dong-fan; Li, Qi

2015-02-01

With the development of intelligent transport system, advanced information feedback strategies have been developed to reduce traffic congestion and enhance the capacity. However, previous strategies provide accurate information to travelers and our simulation results show that accurate information brings negative effects, especially in delay case. Because travelers prefer to the best condition route with accurate information, and delayed information cannot reflect current traffic condition but past. Then travelers make wrong routing decisions, causing the decrease of the capacity and the increase of oscillations and the system deviating from the equilibrium. To avoid the negative effect, bounded rationality is taken into account by introducing a boundedly rational threshold BR. When difference between two routes is less than the BR, routes have equal probability to be chosen. The bounded rationality is helpful to improve the efficiency in terms of capacity, oscillation and the gap deviating from the system equilibrium.

Guiding Warfare to Reach Sustainable Peace

DEFF Research Database (Denmark)

Vestenskov, David; Drewes, Line

The conference report Guiding Warfare to Reach Sustainable Peace constitutes the primary outcome of the conference It is based on excerpts from the conference presenters and workshop discussions. Furthermore, the report contains policy recommendations and key findings, with the ambition of develo......The conference report Guiding Warfare to Reach Sustainable Peace constitutes the primary outcome of the conference It is based on excerpts from the conference presenters and workshop discussions. Furthermore, the report contains policy recommendations and key findings, with the ambition...... of developing best practices in the education and implementation of IHL in capacity building of security forces....

Accurate and efficient calculation of response times for groundwater flow

Science.gov (United States)

Carr, Elliot J.; Simpson, Matthew J.

2018-03-01

We study measures of the amount of time required for transient flow in heterogeneous porous media to effectively reach steady state, also known as the response time. Here, we develop a new approach that extends the concept of mean action time. Previous applications of the theory of mean action time to estimate the response time use the first two central moments of the probability density function associated with the transition from the initial condition, at t = 0, to the steady state condition that arises in the long time limit, as t → ∞ . This previous approach leads to a computationally convenient estimation of the response time, but the accuracy can be poor. Here, we outline a powerful extension using the first k raw moments, showing how to produce an extremely accurate estimate by making use of asymptotic properties of the cumulative distribution function. Results are validated using an existing laboratory-scale data set describing flow in a homogeneous porous medium. In addition, we demonstrate how the results also apply to flow in heterogeneous porous media. Overall, the new method is: (i) extremely accurate; and (ii) computationally inexpensive. In fact, the computational cost of the new method is orders of magnitude less than the computational effort required to study the response time by solving the transient flow equation. Furthermore, the approach provides a rigorous mathematical connection with the heuristic argument that the response time for flow in a homogeneous porous medium is proportional to L2 / D , where L is a relevant length scale, and D is the aquifer diffusivity. Here, we extend such heuristic arguments by providing a clear mathematical definition of the proportionality constant.

Parallelization of learning problems by artificial neural networks. Application in external radiotherapy

International Nuclear Information System (INIS)

Sauget, M.

2007-12-01

This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

Auditory Perceptual Learning for Speech Perception Can be Enhanced by Audiovisual Training.

Science.gov (United States)

Bernstein, Lynne E; Auer, Edward T; Eberhardt, Silvio P; Jiang, Jintao

2013-01-01

Speech perception under audiovisual (AV) conditions is well known to confer benefits to perception such as increased speed and accuracy. Here, we investigated how AV training might benefit or impede auditory perceptual learning of speech degraded by vocoding. In Experiments 1 and 3, participants learned paired associations between vocoded spoken nonsense words and nonsense pictures. In Experiment 1, paired-associates (PA) AV training of one group of participants was compared with audio-only (AO) training of another group. When tested under AO conditions, the AV-trained group was significantly more accurate than the AO-trained group. In addition, pre- and post-training AO forced-choice consonant identification with untrained nonsense words showed that AV-trained participants had learned significantly more than AO participants. The pattern of results pointed to their having learned at the level of the auditory phonetic features of the vocoded stimuli. Experiment 2, a no-training control with testing and re-testing on the AO consonant identification, showed that the controls were as accurate as the AO-trained participants in Experiment 1 but less accurate than the AV-trained participants. In Experiment 3, PA training alternated AV and AO conditions on a list-by-list basis within participants, and training was to criterion (92% correct). PA training with AO stimuli was reliably more effective than training with AV stimuli. We explain these discrepant results in terms of the so-called "reverse hierarchy theory" of perceptual learning and in terms of the diverse multisensory and unisensory processing resources available to speech perception. We propose that early AV speech integration can potentially impede auditory perceptual learning; but visual top-down access to relevant auditory features can promote auditory perceptual learning.

Learning feedback and feedforward control in a mirror-reversed visual environment.

Science.gov (United States)

Kasuga, Shoko; Telgen, Sebastian; Ushiba, Junichi; Nozaki, Daichi; Diedrichsen, Jörn

2015-10-01

When we learn a novel task, the motor system needs to acquire both feedforward and feedback control. Currently, little is known about how the learning of these two mechanisms relate to each other. In the present study, we tested whether feedforward and feedback control need to be learned separately, or whether they are learned as common mechanism when a new control policy is acquired. Participants were trained to reach to two lateral and one central target in an environment with mirror (left-right)-reversed visual feedback. One group was allowed to make online movement corrections, whereas the other group only received visual information after the end of the movement. Learning of feedforward control was assessed by measuring the accuracy of the initial movement direction to lateral targets. Feedback control was measured in the responses to sudden visual perturbations of the cursor when reaching to the central target. Although feedforward control improved in both groups, it was significantly better when online corrections were not allowed. In contrast, feedback control only adaptively changed in participants who received online feedback and remained unchanged in the group without online corrections. Our findings suggest that when a new control policy is acquired, feedforward and feedback control are learned separately, and that there may be a trade-off in learning between feedback and feedforward controllers. Copyright © 2015 the American Physiological Society.

Diagnosing Coronary Heart Disease using Ensemble Machine Learning

OpenAIRE

Kathleen H. Miao; Julia H. Miao; George J. Miao

2016-01-01

Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of long-term survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. Th...

Nanomaterials under REACH. Nanosilver as a case study

NARCIS (Netherlands)

Pronk MEJ; Wijnhoven SWP; Bleeker EAJ; Heugens EHW; Peijnenburg WJGM; Luttik R; Hakkert BC; SEC; SIR; LER

2009-01-01

Om de risico's van nanomaterialen te kunnen inschatten en beheersen, zijn enkele aanpassingen nodig in de Europese chemicalienwetgeving REACH. De gegevens over stoffen waar REACH standaard om vraagt, zijn namelijk onvoldoende om de specifieke eigenschappen van nanomaterialen te bepalen. Hetzelfde

Acoustic noise improves motor learning in spontaneously hypertensive rats, a rat model of attention deficit hyperactivity disorder.

Science.gov (United States)

Söderlund, Göran B W; Eckernäs, Daniel; Holmblad, Olof; Bergquist, Filip

2015-03-01

The spontaneously hypertensive (SH) rat model of ADHD displays impaired motor learning. We used this characteristic to study if the recently described acoustic noise benefit in learning in children with ADHD is also observed in the SH rat model. SH rats and a Wistar control strain were trained in skilled reach and rotarod running under either ambient noise or in 75 dBA white noise. In other animals the effect of methylphenidate (MPH) on motor learning was assessed with the same paradigms. To determine if acoustic noise influenced spontaneous motor activity, the effect of acoustic noise was also determined in the open field activity paradigm. We confirm impaired motor learning in the SH rat compared to Wistar SCA controls. Acoustic noise restored motor learning in SH rats learning the Montoya reach test and the rotarod test, but had no influence on learning in Wistar rats. Noise had no effect on open field activity in SH rats, but increased corner time in Wistar. MPH completely restored rotarod learning and performance but did not improve skilled reach in the SH rat. It is suggested that the acoustic noise benefit previously reported in children with ADHD is shared by the SH rat model of ADHD, and the effect is in the same range as that of stimulant treatment. Acoustic noise may be useful as a non-pharmacological alternative to stimulant medication in the treatment of ADHD. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Exploratory Movement Generates Higher-Order Information That Is Sufficient for Accurate Perception of Scaled Egocentric Distance

Science.gov (United States)

Mantel, Bruno; Stoffregen, Thomas A.; Campbell, Alain; Bardy, Benoît G.

2015-01-01

Body movement influences the structure of multiple forms of ambient energy, including optics and gravito-inertial force. Some researchers have argued that egocentric distance is derived from inferential integration of visual and non-visual stimulation. We suggest that accurate information about egocentric distance exists in perceptual stimulation as higher-order patterns that extend across optics and inertia. We formalize a pattern that specifies the egocentric distance of a stationary object across higher-order relations between optics and inertia. This higher-order parameter is created by self-generated movement of the perceiver in inertial space relative to the illuminated environment. For this reason, we placed minimal restrictions on the exploratory movements of our participants. We asked whether humans can detect and use the information available in this higher-order pattern. Participants judged whether a virtual object was within reach. We manipulated relations between body movement and the ambient structure of optics and inertia. Judgments were precise and accurate when the higher-order optical-inertial parameter was available. When only optic flow was available, judgments were poor. Our results reveal that participants perceived egocentric distance from the higher-order, optical-inertial consequences of their own exploratory activity. Analysis of participants’ movement trajectories revealed that self-selected movements were complex, and tended to optimize availability of the optical-inertial pattern that specifies egocentric distance. We argue that accurate information about egocentric distance exists in higher-order patterns of ambient energy, that self-generated movement can generate these higher-order patterns, and that these patterns can be detected and used to support perception of egocentric distance that is precise and accurate. PMID:25856410

Training with Differential Outcomes Enhances Discriminative Learning and Visuospatial Recognition Memory in Children Born Prematurely

Science.gov (United States)

Martinez, Lourdes; Mari-Beffa, Paloma; Roldan-Tapia, Dolores; Ramos-Lizana, Julio; Fuentes, Luis J.; Estevez, Angeles F.

2012-01-01

Previous studies have demonstrated that discriminative learning is facilitated when a particular outcome is associated with each relation to be learned. When this training procedure is applied (the differential outcome procedure; DOP), learning is faster and more accurate than when the more common non-differential outcome procedure is used. This…

Blackthorn: Large-Scale Interactive Multimodal Learning

DEFF Research Database (Denmark)

Zahálka, Jan; Rudinac, Stevan; Jónsson, Björn Thór

2018-01-01

learning process. The Ratio-64 data representation introduced in this work only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64- compressed data directly, greatly reducing...... outperforming the baseline with respect to the relevance of results: it vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset...

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Long lasting structural changes in primary motor cortex after motor skill learning: a behavioural and stereological study

Directory of Open Access Journals (Sweden)

PAOLA MORALES

2008-12-01

Full Text Available Many motor skills, once acquired, are stored over a long time period, probably sustained by permanent neuronal changes. Thus, in this paper we have investigated with quantitative stereology the generation and persistence of neuronal density changes in primary motor cortex (MI following motor skill learning (skilled reaching task. Rats were trained a lateralised reaching task during an "early" (22-31 days oíd or "late" (362-371 days oíd postnatal period. The trained and corresponding control rats were sacrificed at day 372, immediately after the behavioural testing. The "early" trained group preserved the learned skilled reaching task when tested at day 372, without requiring any additional training. The "late" trained group showed a similar capacity to that of the "early" trained group for learning the skilled reaching task. All trained animáis ("early" and "late" trained groups showed a significant Ínter hemispheric decrease of neuronal density in the corresponding motor forelimb representation área of MI (cortical layers II-III

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

Directory of Open Access Journals (Sweden)

Akira Taniguchi

2017-12-01

Full Text Available In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color. This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

Weakly Supervised Dictionary Learning

Science.gov (United States)

You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

2018-05-01

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

Studying depression using imaging and machine learning methods.

Science.gov (United States)

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Creation of Learning Motivation in the Process of Cognitive Learning in Teaching Music to 12-13 Years Old Pupils

OpenAIRE

Kepule, Iveta

2015-01-01

Creation of learning motivation is one of the main problems of the school. Motivation is the basis for self-improvement; its fulfillment facilitates development of the personality and encourages a pupil to study better in order to reach the set goal. If the teacher knows the reasons for the learning motivation of the pupils, he or she can select the methods and techniques, which could stimulate the inquiry process, and in addition to development of various skills and abilities would increase ...

Trade-off analysis of discharge-desiltation-turbidity and ANN analysis on sedimentation of a combined reservoir-reach system under multi-phase and multi-layer conjunctive releasing operation

Science.gov (United States)

Huang, Chien-Lin; Hsu, Nien-Sheng; Wei, Chih-Chiang; Yao, Chun-Hao

2017-10-01

Multi-objective reservoir operation considering the trade-off of discharge-desiltation-turbidity during typhoons and sediment concentration (SC) simulation modeling are the vital components for sustainable reservoir management. The purposes of this study were (1) to analyze the multi-layer release trade-offs between reservoir desiltation and intake turbidity of downstream purification plants and thus propose a superior conjunctive operation strategy and (2) to develop ANFIS-based (adaptive network-based fuzzy inference system) and RTRLNN-based (real-time recurrent learning neural networks) substitute SC simulation models. To this end, this study proposed a methodology to develop (1) a series of multi-phase and multi-layer sediment-flood conjunctive release modes and (2) a specialized SC numerical model for a combined reservoir-reach system. The conjunctive release modes involve (1) an optimization model where the decision variables are multi-phase reduction/scaling ratios and the timings to generate a superior total release hydrograph for flood control (Phase I: phase prior to flood arrival, Phase II/III: phase prior to/subsequent to peak flow) and (2) a combination method with physical limitations regarding separation of the singular hydrograph into multi-layer release hydrographs for sediment control. This study employed the featured signals obtained from statistical quartiles/sediment duration curve in mesh segmentation, and an iterative optimization model with a sediment unit response matrix and corresponding geophysical-based acceleration factors, for efficient parameter calibration. This research applied the developed methodology to the Shihmen Reservoir basin in Taiwan. The trade-off analytical results using Typhoons Sinlaku and Jangmi as case examples revealed that owing to gravity current and re-suspension effects, Phase I + II can de-silt safely without violating the intake's turbidity limitation before reservoir discharge reaches 2238 m3/s; however

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements.

Science.gov (United States)

Botzer, Lior; Karniel, Amir

2013-07-01

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Mean-field learning for satisfactory solutions

KAUST Repository

Tembine, Hamidou

2013-12-01

One of the fundamental challenges in distributed interactive systems is to design efficient, accurate, and fair solutions. In such systems, a satisfactory solution is an innovative approach that aims to provide all players with a satisfactory payoff anytime and anywhere. In this paper we study fully distributed learning schemes for satisfactory solutions in games with continuous action space. Considering games where the payoff function depends only on own-action and an aggregate term, we show that the complexity of learning systems can be significantly reduced, leading to the so-called mean-field learning. We provide sufficient conditions for convergence to a satisfactory solution and we give explicit convergence time bounds. Then, several acceleration techniques are used in order to improve the convergence rate. We illustrate numerically the proposed mean-field learning schemes for quality-of-service management in communication networks. © 2013 IEEE.

Views of Students on Learning with Technologies in Dutch Education and Training

NARCIS (Netherlands)

Jeroen Bottema; Pieter Swager

2012-01-01

The integrated use of technologies in learning in formal education and training in The Netherlands is far from realized, and there is still a long way to go to reach that goal. But what are the views of students and early career teachers about learning with technologies? This chapter focuses on

Using reflective learning journals to improve students learning and awareness

DEFF Research Database (Denmark)

Jensen, Lars Peter

2008-01-01

students are working in teams together and given special help to develop team and project work skills. When Danish and foreign students are grouped in mixed teams on the 2nd semester, still the Danish students are experts in project work and they are not familiar with taking in less skilled newcomers...... examples from the learning journals, proving that the students reach the learning goals of the course being able to discuss a more professional approach to their team work and they plan how to help foreigners entering their team.......This paper addresses the problem of mixing Danish engineering students having 3 years of experience with project work in teams (PBL setting at Aalborg University), with foreign students starting on Master Engineering educations with close to zero PBL experience. The first semester the foreign...

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

Science.gov (United States)

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

2004-01-01

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

Learning in the e-environment: new media and learning for the future

Directory of Open Access Journals (Sweden)

Milan Matijević

2015-03-01

Full Text Available We live in times of rapid change in all areas of science, technology, communication and social life. Every day we are asked to what extent school prepares us for these changes and for life in a new, multimedia environment. Children and adolescents spend less time at school or in other settings of learning than they do outdoors or within other social communities (family, clubs, societies, religious institutions and the like. Experts must constantly inquire about what exactly influences learning and development in our rich media environment. The list of the most important life competences has significantly changed and expanded since the last century. Educational experts are attempting to predict changes in the content and methodology of learning at the beginning of the 21st century. Answers are sought to key questions such as: what should one learn; how should one learn; where should one learn; why should one learn; and how do these answers relate to the new learning environment? In his examination of the way children and young people learn and grow up, the author places special attention on the relationship between personal and non-personal communication (e.g. the internet, mobile phones and different types of e-learning. He deals with today's questions by looking back to some of the more prominent authors and studies of the past fifty years that tackled identical or similar questions (Alvin Toffler, Ivan Illich, George Orwell, and the members of the Club of Rome. The conclusion reached is that in today's world of rapid and continuous change, it is much more crucial than in the last century, both, to be able to learn, and to adapt to learning with the help of new media.

Reaching the hard to reach.

Science.gov (United States)

Bhiwandi, P; Campbell, M; Potts, M

1994-01-01

The 1994 International Conference on Population and Development proposed increasing contraceptive couple protection from 550 million in 1995 to 880 million in 2015. The task for family planning (FP) programs is to provide access to services for, sometimes, inaccessible rural populations. FP need based on desire for no more children has ranged from under 20% in Senegal to almost 80% in Peru. Socioeconomic development was found not to be a prerequisite for fertility change. Gender inequalities in education and social autonomy must be changed. FP access is very important among women with a disadvantaged background or among women unsure about FP. Bangladesh is a good example of a country with increased contraceptive prevalence despite low income. The rule of thumb is that contraception increases of 15% contribute to a drop in family size of about one child. Program effectiveness is related to a variety of factors: contraceptive availability at many locations, acceptable price of contraception, delivery of the oral contraceptives without prescriptions, and other strategies. FP is a service not a medical treatment. A range of methods must be promoted and available from a range of facilities. Contraceptive use is dependent on the woman's stage in her lifecycle and is dependent on informed choice. Community-based distribution systems are effective, whereas free distribution by poorly-trained field workers is not always very effective because patient payment of all or part of the cost assures quality and freedom of choice. Effective programs for underprivileged groups involve aggressive, easy to manage programs that can be replicated rapidly. FP serves a useful function in depressing maternal mortality among the poor in Africa, who have no access to quality health services. Social marketing is an effective strategy for reaching remote areas. Political will and robust management are necessary commodities.

Differences in context and feedback result in different trajectories and adaptation strategies in reaching.

Directory of Open Access Journals (Sweden)

Fritzie Arce

Full Text Available Computational models of motor control have often explained the straightness of horizontal planar reaching movements as a consequence of optimal control. Departure from rectilinearity is thus regarded as sub-optimal. Here we examine if subjects may instead select to make curved trajectories following adaptation to force fields and visuomotor rotations. Separate subjects adapted to force fields with or without visual feedback of their hand trajectory and were retested after 24 hours. Following adaptation, comparable accuracies were achieved in two ways: with visual feedback, adapted trajectories in force fields were straight whereas without it, they remained curved. The results suggest that trajectory shape is not always straight, but is also influenced by the calibration of available feedback signals for the state estimation required by the task. In a follow-up experiment, where additional subjects learned a visuomotor rotation immediately after force field, the trajectories learned in force fields (straight or curved were transferred when directions of the perturbations were similar but not when directions were opposing. This demonstrates a strong bias by prior experience to keep using a recently acquired control policy that continues to produce successful performance inspite of differences in tasks and feedback conditions. On relearning of force fields on the second day, facilitation by intervening visuomotor rotations occurred only when required motor adjustments and calibration of feedback signals were similar in both tasks. These results suggest that both the available feedback signals and prior history of learning influence the choice and maintenance of control policy during adaptations.

Students’ needs of Computer Science: learning about image processing

Directory of Open Access Journals (Sweden)

Juana Marlen Tellez Reinoso

2009-12-01

Full Text Available To learn the treatment to image, specifically in the application Photoshop Marinates is one of the objectives in the specialty of Degree in Education, Computer Sciencie, guided to guarantee the preparation of the students as future professional, being able to reach in each citizen of our country an Integral General Culture. With that purpose a computer application is suggested, of tutorial type, entitled “Learning Treatment to Image".

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Science.gov (United States)

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals. PMID:22596161

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

NOTE FOR EDITOR: Is E-Learning Necessary For University Students? A Case From Iran

OpenAIRE

OMIDIAN, Faranak; KEYVANIFARD, Fatemeh

2012-01-01

Today many claim that e-learning can result in considerable time and cost-savings , such as traveling , work time and etc . This study was conducted to investigate these questions: should e-learning be used to reduce travel related stress? should e-learning be offered fully online to reach students living in remote areas? should e-learning be adopted to allow working students to study from home ? Pressure to use e-learning was developed as a factor to answer above questions. Data was colle...

Decoding natural reach-and-grasp actions from human EEG

Science.gov (United States)

Schwarz, Andreas; Ofner, Patrick; Pereira, Joana; Ioana Sburlea, Andreea; Müller-Putz, Gernot R.

2018-02-01

Objective. Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates. Approach. In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition. Main results. Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD  ±  5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD  ±  4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD  ±  8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum. Significance. We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people’s everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.

Associative Learning Through Acquired Salience.

Science.gov (United States)

Treviño, Mario

2015-01-01

Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as they become powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a linear model of associative learning coupled to a series of mathematical representations for stimulus salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder, and addiction.

A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits

OpenAIRE

Simmonds, Anna J.

2015-01-01

Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred fr...

Probing the reaching-grasping network in humans through multivoxel pattern decoding.

Science.gov (United States)

Di Bono, Maria Grazia; Begliomini, Chiara; Castiello, Umberto; Zorzi, Marco

2015-11-01

The quest for a putative human homolog of the reaching-grasping network identified in monkeys has been the focus of many neuropsychological and neuroimaging studies in recent years. These studies have shown that the network underlying reaching-only and reach-to-grasp movements includes the superior parieto-occipital cortex (SPOC), the anterior part of the human intraparietal sulcus (hAIP), the ventral and the dorsal portion of the premotor cortex, and the primary motor cortex (M1). Recent evidence for a wider frontoparietal network coding for different aspects of reaching-only and reach-to-grasp actions calls for a more fine-grained assessment of the reaching-grasping network in humans by exploiting pattern decoding methods (multivoxel pattern analysis--MVPA). Here, we used MPVA on functional magnetic resonance imaging (fMRI) data to assess whether regions of the frontoparietal network discriminate between reaching-only and reach-to-grasp actions, natural and constrained grasping, different grasp types, and object sizes. Participants were required to perform either reaching-only movements or two reach-to-grasp types (precision or whole hand grasp) upon spherical objects of different sizes. Multivoxel pattern analysis highlighted that, independently from the object size, all the selected regions of both hemispheres contribute in coding for grasp type, with the exception of SPOC and the right hAIP. Consistent with recent neurophysiological findings on monkeys, there was no evidence for a clear-cut distinction between a dorsomedial and a dorsolateral pathway that would be specialized for reaching-only and reach-to-grasp actions, respectively. Nevertheless, the comparison of decoding accuracy across brain areas highlighted their different contributions to reaching-only and grasping actions. Altogether, our findings enrich the current knowledge regarding the functional role of key brain areas involved in the cortical control of reaching-only and reach-to-grasp actions

Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

International Nuclear Information System (INIS)

Liu, Hui; Tian, Hong-qi; Li, Yan-fei

2015-01-01

Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

Digital Learning Environments: New possibilities and opportunities

Directory of Open Access Journals (Sweden)

Otto Peters

2000-06-01

Full Text Available This paper deals with the general problem whether and, if so, how far the impact of the digitised learning environment on our traditional distance education will change the way in which teachers teach and learners learn. Are the dramatic innovations a menace to established ways of learning and teaching or are they the panacea to overcome some of the difficulties of our system of higher learning and to solve some of our educational problems caused by the big and far-reaching educational paradigm shift? This paper will not deal with technical or technological achievements in the field of information and communication which are, of course, revolutionary and to be acknowledged and admired. Rather, the digital learning environment will be analysed from a pedagogical point of view in order to find out what exactly are the didactic possibilities and opportunities and what are its foreseeable disadvantages.

The Teaching and Learning of Esperanto

Directory of Open Access Journals (Sweden)

Duncan Charters

2015-04-01

Full Text Available One of the first tasks faced by Zamenhof, the inventor of Esperanto (1887, was establishing its status as a living language, achieved in part by teaching the language to others, in part by translation and literary creation, and in part by forming a community of users. One of the earliest learners, Leo Tolstoy, emphasized its ease of learning, and both the early history of the language and contemporary experience show that the receptive and productive skills entailed in learning the language are unusually mutually reinforcing. In formal language-learning situations, students are able to reach an acceptable level of proficiency relatively quickly, allowing them to put the language to practical use. They are also able to learn on their own. Ease of learning builds confidence, so that Esperanto constitutes a good introduction to language study in general, even though the language is more complex linguistically than it may appear at first sight. The language also helps the learning of cultural sensitivity. In recent years, electronic aids to teaching and learning have proliferated and there are many resources available to the teacher and learner.

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

Science.gov (United States)

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

2017-03-01

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

Why Do Athletes Drink Sports Drinks? A Learning Cycle to Explore the Concept of Osmosis

Science.gov (United States)

Carlsen, Brook; Marek, Edmund A.

2010-01-01

Why does an athlete reach for a sports drink after a tough game or practice? The learning cycle presented in this article helps students answer this question. Learning cycles (Marek 2009) are designed to guide students through direct experiences with a particular concept. In this article, students learn about "osmosis," or the moving of water into…

A program wide framework for evaluating data driven teaching and learning - earth analytics approaches, results and lessons learned

Science.gov (United States)

Wasser, L. A.; Gold, A. U.

2017-12-01

There is a deluge of earth systems data available to address cutting edge science problems yet specific skills are required to work with these data. The Earth analytics education program, a core component of Earth Lab at the University of Colorado - Boulder - is building a data intensive program that provides training in realms including 1) interdisciplinary communication and collaboration 2) earth science domain knowledge including geospatial science and remote sensing and 3) reproducible, open science workflows ("earth analytics"). The earth analytics program includes an undergraduate internship, undergraduate and graduate level courses and a professional certificate / degree program. All programs share the goals of preparing a STEM workforce for successful earth analytics driven careers. We are developing an program-wide evaluation framework that assesses the effectiveness of data intensive instruction combined with domain science learning to better understand and improve data-intensive teaching approaches using blends of online, in situ, asynchronous and synchronous learning. We are using targeted online search engine optimization (SEO) to increase visibility and in turn program reach. Finally our design targets longitudinal program impacts on participant career tracts over time.. Here we present results from evaluation of both an interdisciplinary undergrad / graduate level earth analytics course and and undergraduate internship. Early results suggest that a blended approach to learning and teaching that includes both synchronous in-person teaching and active classroom hands-on learning combined with asynchronous learning in the form of online materials lead to student success. Further we will present our model for longitudinal tracking of participant's career focus overtime to better understand long-term program impacts. We also demonstrate the impact of SEO optimization on online content reach and program visibility.

Hydroxyurea Therapy for Children With Sickle Cell Anemia in Sub‐Saharan Africa: Rationale and Design of the REACH Trial

Science.gov (United States)

Tshilolo, Léon; Santos, Brigida; Tomlinson, George A.; Stuber, Susan; Latham, Teresa; Aygun, Banu; Obaro, Stephen K.; Olupot‐Olupot, Peter; Williams, Thomas N.; Odame, Isaac; Ware, Russell E.

2015-01-01

Background Sickle cell anemia (SCA) is an inherited hematological disorder that causes a large but neglected global health burden, particularly in Africa. Hydroxyurea represents the only available disease‐modifying therapy for SCA, and has proven safety and efficacy in high‐resource countries. In sub‐Saharan Africa, there is minimal use of hydroxyurea, due to lack of data, absence of evidence‐based guidelines, and inexperience among healthcare providers. Procedure A partnership was established between investigators in North America and sub‐Saharan Africa, to develop a prospective multicenter research protocol designed to provide data on the safety, feasibility, and benefits of hydroxyurea for children with SCA. Results The Realizing Effectiveness Across Continents with Hydroxyurea (REACH, ClinicalTrials.gov NCT01966731) trial is a prospective, phase I/II open‐label dose escalation study of hydroxyurea that will treat a total of 600 children age 1–10 years with SCA: 150 at each of four different clinical sites within sub‐Saharan Africa (Angola, Democratic Republic of Congo, Kenya, and Uganda). The primary study endpoint will be severe hematological toxicities that occur during the fixed‐dose treatment phase. REACH has an adaptive statistical design that allows for careful assessment of toxicities to accurately identify a safe hydroxyurea dose. Conclusions REACH will provide data that address critical gaps in knowledge for the treatment of SCA in sub‐Saharan Africa. By developing local expertise with the use of hydroxyurea and helping to establish treatment guidelines, the REACH trial results will have the potential to transform care for children with SCA in Africa. Pediatr Blood Cancer © 2015 Wiley Periodicals, Inc. PMID:26275071

The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

Science.gov (United States)

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-06-15

Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

A theory of causal learning in children: Causal maps and Bayes nets

OpenAIRE

Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

GAP-REACH

Science.gov (United States)

Lewis-Fernández, Roberto; Raggio, Greer A.; Gorritz, Magdaliz; Duan, Naihua; Marcus, Sue; Cabassa, Leopoldo J.; Humensky, Jennifer; Becker, Anne E.; Alarcón, Renato D.; Oquendo, María A.; Hansen, Helena; Like, Robert C.; Weiss, Mitchell; Desai, Prakash N.; Jacobsen, Frederick M.; Foulks, Edward F.; Primm, Annelle; Lu, Francis; Kopelowicz, Alex; Hinton, Ladson; Hinton, Devon E.

2015-01-01

Growing awareness of health and health care disparities highlights the importance of including information about race, ethnicity, and culture (REC) in health research. Reporting of REC factors in research publications, however, is notoriously imprecise and unsystematic. This article describes the development of a checklist to assess the comprehensiveness and the applicability of REC factor reporting in psychiatric research publications. The 16-itemGAP-REACH© checklist was developed through a rigorous process of expert consensus, empirical content analysis in a sample of publications (N = 1205), and interrater reliability (IRR) assessment (N = 30). The items assess each section in the conventional structure of a health research article. Data from the assessment may be considered on an item-by-item basis or as a total score ranging from 0% to 100%. The final checklist has excellent IRR (κ = 0.91). The GAP-REACH may be used by multiple research stakeholders to assess the scope of REC reporting in a research article. PMID:24080673

Drag Reduction of an Airfoil Using Deep Learning

Science.gov (United States)

Jiang, Chiyu; Sun, Anzhu; Marcus, Philip

2017-11-01

We reduced the drag of a 2D airfoil by starting with a NACA-0012 airfoil and used deep learning methods. We created a database which consists of simulations of 2D external flow over randomly generated shapes. We then developed a machine learning framework for external flow field inference given input shapes. Past work which utilized machine learning in Computational Fluid Dynamics focused on estimations of specific flow parameters, but this work is novel in the inference of entire flow fields. We further showed that learned flow patterns are transferable to cases that share certain similarities. This study illustrates the prospects of deeper integration of data-based modeling into current CFD simulation frameworks for faster flow inference and more accurate flow modeling.

Substance P signalling in primary motor cortex facilitates motor learning in rats.

Directory of Open Access Journals (Sweden)

Benjamin Hertler

Full Text Available Among the genes that are up-regulated in response to a reaching training in rats, Tachykinin 1 (Tac1-a gene that encodes the neuropeptide Substance P (Sub P-shows an especially strong expression. Using Real-Time RT-PCR, a detailed time-course of Tac1 expression could be defined: a significant peak occurs 7 hours after training ended at the first and second training session, whereas no up-regulation could be detected at a later time-point (sixth training session. To assess the physiological role of Sub P during movement acquisition, microinjections into the primary motor cortex (M1 contralateral to the trained paw were performed. When Sub P was injected before the first three sessions of a reaching training, effectiveness of motor learning became significantly increased. Injections at a time-point when rats already knew the task (i.e. training session ten and eleven had no effect on reaching performance. Sub P injections did not influence the improvement of performance within a single training session, but retention of performance between sessions became strengthened at a very early stage (i.e. between baseline-training and first training session. Thus, Sub P facilitates motor learning in the very early phase of skill acquisition by supporting memory consolidation. In line with these findings, learning related expression of the precursor Tac1 occurs at early but not at later time-points during reaching training.

Substance P signalling in primary motor cortex facilitates motor learning in rats.

Science.gov (United States)

Hertler, Benjamin; Hosp, Jonas Aurel; Blanco, Manuel Buitrago; Luft, Andreas Rüdiger

2017-01-01

Among the genes that are up-regulated in response to a reaching training in rats, Tachykinin 1 (Tac1)-a gene that encodes the neuropeptide Substance P (Sub P)-shows an especially strong expression. Using Real-Time RT-PCR, a detailed time-course of Tac1 expression could be defined: a significant peak occurs 7 hours after training ended at the first and second training session, whereas no up-regulation could be detected at a later time-point (sixth training session). To assess the physiological role of Sub P during movement acquisition, microinjections into the primary motor cortex (M1) contralateral to the trained paw were performed. When Sub P was injected before the first three sessions of a reaching training, effectiveness of motor learning became significantly increased. Injections at a time-point when rats already knew the task (i.e. training session ten and eleven) had no effect on reaching performance. Sub P injections did not influence the improvement of performance within a single training session, but retention of performance between sessions became strengthened at a very early stage (i.e. between baseline-training and first training session). Thus, Sub P facilitates motor learning in the very early phase of skill acquisition by supporting memory consolidation. In line with these findings, learning related expression of the precursor Tac1 occurs at early but not at later time-points during reaching training.

Effects of acute sleep deprivation on motor and reversal learning in mice.

Science.gov (United States)

Varga, Andrew W; Kang, Mihwa; Ramesh, Priyanka V; Klann, Eric

2014-10-01

Sleep supports the formation of a variety of declarative and non-declarative memories, and sleep deprivation often impairs these types of memories. In human subjects, natural sleep either during a nap or overnight leads to long-lasting improvements in visuomotor and fine motor tasks, but rodent models recapitulating these findings have been scarce. Here we present evidence that 5h of acute sleep deprivation impairs mouse skilled reach learning compared to a matched period of ad libitum sleep. In sleeping mice, the duration of total sleep time during the 5h of sleep opportunity or during the first bout of sleep did not correlate with ultimate gain in motor performance. In addition, we observed that reversal learning during the skilled reaching task was also affected by sleep deprivation. Consistent with this observation, 5h of sleep deprivation also impaired reversal learning in the water-based Y-maze. In conclusion, acute sleep deprivation negatively impacts subsequent motor and reversal learning and memory. Copyright © 2014 Elsevier Inc. All rights reserved.

Reach/frequency for printed media: Personal probabilities or models

DEFF Research Database (Denmark)

Mortensen, Peter Stendahl

2000-01-01

The author evaluates two different ways of estimating reach and frequency of plans for printed media. The first assigns reading probabilities to groups of respondents and calculates reach and frequency by simulation. the second estimates parameters to a model for reach/frequency. It is concluded ...... and estiamtes from such models are shown to be closer to panel data. the problem, however, is to get valid input for such models from readership surveys. Means for this are discussed....

Measuring organizational learning. Model testing in two Romanian universities

OpenAIRE

Alexandra Luciana Guţă

2014-01-01

The scientific literature associates organizational learning with superior organization performance. If we refer to the academic environment, we appreciate that it can develop and reach better levels of performance through changes driven from the inside. Thus, through this paper we elaborate on a conceptual model of organizational learning and we test the model on a sample of employees (university teachers and researchers) from two Romanian universities. The model comprises the process of org...

Region and task-specific activation of Arc in primary motor cortex of rats following motor skill learning.

Science.gov (United States)

Hosp, J A; Mann, S; Wegenast-Braun, B M; Calhoun, M E; Luft, A R

2013-10-10

Motor learning requires protein synthesis within the primary motor cortex (M1). Here, we show that the immediate early gene Arc/Arg3.1 is specifically induced in M1 by learning a motor skill. Arc mRNA was quantified using a fluorescent in situ hybridization assay in adult Long-Evans rats learning a skilled reaching task (SRT), in rats performing reaching-like forelimb movement without learning (ACT) and in rats that were trained in the operant but not the motor elements of the task (controls). Apart from M1, Arc expression was assessed within the rostral motor area (RMA), primary somatosensory cortex (S1), striatum (ST) and cerebellum. In SRT animals, Arc mRNA levels in M1 contralateral to the trained limb were 31% higher than ipsilateral (pmotor skill learning in rats. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

[The REACH legislation: the consumer and environment protection perspective].

Science.gov (United States)

Gundert-Remy, Ursula

2008-12-01

REACH has been initiated with the aim of improving existing legislation. In order to assist in the interpretation of the REACH legislation, guidance documents have been developed, which have only lately become available. According to the REACH annexes and supported by guidance documents, waiving of test requirements will be possible, thus, opening the possibility that under REACH no new (eco)toxicological data will be required. Concerning products, a guidance document was released in April 2008 stating that the substance concentration threshold of 0.1 % (w/w) applies to the article as produced or imported and it does not relate to the homogeneous materials or parts of an article, but relates to the article as such (i.e., as produced or imported). Hence, notification will not be required for many products containing chemicals with properties which place them on the candidate list for authorization. In summary, it is at present not foreseeable whether the expected benefit of the REACH legislation will materialise for the environment and for the health of consumers and at the work place.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

Directory of Open Access Journals (Sweden)

Gregory A Ross

Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Environmental Identity: A New Approach to Understanding Students' Participation in Environmental Learning Programs

Science.gov (United States)

Jaksha, Amanda P.

2013-01-01

The goal of this study is to develop an understanding of how participants express their environmental identities during an environmental learning program. Past research on the outcomes of environmental learning programs has focused primarily on changes in knowledge and attitudes. However, even if knowledge or attitudes can be accurately measured,…

Costs of Low-Scale Distance Learning Programs: A Case of Distance Learning Courses in the Aegean Islands.

Directory of Open Access Journals (Sweden)

Costas Tsolakidis

2010-03-01

Full Text Available The advance of Information and Communication Technology (ICT and the reduction of cost in digital applications motivate course designers to develop new application of distance learning programs so as to meet the increasing educational needs in the knowledge-based society. As a consequence, distance learning courses are increasing in number, credibility and acceptability all over the world. The question is whether these programs are efficient in terms of costs. The main theme of this work is to investigate cost behaviour and estimate cost efficiency of distance learning courses applied in low-inhabited, remote islands. The target group consists of high school students of Grade I. The distance learning course that is designed uses several scenarios of the “what-if form” and reaches the conclusion that cost of such solutions is far lower than that of any traditional course, even at the absence of scale economies.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Studying depression using imaging and machine learning methods

Directory of Open Access Journals (Sweden)

Meenal J. Patel

2016-01-01

Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

Science.gov (United States)

Kautzky, Alexander; Dold, Markus; Bartova, Lucie; Spies, Marie; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Fabbri, Chiara; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD. © Copyright 2017 Physicians Postgraduate Press, Inc.

The Aalborg Model and participant directed learning

DEFF Research Database (Denmark)

Qvist, Palle

2009-01-01

Preparing students for a life as active citizens in a democratic society is one of the aims within the Bologna process. The Council of Europe has also stressed the importance of focus on democracy in Higher Education. Higher Education is seen as important to develop a democratic culture among...... students. Teaching democracy should be promoted in lessons and curricula. Creating democratic learning systems in institutions of higher education could be the answer to reaching the aim related to democracy. The Aalborg Model practised at Aalborg University is a learning system which has collaborative...

It takes biking to learn: Physical activity improves learning a second language.

Science.gov (United States)

Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

2017-01-01

Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level.

Hydrodynamic Simulation of the Columbia River, Hanford Reach, 1940--2004

Energy Technology Data Exchange (ETDEWEB)

Waichler, Scott R.; Perkins, William A.; Richmond, Marshall C.

2005-06-15

Many hydrological and biological problems in the Columbia River corridor through the Hanford Site require estimates of river stage (water surface elevation) or river flow and velocity. Systematic collection of river stage data at locations in the Hanford Reach began in 1991, but many environmental projects need river stage information at unmeasured locations or over longer time periods. The Modular Aquatic Simulation System 1D (MASS1), a one-dimensional, unsteady hydrodynamic and water quality model, was used to simulate the Columbia River from Priest Rapids Dam to McNary Dam from 1940 to 2004, providing estimates of water surface elevation, volumetric flow rate, and flow velocity at 161 locations on the Hanford Reach. The primary input data were bathymetric/topographic cross sections of the Columbia River channel, flow rates at Priest Rapids Dam, and stage at McNary Dam. Other inputs included Yakima River and Snake River inflows. Available flow data at a gaging station just below Priest Rapids Dam was mean daily flow from 1940 to 1986 and hourly thereafter. McNary dam was completed in 1957, and hourly stage data are available beginning in 1975. MASS1 was run at an hourly timestep and calibrated and tested using 1991--2004 river stage data from six Hanford Reach locations (areas 100B, 100N, 100D, 100H, 100F, and 300). Manning's roughness coefficient in the Reach above each river recorder location was adjusted using an automated genetic algorithm and gradient search technique in three separate calibrations, corresponding to different data subsets, with minimization of mean absolute error as the objective. The primary calibration was based on 1999, a representative year, and included all locations. The first alternative calibration also used all locations but was limited in time to a high-flow period during spring and early summer of 1997. The second alternative calibration was based on 1999 and included only 300 Area stage data. Model goodness-of-fit for all

IMAGE CAPTURE WITH SYNCHRONIZED MULTIPLE-CAMERAS FOR EXTRACTION OF ACCURATE GEOMETRIES

Directory of Open Access Journals (Sweden)

M. Koehl

2016-06-01

Full Text Available This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D to allow the accuracy assessment.

Image Capture with Synchronized Multiple-Cameras for Extraction of Accurate Geometries

Science.gov (United States)

Koehl, M.; Delacourt, T.; Boutry, C.

2016-06-01

This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D) to allow the accuracy assessment.

Social Learning towards Sustainability: problematic, perspectives and promise

NARCIS (Netherlands)

Wals, A.E.J.; Rodela, R.

2014-01-01

A common thread throughout this special issue is that sustainability is not a destiny one can eventually reach, but rather a continuous learning path towards transformation that should be profound (e.g. affecting moral standards and value systems), transversal (e.g. requiring the involvement of

Effect of reinforcement learning on coordination of multiangent systems

Science.gov (United States)

Bukkapatnam, Satish T. S.; Gao, Greg

2000-12-01

For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

Do working environment interventions reach shift workers?

DEFF Research Database (Denmark)

Nabe-Nielsen, Kirsten; Jørgensen, Marie Birk; Garde, Anne Helene

2016-01-01

PURPOSE: Shift workers are exposed to more physical and psychosocial stressors in the working environment as compared to day workers. Despite the need for targeted prevention, it is likely that workplace interventions less frequently reach shift workers. The aim was therefore to investigate whether...... the reach of workplace interventions varied between shift workers and day workers and whether such differences could be explained by the quality of leadership exhibited at different times of the day. METHODS: We used questionnaire data from 5361 female care workers in the Danish eldercare sector...

Game-powered machine learning.

Science.gov (United States)

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

Reaching Adolescents and Youth in Burkina Faso, Guinea-Bissau

African Journals Online (AJOL)

AJRH Managing Editor

typical profile of individuals in contact with peer educators or attending youth ... being reached (versus not reached) by programs ... characteristics in order to serve groups that may be ... places for counseling services but the frequency of.

Accurate and precise determination of 2-25mg amounts of uranium by means of a special automatic potentiometric titration

International Nuclear Information System (INIS)

Slanina, J.; Bakker, F.; Groen, A.J.P.; Lingerak, W.A.

1978-01-01

A precise and accurate potentiometric titration of 2-25 mg of uranium is described. The uranium is reduced to U(IV) according to the method of Eberle et al. [3], and titrated with 0.05 N potassium dichromate, using a platinum indicator electrode. During the sample preparation the walls of the titration vessel are cleaned by centrifugation. To avoid overshoot of the set point a special differentiator is described, that interrupts the titration until equilibrium is reached. The precision of the method is 0.02%, the accuracy is better than 0.04% rel. Each titration takes 5 min. (orig.) [de

Reaching soldiers with untreated substance use disorder: lessons learned in the development of a marketing campaign for the Warrior Check-Up study.

Science.gov (United States)

Walton, Thomas O; Walker, Denise D; Kaysen, Debra L; Roffman, Roger A; Mbilinyi, Lyungai; Neighbors, Clayton

2013-07-01

The Warrior Check-Up, a confidential telephone-delivered intervention, is designed to reach active-duty soldiers with untreated substance-use disorder at a large U.S. military base. This paper describes the development and successful implementation of the study's marketing strategies at the recruitment period's midpoint (2010-2012). Qualitative analyses of focus groups (n = 26) and survey responses (n = 278) describe the process of campaign design. Measures of demographics, media exposure, post-traumatic stress, anxiety and depression gathered from callers (n = 172) are used in quantitative analysis assessing the campaign's success in reaching this population. Implications, limitations, and suggestions for future research are discussed. Department of Defense provided study funding.

Keep it Accurate and Diverse

DEFF Research Database (Denmark)

Ali Bagheri, Mohammad; Gao, Qigang; Guerrero, Sergio Escalera

2015-01-01

the performance of an ensemble of action learning techniques, each performing the recognition task from a different per- spective. The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple...... to improve the recognition perfor- mance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual clas- sifiers are compared with those...... obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology....

Learning styles: The learning methods of air traffic control students

Science.gov (United States)

Jackson, Dontae L.

In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

Applied CAL on Problem Based Learning Using Gagne’s Instructional Design

Directory of Open Access Journals (Sweden)

Sri Sundari Purbohadi

2014-10-01

Full Text Available Abstract— In the Problem-Based Learning (PBL model, students are expected to study independently. One of the methods that can improve the ability or skill of learners is using Computer Assisted Learning (CAL. Implementation of CAL in PBL should be able to create Self-Directed Learning (SDL culture through appropriate instructional design and interesting modules. In this paper, the CAL software is developed using multimedia learning principles, convenient appearance, and user-friendly navigation. The CAL’s learning content is designed using Gagne's instructional design. The experiment proved the CAL was able to give effect size 0.89 and developed self-directed learning culture. From the interviews, students were very glad and interested to use the CAL modules because they can learn anytime and can reach the course objectives without a lecturer.

E-LEARNING INNOVATIONS IN HIGHER EDUCATION

Directory of Open Access Journals (Sweden)

NICOLETA GUDANESCU

2012-05-01

Full Text Available This scientific work is presenting the ways to do computer assisted education for students, giving the good practice examples, presenting new electronic learning systems, the advantages and limits and to try to emphasize that these days E-learning is one of the most efficient way to reach education at all levels, specially higher education systems. The objectives of this paper are: to explain the contribution of modern technologies and electronic systems to educational processes, to define the concept of technology based learning, to introduce the electronic tools for education, to present good practice examples in implementing E-learning systems in higher education and corporate environment in Romania and last but not least the new electronic learning systems. Introducing the computers and ITC in educational processes facilitates them and makes the educational system modern and efficient. E - learning innovations offers a core group of professional development courses designed to help anyone achieve professional advancement and personal enrichment. The programs are founded on an extensive experience and understanding of technology-based learning environments. They focus on the most current industry practices for various learning environments and best approaches for multiple learning styles. They ensure that the students get the information and skills needed to achieve more in teaching practice and to confidently enter the distance or online classroom.

REACH and nanomaterials: current status

International Nuclear Information System (INIS)

Alessandrelli, Maria; Di Prospero Fanghella, Paola; Polci, Maria Letizia; Castelli, Stefano; Pettirossi, Flavio

2015-01-01

New challenges for regulators are emerging about a specific assessment and appropriate management of the potential risks of nanomaterials. In the framework of European legislation on chemicals, Regulation (EC) No. 1907/2006 REACH aims to ensure the safety of human health and the environment through the collection of information on the physico-chemical characteristics of the substances and on their profile (eco) toxicological and the identification of appropriate risk management linked to 'exposure to these substances without impeding scientific progress and the competitiveness of industry. In order to cover the current shortage of information on the safety of nanomaterials and tackle the acknowledged legal vacuum, are being a rich activities, carried out both by regulators both by stake holders, and discussions on the proposals for adapting the European regulatory framework for chemicals . The European Commission is geared to strengthen the REACH Regulation by means of updates of its annexes. The importance of responding to the regulatory requirements has highlighted the need for cooperation between European organizations, scientists and industries to promote and ensure the safe use of nanomaterials. [it

Experimenting `learn by doing' and `learn by failing'

Science.gov (United States)

Pozzi, Rossella; Noè, Carlo; Rossi, Tommaso

2015-01-01

According to the literature, in recent years, developing experiential learning has fulfilled the requirement of a deep understanding of lean philosophy by engineering students, demonstrating the advantages and disadvantages of some of the key principles of lean manufacturing. On the other hand, the literature evidences how some kinds of game-based experiential learning overlook daily difficulties, which play a central role in manufacturing systems. To fill the need of a game overcoming such lack of vision, an innovative game direct in-field, named Kart Factory, has been developed. Actual production shifts are simulated, while keeping all the elements peculiar to a real production set (i.e. complexity, effort, safety). The working environment is a real pedal car assembly department, the products to be assembled have relevant size and weight (i.e. up to 35 kg approximately), and the provided tools are real production equipment (e.g. keys, screwdrivers, trans-pallets, etc.). Due to the need to maximise the impact on students, a labour-intensive process characterises the production department. The whole training process is based on three educational principles: Experience Value Principle, Error Value Principle, and Team Value Principle. As the 'learn by doing' and 'learn by failing' are favoured, the theory follows the practice, while crating the willingness to 'do' instead of just designing or planning. The gathered data prove the Kart Factory's effectiveness in reaching a good knowledge of lean concepts, notwithstanding the students' initial knowledge level.

Constant size descriptors for accurate machine learning models of molecular properties

Science.gov (United States)

Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole; Yaron, David J.

2018-06-01

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

Learning curves in health professions education.

Science.gov (United States)

Pusic, Martin V; Boutis, Kathy; Hatala, Rose; Cook, David A

2015-08-01

Learning curves, which graphically show the relationship between learning effort and achievement, are common in published education research but are not often used in day-to-day educational activities. The purpose of this article is to describe the generation and analysis of learning curves and their applicability to health professions education. The authors argue that the time is right for a closer look at using learning curves-given their desirable properties-to inform both self-directed instruction by individuals and education management by instructors.A typical learning curve is made up of a measure of learning (y-axis), a measure of effort (x-axis), and a mathematical linking function. At the individual level, learning curves make manifest a single person's progress towards competence including his/her rate of learning, the inflection point where learning becomes more effortful, and the remaining distance to mastery attainment. At the group level, overlaid learning curves show the full variation of a group of learners' paths through a given learning domain. Specifically, they make overt the difference between time-based and competency-based approaches to instruction. Additionally, instructors can use learning curve information to more accurately target educational resources to those who most require them.The learning curve approach requires a fine-grained collection of data that will not be possible in all educational settings; however, the increased use of an assessment paradigm that explicitly includes effort and its link to individual achievement could result in increased learner engagement and more effective instructional design.

Improving exposure scenario definitions within REACH

DEFF Research Database (Denmark)

Lee, Jihyun; Pizzol, Massimo; Thomsen, Marianne

In recent years, the paradigm of chemical management system has changed from being toxicity oriented and media based to being risk oriented and receptor based. This trend is evident not only regarding environmental quality standards, but also for industrial chemical regulations. Political...... instruments to support a precautionary chemicals management system and to protect receptor’s health have also been increasing. Since 2007, the European Union adopted REACH (the Regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals): REACH makes industry responsible for assessing...... and managing the risks posed by industrial chemicals and providing appropriate safety information to their users (EC, 2007). However, to ensure a high level of protection of human health and the environment, there is a need to consider ‘aggregate exposure’ including background exposures from environment which...

Song learning and cognitive ability are not consistently related in a songbird.

Science.gov (United States)

Anderson, Rindy C; Searcy, William A; Peters, Susan; Hughes, Melissa; DuBois, Adrienne L; Nowicki, Stephen

2017-03-01

Learned aspects of song have been hypothesized to signal cognitive ability in songbirds. We tested this hypothesis in hand-reared song sparrows (Melospiza melodia) that were tutored with playback of adult songs during the critical period for song learning. The songs developed by the 19 male subjects were compared to the model songs to produce two measures of song learning: the proportion of notes copied from models and the average spectrogram cross-correlation between copied notes and model notes. Song repertoire size, which reflects song complexity, was also measured. At 1 year of age, subjects were given a battery of five cognitive tests that measured speed of learning in the context of a novel foraging task, color association, color reversal, detour-reaching, and spatial learning. Bivariate correlations between the three song measures and the five cognitive measures revealed no significant associations. As in other studies of avian cognition, different cognitive measures were for the most part not correlated with each other, and this result remained true when 22 hand-reared female song sparrows were added to the analysis. General linear mixed models controlling for effects of neophobia and nest of origin indicated that all three song measures were associated with better performance on color reversal and spatial learning but were associated with worse performance on novel foraging and detour-reaching. Overall, the results do not support the hypothesis that learned aspects of song signal cognitive ability.

A theory of causal learning in children: causal maps and Bayes nets.

Science.gov (United States)

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

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Evaluation of Contribution of Local Newspapers to Lifelong Learning (Example of Bartin Province)

Science.gov (United States)

Çuhadar, Elif; Ünal, Fatma

2018-01-01

In this study, while the definition of informal education, which displays the main features of lifelong learning, is made, it is also attempted to identify the contributions of the local newspapers, through which the society can reach its own unique and necessary information, to the lifelong learning of their readers. In the research, within this…

Organizational Learning Capability: An Example of University Hospital

Directory of Open Access Journals (Sweden)

Yasin UZUNTARLA

2015-06-01

Full Text Available In health care institutions aiming healthy society by the way protecting and promoting human health, reaching information has a vital importance. This descriptive research purposed an evaluation of organizational learning capability of 396 employees working in Gülhane Military Medical Academy Hospital. A questionnaire including socio-demographic characteristics was used along with Organizational Learning Capability scale designed by Ricardo CHIVA and His Friends. Data acquired was analyzed with SPSS 15.0 program. Participants’ Organizational Learning Capability and its subscales means were assessed in terms of their sociodemographic characteristics. Assessing participants’ answers in terms of 5 subscales which are experimentation, risk taking, interaction with the external environment, dialogue and participatory decision-making; for education level and professional groups, statistical significant differences was found between Organizational Learning Capability and its subscales means.

Reach, engagement, and effectiveness: a systematic review of evaluation methodologies used in health promotion via social networking sites.

Science.gov (United States)

Lim, Megan S C; Wright, Cassandra J C; Carrotte, Elise R; Pedrana, Alisa E

2016-02-01

Issue addressed Social networking sites (SNS) are increasingly popular platforms for health promotion. Advancements in SNS health promotion require quality evidence; however, interventions are often not formally evaluated. This study aims to describe evaluation practices used in SNS health promotion. Methods A systematic review was undertaken of Medline, PsycINFO, Scopus, EMBASE, CINAHL Plus, Communication and Mass Media Complete, and Cochrane Library databases. Articles published between 2006 and 2013 describing any health promotion intervention delivered using SNS were included. Results Forty-seven studies were included. There were two main evaluation approaches: closed designs (n=23), which used traditional research designs and formal recruitment procedures; and open designs (n=19), which evaluated the intervention in a real-world setting, allowing unknown SNS users to interact with the content without enrolling in research. Closed designs were unable to assess reach and engagement beyond their research sample. Open designs often relied on weaker study designs with no use of objective outcome measures and yielded low response rates. Conclusions Barriers to evaluation included low participation rates, high attrition, unknown representativeness and lack of comparison groups. Acceptability was typically assessed among those engaged with the intervention, with limited population data available to accurately assess intervention reach. Few studies were able to assess uptake of the intervention in a real-life setting while simultaneously assessing effectiveness of interventions with research rigour. So what? Through use of quasi-experimental or well designed before-after evaluations, in combination with detailed engagement metrics, it is possible to balance assessment of effectiveness and reach to evaluate SNS health promotion.

Barriers and Opportunities of e-Learning Implementation in Iraq: A Case of Public Universities

Science.gov (United States)

Al-Azawei, Ahmed; Parslow, Patrick; Lundqvist, Karsten

2016-01-01

Although the implementation of e-learning initiatives has reached advanced stages in developed countries, it is still in its infancy in many developing nations and the Middle East in particular. Recently, few public universities in Iraq have initiated limited attempts to use e-learning alongside traditional classrooms. However, different obstacles…

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

Science.gov (United States)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Conceptualizing impact assessment as a learning process

International Nuclear Information System (INIS)

Sánchez, Luis E.; Mitchell, Ross

2017-01-01

This paper explores how project developers and their consultants, government regulators and stakeholders can learn from the impact assessment (IA) process, thus potentially improving its effectiveness and enhancing project sustainability. Despite the benefits that learning can bring to an organization, failure to learn appears commonplace both within the IA process and, once approved, subsequent industrial development. To nurture organizational learning through IA, enabling structures that foster information sharing and interpretation and enhance organizational memory are needed. In this paper learning outcomes are grouped into three categories: acquisition of knowledge and skills, developing new behaviors and developing sustainability-oriented norms and values. Means to achieve such outcomes include education and training, experiential learning, learning through public participation (social learning) and a ‘learning organization approach’. Societal expectations increasingly demand not only projects that ‘pass’ the review criteria of regulators, financiers and the community, but IA processes capable of delivering sustainable outcomes that include learning and sharing of knowledge. It is proposed that learning be treated as a purposeful – not as an accidental – outcome of IA, and facilitated by adopting a ‘learning organization approach’ coupled with best practice such as early stakeholder engagement. - Highlights: • Proponents are challenged to develop projects that deliver sustainable outcomes. • Passing the test of government approval may be insufficient to obtain a social license. • Learning by all stakeholders is vital to meet these challenges. • Learning outcomes have to go beyond instrumental learning to reach new behaviors, norms and values. • A “learning organization approach” can promote mutual learning and improve project design.

Conceptualizing impact assessment as a learning process

Energy Technology Data Exchange (ETDEWEB)

Sánchez, Luis E., E-mail: lsanchez@usp.br [Escola Politécnica, University of São Paulo, Av. Prof. Mello Moraes, 2373, 05508-900 São Paulo (Brazil); Mitchell, Ross, E-mail: ross.mitchell@ualberta.net [Shell International Exploration & Production BV (Netherlands)

2017-01-15

This paper explores how project developers and their consultants, government regulators and stakeholders can learn from the impact assessment (IA) process, thus potentially improving its effectiveness and enhancing project sustainability. Despite the benefits that learning can bring to an organization, failure to learn appears commonplace both within the IA process and, once approved, subsequent industrial development. To nurture organizational learning through IA, enabling structures that foster information sharing and interpretation and enhance organizational memory are needed. In this paper learning outcomes are grouped into three categories: acquisition of knowledge and skills, developing new behaviors and developing sustainability-oriented norms and values. Means to achieve such outcomes include education and training, experiential learning, learning through public participation (social learning) and a ‘learning organization approach’. Societal expectations increasingly demand not only projects that ‘pass’ the review criteria of regulators, financiers and the community, but IA processes capable of delivering sustainable outcomes that include learning and sharing of knowledge. It is proposed that learning be treated as a purposeful – not as an accidental – outcome of IA, and facilitated by adopting a ‘learning organization approach’ coupled with best practice such as early stakeholder engagement. - Highlights: • Proponents are challenged to develop projects that deliver sustainable outcomes. • Passing the test of government approval may be insufficient to obtain a social license. • Learning by all stakeholders is vital to meet these challenges. • Learning outcomes have to go beyond instrumental learning to reach new behaviors, norms and values. • A “learning organization approach” can promote mutual learning and improve project design.

Muscle cocontraction following dynamics learning.

Science.gov (United States)

Darainy, Mohammad; Ostry, David J

2008-09-01

Coactivation of antagonist muscles is readily observed early in motor learning, in interactions with unstable mechanical environments and in motor system pathologies. Here we present evidence that the nervous system uses coactivation control far more extensively and that patterns of cocontraction during movement are closely tied to the specific requirements of the task. We have examined the changes in cocontraction that follow dynamics learning in tasks that are thought to involve finely sculpted feedforward adjustments to motor commands. We find that, even following substantial training, cocontraction varies in a systematic way that depends on both movement direction and the strength of the external load. The proportion of total activity that is due to cocontraction nevertheless remains remarkably constant. Moreover, long after indices of motor learning and electromyographic measures have reached asymptotic levels, cocontraction still accounts for a significant proportion of total muscle activity in all phases of movement and in all load conditions. These results show that even following dynamics learning in predictable and stable environments, cocontraction forms a central part of the means by which the nervous system regulates movement.

Computational advantages of reverberating loops for sensorimotor learning.

Science.gov (United States)

Fortney, Kristen; Tweed, Douglas B

2012-03-01

When we learn something new, our brain may store the information in synapses or in reverberating loops of electrical activity, but current theories of motor learning focus almost entirely on the synapses. Here we show that loops could also play a role and would bring advantages: loop-based algorithms can learn complex control tasks faster, with exponentially fewer neurons, and avoid the problem of weight transport. They do all this at a cost: in the presence of long feedback delays, loop algorithms cannot control very fast movements, but in this case, loop and synaptic mechanisms can complement each other-mixed systems quickly learn to make accurate but not very fast motions and then gradually speed up. Loop algorithms explain aspects of consolidation, the role of attention, and the relapses that are sometimes seen after a task has apparently been learned, and they make further predictions.

Valence of Facial Cues Influences Sheep Learning in a Visual Discrimination Task

Directory of Open Access Journals (Sweden)

Lucille G. A. Bellegarde

2017-11-01

Full Text Available Sheep are one of the most studied farm species in terms of their ability to process information from faces, but little is known about their face-based emotion recognition abilities. We investigated (a whether sheep could use images of sheep faces taken in situation of varying valence as cues in a simultaneous discrimination task and (b whether the valence of the situation affects their learning performance. To accomplish this, we photographed faces of sheep in three situations inducing emotional states of neutral (ruminating in the home pen or negative valence (social isolation or aggressive interaction. Sheep (n = 35 first had to learn a discrimination task with colored cards. Animals that reached the learning criterion (n = 16 were then presented with pairs of images of the face of a single individual taken in the neutral situation and in one of the negative situations. Finally, sheep had to generalize what they had learned to new pairs of images of faces taken in the same situation, but of a different conspecific. All sheep that learned the discrimination task with colored cards reached the learning criterion with images of faces. Sheep that had to associate a negative image with a food reward learned faster than sheep that had to associate a neutral image with a reward. With the exception of sheep from the aggression-rewarded group, sheep generalized this discrimination to images of faces of different individuals. Our results suggest that sheep can perceive the emotional valence displayed on faces of conspecifics and that this valence affects learning processes.

Deep learning classification in asteroseismology

Science.gov (United States)

Hon, Marc; Stello, Dennis; Yu, Jie

2017-08-01

In the power spectra of oscillating red giants, there are visually distinct features defining stars ascending the red giant branch from those that have commenced helium core burning. We train a 1D convolutional neural network by supervised learning to automatically learn these visual features from images of folded oscillation spectra. By training and testing on Kepler red giants, we achieve an accuracy of up to 99 per cent in separating helium-burning red giants from those ascending the red giant branch. The convolutional neural network additionally shows capability in accurately predicting the evolutionary states of 5379 previously unclassified Kepler red giants, by which we now have greatly increased the number of classified stars.

Machine learning molecular dynamics for the simulation of infrared spectra.

Science.gov (United States)

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Perceptual learning modifies the functional specializations of visual cortical areas.

Science.gov (United States)

Chen, Nihong; Cai, Peng; Zhou, Tiangang; Thompson, Benjamin; Fang, Fang

2016-05-17

Training can improve performance of perceptual tasks. This phenomenon, known as perceptual learning, is strongest for the trained task and stimulus, leading to a widely accepted assumption that the associated neuronal plasticity is restricted to brain circuits that mediate performance of the trained task. Nevertheless, learning does transfer to other tasks and stimuli, implying the presence of more widespread plasticity. Here, we trained human subjects to discriminate the direction of coherent motion stimuli. The behavioral learning effect substantially transferred to noisy motion stimuli. We used transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms underlying the transfer of learning. The TMS experiment revealed dissociable, causal contributions of V3A (one of the visual areas in the extrastriate visual cortex) and MT+ (middle temporal/medial superior temporal cortex) to coherent and noisy motion processing. Surprisingly, the contribution of MT+ to noisy motion processing was replaced by V3A after perceptual training. The fMRI experiment complemented and corroborated the TMS finding. Multivariate pattern analysis showed that, before training, among visual cortical areas, coherent and noisy motion was decoded most accurately in V3A and MT+, respectively. After training, both kinds of motion were decoded most accurately in V3A. Our findings demonstrate that the effects of perceptual learning extend far beyond the retuning of specific neural populations for the trained stimuli. Learning could dramatically modify the inherent functional specializations of visual cortical areas and dynamically reweight their contributions to perceptual decisions based on their representational qualities. These neural changes might serve as the neural substrate for the transfer of perceptual learning.

Machine learning-based dual-energy CT parametric mapping.

Science.gov (United States)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-05-22

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z_eff), relative electron density (ρ_e), mean excitation energy (I_x), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 seconds. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. . © 2018 Institute of Physics and Engineering in

Reaching Soldiers with Untreated Substance Use Disorder: Lessons Learned in the Development of a Marketing Campaign for the Warrior Check-Up Study

Science.gov (United States)

Walton, Thomas O.; Walker, Denise D.; Kaysen, Debra L.; Roffman, Roger A.; Mbilinyi, Lyungai; Neighbors, Clayton

2016-01-01

The Warrior Check-Up, a confidential telephone-delivered intervention, is designed to reach active-duty soldiers with untreated substance-use disorder at a large US military base. This paper describes the development and successful implementation of the study’s marketing strategies at the recruitment period’s midpoint (2010–2012). Qualitative analyses of focus groups (n = 26) and survey responses (n = 278) describe the process of campaign design. Measures of demographics, media exposure, post-traumatic stress, anxiety and depression gathered from callers (n = 172) are used in quantitative analysis assessing the campaign’s success in reaching this population. Implications, limitations, and suggestions for future research are discussed. Department of Defense provided study funding. PMID:23869462

Attentional Bias in Human Category Learning: The Case of Deep Learning.

Science.gov (United States)

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Attentional Bias in Human Category Learning: The Case of Deep Learning

Directory of Open Access Journals (Sweden)

Catherine Hanson

2018-04-01

structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error is reached resulting in rapid asymptotic learning.

Efficient Software Assets for Fostering Learning in Applied Games

NARCIS (Netherlands)

Maurer, Matthias; Nussbaumer, Alexander; Steiner, Christina; Van der Vegt, Wim; Nadolski, Rob; Nyamsuren, Enkhbold; Albert, Dietrich

2018-01-01

Digital game technologies are a promising way to enable training providers to reach other target groups, namely those who are not interested in traditional learning technologies. Theoretically, through using digital game technologies we are able to foster the acquisition of any competence by

The Management of eLearning at University of KKU, Abha

Directory of Open Access Journals (Sweden)

Fatimah Al-Saif

2013-03-01

Full Text Available e-Learning, of late, has been witnessing an unprecedented expansion as an opportunity for higher education. This expanding alternative mode calls for ensuring and imparting a sound and qualitative education. So the present case study made an attempt to discuss key aspects of a quality management model for eLearning currently operating at the University of KKU and illustrates the issues related to the quality dimensions of e-learning. Some of these dimensions are: learning process, administrative processes, teaching materials, resources and SWOT (Strengths, Weakness, Opportunities, Threats analysis etc. This study reiterates the relevance of imparting qualitative education through e-learning for quality improvement in ways that facilitate how staff are empowered and supported to develop meaningful eLearning resources for students, how quality improvement is managed, and how organizational learning takes place. The findings of the study further demonstrate that if the concept of e-learning is imparted with a better approach and perspective, the reach will be phenomenal.

Machine learning to analyze images of shocked materials for precise and accurate measurements

Energy Technology Data Exchange (ETDEWEB)

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.; Meehan, B. T.; Ramos, Kyle J.; Bolme, Cindy A.; Sandberg, Richard L.; Nelson, Keith A.

2017-09-14

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

Application of parsimonious learning feedforward control to mechatronic systems

NARCIS (Netherlands)

de Vries, Theodorus J.A.; Velthuis, W.J.R.; Idema, L.J.

2001-01-01

For motion control, learning feedforward controllers (LFFCs) should be applied when accurate process modelling is difficult. When controlling such processes with LFFCs in the form of multidimensional B-spline networks, large network sizes and a poor generalising ability may result, known as the

A Comparative Analysis of Machine Learning Techniques for Credit Scoring

OpenAIRE

Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

2012-01-01

Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

Peningkatan Prestasi Belajar Pendidikan Agama Islam Melalui Penerapan Card Sort Learning

Directory of Open Access Journals (Sweden)

Nur Fadilah

2017-11-01

Full Text Available Appropriate learning methods should be applied in order to maximize the students’ ability during learning activities. The purpose of this study is to determine the improvement of learning achievement of Islamic Religious Education (PAI through the application of card sort learning method. Action study conducted on PAI learning. The material of this learning is to  understand the provision of sholat of fourth graders of Gunungsari State Elementary School 2 Kaliori Sub district Rembang District Lesson Year 2015/2016. The indicator of successful learning in this research is 75%. The results showed that the percentage of learning mastery at the pre cycle stage was 10.7%, 67.9% in the first cycle, and in the second cycle reached 92.9%. The average score of students' test results also increased significantly, ie the pre cycle stage was 58.8, the first cycle was 72.4, and in the second cycle reached 78.9. This means, through the implementation of card sort learning methode can improve student learning achievement on PAI learning material understanding the provision of sholat.  lAbstrak Metode pembelajaran yang tepat harus diterapkan untuk memaksimalkan kemampuan siswa selama kegiatan pembelajaran. Penelitian ini bertujuan untuk mengetahui peningkatan prestasi belajar Pendidikan Agama Islam (PAI melalui penerapan metode card sort. Studi tindakan (action research dilakukan pada pembelajaran PAI materi mengenai rukun sholat siswa kelas IV Sekolah Dasar Negeri Gunungsari 2 Kecamatan Kaliori Kabupaten Rembang Tahun Pelajaran 2015/2016. Indikator eHasil penelitian menunjukkan bahwa persentase ketuntasan belajar pada tahap pra siklus sebesar 10,7%, pada siklus I sebesar 67,9%, dan pada siklus II mencapai 92,9%. Nilai rata-rata hasil tes siswa juga mengalami peningkatan yang signifikan, yaitu para tahap pra siklus sebesar 58,8, siklus I sebesar 72,4, dan pada siklus II naik menjadi 78,9. Hal ini berarti, melalui penerapan card sort learning dapat

Climate Resilience: Outreach and Engagement with Hard to Reach Communities

Science.gov (United States)

Baja, K.

2017-12-01

Baltimore faces a unique combination of shocks and stresses that cut across social, economic, and environmental sectors. Like many postindustrial cities, Baltimore has experienced a decline in its population - resulting in a lower tax base. These trends have had deleterious effects on the city's ability to attend to much needed infrastructure improvements and human services. Furthermore, Baltimore has an unfortunate history of deliberate racial segregation that is directly responsible for many of the economic and social challenges the City faces today. In addition to considerable social and economic issues, the city is already experiencing negative impacts from climate change. Baltimore is vulnerable to many natural hazards including heavy precipitation, sea level rise, storm surge, and extreme heat. Impacts from hazards and the capacity to adapt to them is not equal across all populations. Low-income residents and communities of color are most vulnerable and lack access to the resources to effectively plan, react and recover. They are also less likely to engage in government processes or input sessions, either due to distrust or ineffective outreach efforts by government employees and partners. This session is focused on sharing best practices and lessons learned from Baltimore's approach to community outreach and engagement as well as its focus on power shifting and relationship building with hard-to-reach communities. Reducing neighborhood vulnerability and strengthening the fabric that holds systems together requires a large number of diverse stakeholders coordinated around resiliency efforts. With the history of deliberate segregation and current disparities it remains critical to build trust, shift power from government to residents, and focus on relationship building. Baltimore City utilized this approach in planning, implementation and evaluation of resiliency work. This session will highlight two plan development processes, several projects, and innovative

Proprioceptive body illusions modulate the visual perception of reaching distance.

Directory of Open Access Journals (Sweden)

Agustin Petroni

Full Text Available The neurobiology of reaching has been extensively studied in human and non-human primates. However, the mechanisms that allow a subject to decide-without engaging in explicit action-whether an object is reachable are not fully understood. Some studies conclude that decisions near the reach limit depend on motor simulations of the reaching movement. Others have shown that the body schema plays a role in explicit and implicit distance estimation, especially after motor practice with a tool. In this study we evaluate the causal role of multisensory body representations in the perception of reachable space. We reasoned that if body schema is used to estimate reach, an illusion of the finger size induced by proprioceptive stimulation should propagate to the perception of reaching distances. To test this hypothesis we induced a proprioceptive illusion of extension or shrinkage of the right index finger while participants judged a series of LEDs as reachable or non-reachable without actual movement. Our results show that reach distance estimation depends on the illusory perceived size of the finger: illusory elongation produced a shift of reaching distance away from the body whereas illusory shrinkage produced the opposite effect. Combining these results with previous findings, we suggest that deciding if a target is reachable requires an integration of body inputs in high order multisensory parietal areas that engage in movement simulations through connections with frontal premotor areas.

Accurate Fall Detection in a Top View Privacy Preserving Configuration.

Science.gov (United States)

Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio

2018-05-29

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

Active learning for noisy oracle via density power divergence.

Science.gov (United States)

Sogawa, Yasuhiro; Ueno, Tsuyoshi; Kawahara, Yoshinobu; Washio, Takashi

2013-10-01

The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as β-divergence and γ-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

REACH-related substitution within the Danish printing industry

DEFF Research Database (Denmark)

Larsen, Henrik Fred; Bøg, Carsten; Markussen, Helene

are running a substitution project. A major part of the work has been mapping the presence of chemicals which are potential candidates for substitution (e.g. PBT, CMR, vPvB, EDS) within the Danish printing industry. The mapping comprises a combination of a literature study and an investigation of the actual......The accomplishment of the EU REACH regulation will most probably promote substitution within sectors handling a lot of different chemicals like the printing industry. With the aim of being at the cutting edge of this development the Danish EPA together with the Danish printing industry and IPU...... fulfil one or more of the criteria (e.g. CMR, EDS) for the REACH Annex XIV candidate list (authorisation). The paper presents the results of the mapping of chemical candidates and the first results of the actual substitutions. Keywords: REACH, chemicals, substitution, printing industry....

Less is more: regularization perspectives on large scale machine learning

CERN Multimedia

CERN. Geneva

2017-01-01

Deep learning based techniques provide a possible solution at the expanse of theoretical guidance and, especially, of computational requirements. It is then a key challenge for large scale machine learning to devise approaches guaranteed to be accurate and yet computationally efficient. In this talk, we will consider a regularization perspectives on machine learning appealing to classical ideas in linear algebra and inverse problems to scale-up dramatically nonparametric methods such as kernel methods, often dismissed because of prohibitive costs. Our analysis derives optimal theoretical guarantees while providing experimental results at par or out-performing state of the art approaches.

A Learning Method for Neural Networks Based on a Pseudoinverse Technique

Directory of Open Access Journals (Sweden)

Chinmoy Pal

1996-01-01

Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

LEARNING CURVE IN SINGLE-LEVEL MINIMALLY INVASIVE TLIF: EXPERIENCE OF A NEUROSURGEON

Directory of Open Access Journals (Sweden)

Samuel Romano-Feinholz

Full Text Available ABSTRACT Objective: To describe the learning curve that shows the progress of a single neurosurgeon when performing single-level MI-TLIF. Methods: We included 99 consecutive patients who underwent single-level MI-TLIF by the same neurosurgeon (JASS. Patient’s demographic characteristics were analyzed. In addition, surgical time, intraoperative blood loss and hospital stay were evaluated. The learning curves were calculated with a piecewise regression model. Results: The mean age was 54.6 years. The learning curves showed an inverse relationship between the surgical experience and the variable analyzed, reaching an inflection point for surgical time in case 43 and for blood loss in case 48. The mean surgical time was 203.3 minutes (interquartile range [IQR] 150-240 minutes, intraoperative bleeding was 97.4ml (IQR 40-100ml and hospital stay of four days (IQR 3-5 days. Conclusions: MI-TLIF is a very frequent surgical procedure due to its effectiveness and safety, which has shown similar results to open procedure. According to this study, the required learning curve is slightly higher than for open procedures, and is reached after about 45 cases.

Development of a machine learning potential for graphene

Science.gov (United States)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Geophysics field school: A team-based learning experience for students and faculty

Science.gov (United States)

Karchewski, B.; Innanen, K. A.; Lauer, R. M.; Pidlisecky, A.

2016-12-01

The core challenge facing a modern science educator is to deliver a curriculum that reaches broadly and deeply into the technical domain, while also helping students to develop fundamental scientific skills such as inquiry, critical thinking and technical communication. That is, our aim is for students to achieve significant learning at all levels summarized by Bloom's Taxonomy of Educational Objectives. It is not always clear how to achieve the full spectrum of goals, with much debate over which component is more important in a science education. Team-based and experiential learning are research-supported approaches that aim to reach across the spectrum by placing students in a setting where they solve practical problems in teams of peers. This learning mode modifies the role of the instructor to a guide or facilitator, and students take a leadership role in their own education. We present a case study of our team's implementation of team-based learning in a geophysics field school, an inherently experiential learning environment. The core philosophies behind our implementation are to present clearly defined learning outcomes, to recognize that students differ in their learning modalities and to strive to engage students through a range of evidence-based learning experiences. We discuss the techniques employed to create functional teams, the key learning activities involved in a typical day of field school and data demonstrating the learning activities that showed the strongest correlation to overall performance in the course. In the process, we also realized that our team-based approach to course design and implementation also enhanced our skillsets as educators, and our institution recently recognized our efforts with a team teaching award. Therefore, we conclude with some of our observations of best practices for team teaching in a field setting to initiate discussions with colleagues engaged in similar activities.

Building machines that learn and think like people.

Science.gov (United States)

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Bypassing the Kohn-Sham equations with machine learning.

Science.gov (United States)

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Machine learning in the string landscape

Science.gov (United States)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

Cerebellar motor learning: when is cortical plasticity not enough?

Directory of Open Access Journals (Sweden)

John Porrill

2007-10-01

Full Text Available Classical Marr-Albus theories of cerebellar learning employ only cortical sites of plasticity. However, tests of these theories using adaptive calibration of the vestibulo-ocular reflex (VOR have indicated plasticity in both cerebellar cortex and the brainstem. To resolve this long-standing conflict, we attempted to identify the computational role of the brainstem site, by using an adaptive filter version of the cerebellar microcircuit to model VOR calibration for changes in the oculomotor plant. With only cortical plasticity, introducing a realistic delay in the retinal-slip error signal of 100 ms prevented learning at frequencies higher than 2.5 Hz, although the VOR itself is accurate up to at least 25 Hz. However, the introduction of an additional brainstem site of plasticity, driven by the correlation between cerebellar and vestibular inputs, overcame the 2.5 Hz limitation and allowed learning of accurate high-frequency gains. This "cortex-first" learning mechanism is consistent with a wide variety of evidence concerning the role of the flocculus in VOR calibration, and complements rather than replaces the previously proposed "brainstem-first" mechanism that operates when ocular tracking mechanisms are effective. These results (i describe a process whereby information originally learnt in one area of the brain (cerebellar cortex can be transferred and expressed in another (brainstem, and (ii indicate for the first time why a brainstem site of plasticity is actually required by Marr-Albus type models when high-frequency gains must be learned in the presence of error delay.

Math’s teaching through cooperative learning in year-2 Primary Education

Directory of Open Access Journals (Sweden)

Jesús Iglesias Muñiz

2017-05-01

Full Text Available The goal of this study was to assess the effects of cooperative learning as a methodological tool for maths teaching. A quasi-experimental design with non-equivalent groups of students was used. A total of 33 students belonging to two year-2 Primary Education intact classes agreed to participate. One experienced a cooperative learning approach, while the other one experienced a traditional approach. Assessment was performed quantitatively through a maths’ skills test and qualitatively through children’s drawings. Quantitative results showed that the cooperative learning group reached higher math scores, while from the qualitative results emerged 3 positive categories: enjoyment, learning, group work and 3 negative: boredom/tiredness, difficult and bad behaviour. Cooperative learning seems to debilitate students’ negative perceptions on the math class.

Comparison of technology-based cooperative learning with technology-based individual learning in enhancing fundamental nursing proficiency.

Science.gov (United States)

Lin, Zu-Chun

2013-05-01

The aim of nursing education is to prepare students with critical thinking, high interests in profession and high proficiency in patient care. Cooperative learning promotes team work and encourages knowledge building upon discussion. It has been viewed as one of the most powerful learning methods. Technology has been considered an influential tool in teaching and learning. It assists students in gathering more information to solve the problems and master skills better. The purpose of this study was to compare the effect of technology-based cooperative learning with technology-based individual learning in nursing students' critical thinking in catheterization knowledge gaining, error discovering, skill acquisitions, and overall scores. This study used a pretest-posttest experimental design. Ninety-eight students were assigned randomly to one of two groups. Questionnaires and tests were collected at baseline and after completion of intervention. The results of this study showed that there was no significant difference in related catheterization skill performance. However, the remaining variables differed greatly between the two groups. CONCLUSIONS AND APPLICATIONS: This study's findings guide the researchers and instructors to use technology-based cooperative learning more appropriately. Future research should address the design of the course module and the availability of mobile devices to reach student-centered and learn on the move goals. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Comparison of Undergraduate Students' English Vocabulary Learning: Using Mobile Phones and Flash Cards

Science.gov (United States)

Basoglu, Emrah Baki; Akdemir, Omur

2010-01-01

Knowing a foreign language has become crucial to reach information. Learning vocabulary is the fundamental step to learn a foreign language. New devices are invented everyday to fulfill the needs of citizens of the twenty-first century. Increased use of mobile phones has made them popular for not only communication, but also entertainment and…

Environmental stressors afflicting tailwater stream reaches across the United States

Science.gov (United States)

Miranda, Leandro E.; Krogman, R. M.

2014-01-01

The tailwater is the reach of a stream immediately below an impoundment that is hydrologically, physicochemically and biologically altered by the presence and operation of a dam. The overall goal of this study was to gain a nationwide awareness of the issues afflicting tailwater reaches in the United States. Specific objectives included the following: (i) estimate the percentage of reservoirs that support tailwater reaches with environmental conditions suitable for fish assemblages throughout the year, (ii) identify and quantify major sources of environmental stress in those tailwaters that do support fish assemblages and (iii) identify environmental features of tailwater reaches that determine prevalence of key fish taxa. Data were collected through an online survey of fishery managers. Relative to objective 1, 42% of the 1306 reservoirs included in this study had tailwater reaches with sufficient flow to support a fish assemblage throughout the year. The surface area of the reservoir and catchment most strongly delineated reservoirs maintaining tailwater reaches with or without sufficient flow to support a fish assemblage throughout the year. Relative to objective 2, major sources of environmental stress generally reflected flow variables, followed by water quality variables. Relative to objective 3, zoogeography was the primary factor discriminating fish taxa in tailwaters, followed by a wide range of flow and water quality variables. Results for objectives 1–3 varied greatly among nine geographic regions distributed throughout the continental United States. Our results provide a large-scale view of the effects of reservoirs on tailwater reaches and may help guide research and management needs.

Flow Navigation by Smart Microswimmers via Reinforcement Learning

Science.gov (United States)

Colabrese, Simona; Biferale, Luca; Celani, Antonio; Gustavsson, Kristian

2017-11-01

We have numerically modeled active particles which are able to acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. We show that those swimmers can learn effective strategies just by experience, using a reinforcement learning algorithm. As an example, we focus on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, exploiting the underlying flow whenever possible. The reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This work paves the way towards the engineering of smart microswimmers that solve difficult navigation problems. ERC AdG NewTURB 339032.

Self-Access Language Learning Programme: The Case of the English Language Voluntary Intensive Independent Catch-up Study

Directory of Open Access Journals (Sweden)

Salomi Papadima-Sophocleous

2013-06-01

Full Text Available This study investigated whether and to what extent an English Language Voluntary Intensive Independent Catch-up Study (ELVIICS, a Self-Access Language Learning (SALL programme, was effective in helping first-year Greek-Cypriot students fill in the gaps in their English language learning and come closer to the required language competence level of the Common European Framework of Reference (CEFR B1 level. It also examined students’ perceptions of such learning. The students followed the ELVIICS at their own pace, time and space until they felt they had reached the aimed level. Analysis of the achievement test results revealed that students’ language competence improved and reached the required level. Additional quantitative data also revealed that students felt ELVIICS also helped them improve their self-confidence, computer skills and autonomous learning. Moreover, students claimed that ELVIICS assisted them in getting through and successfully completing their compulsory course.

Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.

Science.gov (United States)

Levin, Scott; Toerper, Matthew; Hamrock, Eric; Hinson, Jeremiah S; Barnes, Sean; Gardner, Heather; Dugas, Andrea; Linton, Bob; Kirsch, Tom; Kelen, Gabor

2018-05-01

Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation. A multisite, retrospective, cross-sectional study of 172,726 ED visits from urban and community EDs was conducted. E-triage is composed of a random forest model applied to triage data (vital signs, chief complaint, and active medical history) that predicts the need for critical care, an emergency procedure, and inpatient hospitalization in parallel and translates risk to triage level designations. Predicted outcomes and secondary outcomes of elevated troponin and lactate levels were evaluated and compared with the Emergency Severity Index (ESI). E-triage predictions had an area under the curve ranging from 0.73 to 0.92 and demonstrated equivalent or improved identification of clinical patient outcomes compared with ESI at both EDs. E-triage provided rationale for risk-based differentiation of the more than 65% of ED visits triaged to ESI level 3. Matching the ESI patient distribution for comparisons, e-triage identified more than 10% (14,326 patients) of ESI level 3 patients requiring up triage who had substantially increased risk of critical care or emergency procedure (1.7% ESI level 3 versus 6.2% up triaged) and hospitalization (18.9% versus 45.4%) across EDs. E-triage more accurately classifies ESI level 3 patients and highlights opportunities to use predictive analytics to support triage decisionmaking. Further prospective validation is needed. Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Scientific governance and the process for exposure scenario development in REACH

NARCIS (Netherlands)

Money, C.D.; Hemmen, J.J. van; Vermeire, T.G.

2007-01-01

The primary process established by the European Commission to address the science needed to define key REACH concepts and to help rationally implement REACH's ambitions is enshrined in a series of activities known as the REACH Implementation Projects (RIPs). These are projects that aim to define the

SupportNet: a novel incremental learning framework through deep learning and support data

KAUST Repository

Li, Yu; Li, Zhongxiao; Ding, Lizhong; Hu, Yuhui; Chen, Wei; Gao, Xin

2018-01-01

Motivation: In most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems. Results: Here we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data. Availability: Our program is accessible at: \\url{https://github.com/lykaust15/SupportNet}.

SupportNet: a novel incremental learning framework through deep learning and support data

KAUST Repository

Li, Yu

2018-05-08

Motivation: In most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems. Results: Here we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data. Availability: Our program is accessible at: \\\\url{https://github.com/lykaust15/SupportNet}.

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

Science.gov (United States)

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

2017-03-01

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

Skill learning from kinesthetic feedback.

Science.gov (United States)

Pinzon, David; Vega, Roberto; Sanchez, Yerly Paola; Zheng, Bin

2017-10-01

It is important for a surgeon to perform surgical tasks under appropriate guidance from visual and kinesthetic feedback. However, our knowledge on kinesthetic (muscle) memory and its role in learning motor skills remains elementary. To discover the effect of exclusive kinesthetic training on kinesthetic memory in both performance and learning. In Phase 1, a total of twenty participants duplicated five 2 dimensional movements of increasing complexity via passive kinesthetic guidance, without visual or auditory stimuli. Five participants were asked to repeat the task in the Phase 2 over a period of three weeks, for a total of nine sessions. Subjects accurately recalled movement direction using kinesthetic memory, but recalling movement length was less precise. Over the nine training sessions, error occurrence dropped after the sixth session. Muscle memory constructs the foundation for kinesthetic training. Knowledge gained helps surgeons learn skills from kinesthetic information in the condition where visual feedback is limited. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning plan applicability through active mental entities

International Nuclear Information System (INIS)

Baroni, Pietro; Fogli, Daniela; Guida, Giovanni

1999-01-01

This paper aims at laying down the foundations of a new approach to learning in autonomous mobile robots. It is based on the assumption that robots can be provided with built-in action plans and with mechanisms to modify and improve such plans. This requires that robots are equipped with some form of high-level reasoning capabilities. Therefore, the proposed learning technique is embedded in a novel distributed control architecture featuring an explicit model of robot's cognitive activity. In particular, cognitive activity is obtained by the interaction of active mental entities, such as intentions, persuasions and expectations. Learning capabilities are implemented starting from the interaction of such mental entities. The proposal is illustrated through an example concerning a robot in charge of reaching a target in an unknown environment cluttered with obstacles

An e-learning Programming Method And It's Implementation Based On Multimedia And Web

International Nuclear Information System (INIS)

Madenda, Sarifuddin; Tommy, F. R.

2001-01-01

New developments in information technology and telecommunication play an important rile in exchanging fast and accurate information which range from text, sound, graphic to video. These technologies seem to be very effective for Distance learning, Virtual University and E-learning. This paper presents an E-learning programming method and it's implementation based on multimedia and Web. An example of the study case corresponds to human organ, where the organ functions are presented as texts and sounds and the activities as graphic and video

Geomorphic Controls on Floodplain Soil Organic Carbon in the Yukon Flats, Interior Alaska, From Reach to River Basin Scales

Science.gov (United States)

Lininger, K. B.; Wohl, E.; Rose, J. R.

2018-03-01

Floodplains accumulate and store organic carbon (OC) and release OC to rivers, but studies of floodplain soil OC come from small rivers or small spatial extents on larger rivers in temperate latitudes. Warming climate is causing substantial change in geomorphic process and OC fluxes in high latitude rivers. We investigate geomorphic controls on floodplain soil OC concentrations in active-layer mineral sediment in the Yukon Flats, interior Alaska. We characterize OC along the Yukon River and four tributaries in relation to geomorphic controls at the river basin, segment, and reach scales. Average OC concentration within floodplain soil is 2.8% (median = 2.2%). Statistical analyses indicate that OC varies among river basins, among planform types along a river depending on the geomorphic unit, and among geomorphic units. OC decreases with sample depth, suggesting that most OC accumulates via autochthonous inputs from floodplain vegetation. Floodplain and river characteristics, such as grain size, soil moisture, planform, migration rate, and riverine DOC concentrations, likely influence differences among rivers. Grain size, soil moisture, and age of surface likely influence differences among geomorphic units. Mean OC concentrations vary more among geomorphic units (wetlands = 5.1% versus bars = 2.0%) than among study rivers (Dall River = 3.8% versus Teedrinjik River = 2.3%), suggesting that reach-scale geomorphic processes more strongly control the spatial distribution of OC than basin-scale processes. Investigating differences at the basin and reach scale is necessary to accurately assess the amount and distribution of floodplain soil OC, as well as the geomorphic controls on OC.

Solar Hydrogen Reaching Maturity

Directory of Open Access Journals (Sweden)

Rongé Jan

2015-09-01

Full Text Available Increasingly vast research efforts are devoted to the development of materials and processes for solar hydrogen production by light-driven dissociation of water into oxygen and hydrogen. Storage of solar energy in chemical bonds resolves the issues associated with the intermittent nature of sunlight, by decoupling energy generation and consumption. This paper investigates recent advances and prospects in solar hydrogen processes that are reaching market readiness. Future energy scenarios involving solar hydrogen are proposed and a case is made for systems producing hydrogen from water vapor present in air, supported by advanced modeling.

A new model with an anatomically accurate human renal collecting system for training in fluoroscopy-guided percutaneous nephrolithotomy access.

Science.gov (United States)

Turney, Benjamin W

2014-03-01

Obtaining renal access is one of the most important and complex steps in learning percutaneous nephrolithotomy (PCNL). Ideally, this skill should be practiced outside the operating room. There is a need for anatomically accurate and cheap models for simulated training. The objective was to develop a cost-effective, anatomically accurate, nonbiologic training model for simulated PCNL access under fluoroscopic guidance. Collecting systems from routine computed tomography urograms were extracted and reformatted using specialized software. These images were printed in a water-soluble plastic on a three-dimensional (3D) printer to create biomodels. These models were embedded in silicone and then the models were dissolved in water to leave a hollow collecting system within a silicone model. These PCNL models were filled with contrast medium and sealed. A layer of dense foam acted as a spacer to replicate the tissues between skin and kidney. 3D printed models of human collecting systems are a useful adjunct in planning PCNL access. The PCNL access training model is relatively low cost and reproduces the anatomy of the renal collecting system faithfully. A range of models reflecting the variety and complexity of human collecting systems can be reproduced. The fluoroscopic triangulation process needed to target the calix of choice can be practiced successfully in this model. This silicone PCNL training model accurately replicates the anatomic architecture and orientation of the human renal collecting system. It provides a safe, clean, and effective model for training in accurate fluoroscopy-guided PCNL access.

The Cognition of Maximal Reach Distance in Parkinson’s Disease

Directory of Open Access Journals (Sweden)

Satoru Otsuki

2016-01-01

Full Text Available This study aimed to investigate whether the cognition of spatial distance in reaching movements was decreased in patients with Parkinson’s disease (PD and whether this cognition was associated with various symptoms of PD. Estimated and actual maximal reaching distances were measured in three directions in PD patients and healthy elderly volunteers. Differences between estimated and actual measurements were compared within each group. In the PD patients, the associations between “error in cognition” of reaching distance and “clinical findings” were also examined. The results showed that no differences were observed in any values regardless of dominance of hand and severity of symptoms. The differences between the estimated and actual measurements were negatively deviated in the PD patients, indicating that they tended to underestimate reaching distance. “Error in cognition” of reaching distance correlated with the items of posture in the motor section of the Unified Parkinson’s Disease Rating Scale. This suggests that, in PD patients, postural deviation and postural instability might affect the cognition of the distance from a target object.

The Effect of Technology on Students' Opinions about Authentic Learning Activities in Science Courses

Science.gov (United States)

Coskun, Hilal; Dogan, Alev; Uluay, Gulsah

2017-01-01

Today, most of the researchers have agreed on the importance of classroom environment where students responsible of their own learning. It is important to use modern learning methods with technology to reach this aim in courses. The main purpose of this study is to investigate the effect of using Technology in science courses to investigate 7th…

Children's Visual Processing of Egocentric Cues in Action Planning for Reach

Science.gov (United States)

Cordova, Alberto; Gabbard, Carl

2011-01-01

In this study the authors examined children's ability to code visual information into an egocentric frame of reference for planning reach movements. Children and adults estimated reach distance via motor imagery in immediate and response-delay conditions. Actual maximum reach was compared to estimates in multiple locations in peripersonal and…

The Sense of Confidence during Probabilistic Learning: A Normative Account.

Directory of Open Access Journals (Sweden)

Florent Meyniel

2015-06-01

Full Text Available Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics and at the second level (uncertainty due to unexpected changes in these stochastic characteristics. Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems

Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment.

Science.gov (United States)

Xing, Junliang; Niu, Zhiheng; Huang, Junshi; Hu, Weiming; Zhou, Xi; Yan, Shuicheng

2018-04-01

Face alignment acts as an important task in computer vision. Regression-based methods currently dominate the approach to solving this problem, which generally employ a series of mapping functions from the face appearance to iteratively update the face shape hypothesis. One keypoint here is thus how to perform the regression procedure. In this work, we formulate this regression procedure as a sparse coding problem. We learn two relational dictionaries, one for the face appearance and the other one for the face shape, with coupled reconstruction coefficient to capture their underlying relationships. To deploy this model for face alignment, we derive the relational dictionaries in a stage-wised manner to perform close-loop refinement of themselves, i.e., the face appearance dictionary is first learned from the face shape dictionary and then used to update the face shape hypothesis, and the updated face shape dictionary from the shape hypothesis is in return used to refine the face appearance dictionary. To improve the model accuracy, we extend this model hierarchically from the whole face shape to face part shapes, thus both the global and local view variations of a face are captured. To locate facial landmarks under occlusions, we further introduce an occlusion dictionary into the face appearance dictionary to recover face shape from partially occluded face appearance. The occlusion dictionary is learned in a data driven manner from background images to represent a set of elemental occlusion patterns, a sparse combination of which models various practical partial face occlusions. By integrating all these technical innovations, we obtain a robust and accurate approach to locate facial landmarks under different face views and possibly severe occlusions for face images in the wild. Extensive experimental analyses and evaluations on different benchmark datasets, as well as two new datasets built by ourselves, have demonstrated the robustness and accuracy of our proposed

Machine learning versus knowledge based classification of legal texts

NARCIS (Netherlands)

de Maat, E.; Krabben, K.; Winkels, R.; Winkels, R.G.F.

2010-01-01

This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but

Growth as Product and as Process: Student Learning Outcomes Attained through College Experiences in China

Science.gov (United States)

Cen, Yuhao

2012-01-01

Little empirical research has been done on student learning outcomes and college experiences in China, where the gross enrollment rate in higher education reached 26.5 percent and the undergraduate population exceeded 22 million in 2010. This study seeks to describe, explain, and interpret student learning as perceived from students in Chinese…

Key Design Requirements for Long-Reach Manipulators

Energy Technology Data Exchange (ETDEWEB)

Kwon, D.S.

2001-01-01

Long-reach manipulators differ from industrial robots and teleoperators typically used in the nuclear industry in that the aspect ratio (length to diameter) of links is much greater and link flexibility, as well as joint or drive train flexibility, is likely to be significant. Long-reach manipulators will be required for a variety of applications in the Environmental Restoration and Waste Management Program. While each application will present specific functional, kinematic, and performance requirements, an approach for determining the kinematic applicability and performance characteristics is presented, with a focus on waste storage tank remediation. Requirements are identified, kinematic configurations are considered, and a parametric study of link design parameters and their effects on performance characteristics is presented.

Key design requirements for long-reach manipulators

International Nuclear Information System (INIS)

Kwon, D.S.; March-Leuba, S.; Babcock, S.M.; Hamel, W.R.

1993-09-01

Long-reach manipulators differ from industrial robots and teleoperators typically used in the nuclear industry in that the aspect ratio (length to diameter) of links is much greater and link flexibility, as well as joint or drive train flexibility, is likely to be significant. Long-reach manipulators will be required for a variety of applications in the Environmental Restoration and Waste Management Program. While each application will present specific functional kinematic, and performance requirements an approach for determining the kinematic applicability and performance characteristics is presented, with a focus on waste storage tank remediation. Requirements are identified, kinematic configurations are considered, and a parametric study of link design parameters and their effects on performance characteristics is presented

Key Design Requirements for Long-Reach Manipulators

International Nuclear Information System (INIS)

Kwon, D.S.

2001-01-01

Long-reach manipulators differ from industrial robots and teleoperators typically used in the nuclear industry in that the aspect ratio (length to diameter) of links is much greater and link flexibility, as well as joint or drive train flexibility, is likely to be significant. Long-reach manipulators will be required for a variety of applications in the Environmental Restoration and Waste Management Program. While each application will present specific functional, kinematic, and performance requirements, an approach for determining the kinematic applicability and performance characteristics is presented, with a focus on waste storage tank remediation. Requirements are identified, kinematic configurations are considered, and a parametric study of link design parameters and their effects on performance characteristics is presented

Accurate halo-galaxy mocks from automatic bias estimation and particle mesh gravity solvers

Science.gov (United States)

Vakili, Mohammadjavad; Kitaura, Francisco-Shu; Feng, Yu; Yepes, Gustavo; Zhao, Cheng; Chuang, Chia-Hsun; Hahn, ChangHoon

2017-12-01

Reliable extraction of cosmological information from clustering measurements of galaxy surveys requires estimation of the error covariance matrices of observables. The accuracy of covariance matrices is limited by our ability to generate sufficiently large number of independent mock catalogues that can describe the physics of galaxy clustering across a wide range of scales. Furthermore, galaxy mock catalogues are required to study systematics in galaxy surveys and to test analysis tools. In this investigation, we present a fast and accurate approach for generation of mock catalogues for the upcoming galaxy surveys. Our method relies on low-resolution approximate gravity solvers to simulate the large-scale dark matter field, which we then populate with haloes according to a flexible non-linear and stochastic bias model. In particular, we extend the PATCHY code with an efficient particle mesh algorithm to simulate the dark matter field (the FASTPM code), and with a robust MCMC method relying on the EMCEE code for constraining the parameters of the bias model. Using the haloes in the BigMultiDark high-resolution N-body simulation as a reference catalogue, we demonstrate that our technique can model the bivariate probability distribution function (counts-in-cells), power spectrum and bispectrum of haloes in the reference catalogue. Specifically, we show that the new ingredients permit us to reach percentage accuracy in the power spectrum up to k ∼ 0.4 h Mpc-1 (within 5 per cent up to k ∼ 0.6 h Mpc-1) with accurate bispectra improving previous results based on Lagrangian perturbation theory.

Analysis of the “naming game” with learning errors in communications

OpenAIRE

Yang Lou; Guanrong Chen

2015-01-01

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 ...

Beyond e-learning: from blended methodology to transmedia education

Directory of Open Access Journals (Sweden)

Favrin Valentina

2015-06-01

Full Text Available Nowadays, at the time of convergence culture, social network, and transmedia storytelling – when social interactions are constantly remediated – e-learning, especially in universities, should be conceived as a sharing educational activity. Different learning experiences should become smoother and able to fade out the closed learning environments (as software platform and classrooms (either virtual or not. In this paper, we will show some experiences of the Communication Sciences degree program of the University of Cagliari, which is supplied through an e-learning method. In the ten years since its foundation, the approach has evolved from a blended learning with two kinds of traditional activity (online activities and face-to-face lessons to a much more dynamic learning experience. Many new actors (communication companies, local government, public-service corporations, new media and social media – indeed – have been involved in educational and teaching process. But also these processes changed: collaborative working, new media comprehension, self-guided problem solving are examples of the new literacies and approaches that can be reached as new learning objectives.

Dynamic channel adjustments in the Jingjiang Reach of the Middle Yangtze River

Science.gov (United States)

Xia, Junqiang; Deng, Shanshan; Lu, Jinyou; Xu, Quanxi; Zong, Quanli; Tan, Guangming

2016-03-01

Significant channel adjustments have occurred in the Jingjiang Reach of the Middle Yangtze River, because of the operation of the Three Gorges Project (TGP). The Jingjiang Reach is selected as the study area, covering the Upper Jingjiang Reach (UJR) and Lower Jingjiang Reach (LJR). The reach-scale bankfull channel dimensions in the study reach were calculated annually from 2002 to 2013 by means of a reach-averaged approach and surveyed post-flood profiles at 171 sections. We find from the calculated results that: the reach-scale bankfull widths changed slightly in the UJR and LJR, with the corresponding depths increasing by 1.6 m and 1.0 m the channel adjustments occurred mainly with respect to bankfull depth because of the construction of large-scale bank revetment works, although there were significant bank erosion processes in local regions without the bank protection engineering. The reach-scale bankfull dimensions in the UJR and LJR generally responded to the previous five-year average fluvial erosion intensity during flood seasons, with higher correlations being obtained for the depth and cross-sectional area. It is concluded that these dynamic adjustments of the channel geometry are a direct result of recent human activities such as the TGP operation.

Assessing Complex Learning Objectives through Analytics

Science.gov (United States)

Horodyskyj, L.; Mead, C.; Buxner, S.; Semken, S. C.; Anbar, A. D.

2016-12-01

A significant obstacle to improving the quality of education is the lack of easy-to-use assessments of higher-order thinking. Most existing assessments focus on recall and understanding questions, which demonstrate lower-order thinking. Traditionally, higher-order thinking is assessed with practical tests and written responses, which are time-consuming to analyze and are not easily scalable. Computer-based learning environments offer the possibility of assessing such learning outcomes based on analysis of students' actions within an adaptive learning environment. Our fully online introductory science course, Habitable Worlds, uses an intelligent tutoring system that collects and responds to a range of behavioral data, including actions within the keystone project. This central project is a summative, game-like experience in which students synthesize and apply what they have learned throughout the course to identify and characterize a habitable planet from among hundreds of stars. Student performance is graded based on completion and accuracy, but two additional properties can be utilized to gauge higher-order thinking: (1) how efficient a student is with the virtual currency within the project and (2) how many of the optional milestones a student reached. In the project, students can use the currency to check their work and "unlock" convenience features. High-achieving students spend close to the minimum amount required to reach these goals, indicating a high-level of concept mastery and efficient methodology. Average students spend more, indicating effort, but lower mastery. Low-achieving students were more likely to spend very little, which indicates low effort. Differences on these metrics were statistically significant between all three of these populations. We interpret this as evidence that high-achieving students develop and apply efficient problem-solving skills as compared to lower-achieving student who use more brute-force approaches.

Decentralized indirect methods for learning automata games.

Science.gov (United States)

Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

2011-10-01

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

OpenAIRE

Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla

2012-01-01

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...

Visual-motor association learning in undergraduate students as a function of the autism-spectrum quotient.

Science.gov (United States)

Parkington, Karisa B; Clements, Rebecca J; Landry, Oriane; Chouinard, Philippe A

2015-10-01

We examined how performance on an associative learning task changes in a sample of undergraduate students as a function of their autism-spectrum quotient (AQ) score. The participants, without any prior knowledge of the Japanese language, learned to associate hiragana characters with button responses. In the novel condition, 50 participants learned visual-motor associations without any prior exposure to the stimuli's visual attributes. In the familiar condition, a different set of 50 participants completed a session in which they first became familiar with the stimuli's visual appearance prior to completing the visual-motor association learning task. Participants with higher AQ scores had a clear advantage in the novel condition; the amount of training required reaching learning criterion correlated negatively with AQ. In contrast, participants with lower AQ scores had a clear advantage in the familiar condition; the amount of training required to reach learning criterion correlated positively with AQ. An examination of how each of the AQ subscales correlated with these learning patterns revealed that abilities in visual discrimination-which is known to depend on the visual ventral-stream system-may have afforded an advantage in the novel condition for the participants with the higher AQ scores, whereas abilities in attention switching-which are known to require mechanisms in the prefrontal cortex-may have afforded an advantage in the familiar condition for the participants with the lower AQ scores.

Differential Recruitment of Parietal Cortex during Spatial and Non-spatial Reach Planning

Directory of Open Access Journals (Sweden)

Pierre-Michel Bernier

2017-05-01

Full Text Available The planning of goal-directed arm reaching movements is associated with activity in the dorsal parieto-frontal cortex, within which multiple regions subserve the integration of arm- and target-related sensory signals to encode a motor goal. Surprisingly, many of these regions show sustained activity during reach preparation even when target location is not specified, i.e., when a motor goal cannot be unambiguously formed. The functional role of these non-spatial preparatory signals remains unresolved. Here this process was investigated in humans by comparing reach preparatory activity in the presence or absence of information regarding upcoming target location. In order to isolate the processes specific to reaching and to control for visuospatial attentional factors, the reaching task was contrasted to a finger movement task. Functional MRI and electroencephalography (EEG were used to characterize the spatio-temporal pattern of reach-related activity in the parieto-frontal cortex. Reach planning with advance knowledge of target location induced robust blood oxygenated level dependent and EEG responses across parietal and premotor regions contralateral to the reaching arm. In contrast, reach preparation without knowledge of target location was associated with a significant BOLD response bilaterally in the parietal cortex. Furthermore, EEG alpha- and beta-band activity was restricted to parietal scalp sites, the magnitude of the latter being correlated with reach reaction times. These results suggest an intermediate stage of sensorimotor transformations in bilateral parietal cortex when target location is not specified.

Efficacy of REACH Forgiveness across cultures.

Science.gov (United States)

Lin, Yin; Worthington, Everett L; Griffin, Brandon J; Greer, Chelsea L; Opare-Henaku, Annabella; Lavelock, Caroline R; Hook, Joshua N; Ho, Man Yee; Muller, Holly

2014-09-01

This study investigates the efficacy of the 6-hour REACH Forgiveness intervention among culturally diverse undergraduates. Female undergraduates (N = 102) and foreign extraction (46.2%) and domestic (43.8%) students in the United States were randomly assigned to immediate treatment or waitlist conditions. Treatment efficacy and the effect of culture on treatment response were assessed using measures of emotional and decisional forgiveness across 3 time periods. Students in the treatment condition reported greater improvement in emotional forgiveness, but not decisional forgiveness, relative to those in the waitlist condition. Gains were maintained at a 1-week follow-up. Although culture did not moderate the effect of treatment, a main effect of culture on emotional forgiveness and marginally significant interaction effect of culture on decisional forgiveness were found. The REACH Forgiveness intervention was efficacious for college students from different cultural backgrounds when conducted in the United States. However, some evidence may warrant development of culturally adapted forgiveness interventions. © 2014 Wiley Periodicals, Inc.

Classification of carcinogenic and mutagenic properties using machine learning method

DEFF Research Database (Denmark)

Moorthy, N. S.Hari Narayana; Kumar, Surendra; Poongavanam, Vasanthanathan

2017-01-01

An accurate calculation of carcinogenicity of chemicals became a serious challenge for the health assessment authority around the globe because of not only increased cost for experiments but also various ethical issues exist using animal models. In this study, we provide machine learning...

Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

Science.gov (United States)

Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

2013-01-01

Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

On Knowledge of Learning. A Phenomenological Sketch

Directory of Open Access Journals (Sweden)

Käte Meyer-Drawe

2011-02-01

Full Text Available He word “learning” has the endless meanings. The learning occurs not only from knowledge but also as knowledge. Learning in this sense depends not only on our initiative. We cannot just resolve to learn. The whole reliable order can reach deadlock.. The old reliable knowledge and ability mismatchwhile we do not yet have any new possibility. Learning in this sense means a kind of “awakening”, in statu nascendi as the response to a challenge. Thus the condition of learning is sensitiveness to the other or to something, while it is possible via agreement that is not always disposed to us because of our peculiar attitudes. We always think more than we can express. Inevitably, we can do more than we anticipate. Only after the other takes this surplus the knowledge and the ability correspond to reality before they are realized. In learning we are met by another, to which we answer as to something.. This Something is always outstripping the meanings and aspirations. That is why this learning in true sense begins not in us. The destiny of every experience concerning us depends on this, while we cannot anticipate it exactly. Thus, the sense of in statu nascendi corresponds to the saying modo praeterito. 

Accurate and Simple Calibration of DLP Projector Systems

DEFF Research Database (Denmark)

Wilm, Jakob; Olesen, Oline Vinter; Larsen, Rasmus

2014-01-01

does not rely on an initial camera calibration, and so does not carry over the error into projector calibration. A radial interpolation scheme is used to convert features coordinates into projector space, thereby allowing for a very accurate procedure. This allows for highly accurate determination...

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

Science.gov (United States)

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The