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Sample records for multiple instance learning

  1. Dissimilarity-based multiple instance learning

    DEFF Research Database (Denmark)

    Sørensen, Lauge; Loog, Marco; Tax, David M. J.

    2010-01-01

    In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised cla...... between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results....

  2. Multiple-instance learning as a classifier combining problem

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M. J.; Duin, Robert P. W.

    2013-01-01

    In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the ass...

  3. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  4. Parallel multiple instance learning for extremely large histopathology image analysis.

    Science.gov (United States)

    Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

    2017-08-03

    Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

  5. 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

  6. HyDR-MI : A hybrid algorithm to reduce dimensionality in multiple instance learning

    NARCIS (Netherlands)

    Zafra, A.; Pechenizkiy, M.; Ventura, S.

    2013-01-01

    Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple

  7. Event recognition in personal photo collections via multiple instance learning-based classification of multiple images

    Science.gov (United States)

    Ahmad, Kashif; Conci, Nicola; Boato, Giulia; De Natale, Francesco G. B.

    2017-11-01

    Over the last few years, a rapid growth has been witnessed in the number of digital photos produced per year. This rapid process poses challenges in the organization and management of multimedia collections, and one viable solution consists of arranging the media on the basis of the underlying events. However, album-level annotation and the presence of irrelevant pictures in photo collections make event-based organization of personal photo albums a more challenging task. To tackle these challenges, in contrast to conventional approaches relying on supervised learning, we propose a pipeline for event recognition in personal photo collections relying on a multiple instance-learning (MIL) strategy. MIL is a modified form of supervised learning and fits well for such applications with weakly labeled data. The experimental evaluation of the proposed approach is carried out on two large-scale datasets including a self-collected and a benchmark dataset. On both, our approach significantly outperforms the existing state-of-the-art.

  8. Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

    Science.gov (United States)

    Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

    2016-01-01

    Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

  9. Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

    Science.gov (United States)

    Wang, Shijun; McKenna, Matthew T; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Sahiner, Berkman; Summers, Ronald M

    2012-05-01

    In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

  10. Instance annotation for multi-instance multi-label learning

    Science.gov (United States)

    F. Briggs; X.Z. Fern; R. Raich; Q. Lou

    2013-01-01

    Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior work on MIML has focused on predicting label sets for previously unseen...

  11. Automated detection of age-related macular degeneration in OCT images using multiple instance learning

    Science.gov (United States)

    Sun, Weiwei; Liu, Xiaoming; Yang, Zhou

    2017-07-01

    Age-related Macular Degeneration (AMD) is a kind of macular disease which mostly occurs in old people,and it may cause decreased vision or even lead to permanent blindness. Drusen is an important clinical indicator for AMD which can help doctor diagnose disease and decide the strategy of treatment. Optical Coherence Tomography (OCT) is widely used in the diagnosis of ophthalmic diseases, include AMD. In this paper, we propose a classification method based on Multiple Instance Learning (MIL) to detect AMD. Drusen can exist in a few slices of OCT images, and MIL is utilized in our method. We divided the method into two phases: training phase and testing phase. We train the initial features and clustered to create a codebook, and employ the trained classifier in the test set. Experiment results show that our method achieved high accuracy and effectiveness.

  12. Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

    Science.gov (United States)

    Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

    2018-05-01

    Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Feature selection is the ReliefF for multiple instance learning

    NARCIS (Netherlands)

    Zafra, A.; Pechenizkiy, M.; Ventura, S.

    2010-01-01

    Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In

  14. Multiple-instance learning for computer-aided detection of tuberculosis

    Science.gov (United States)

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

    2014-03-01

    Detection of tuberculosis (TB) on chest radiographs (CXRs) is a hard problem. Therefore, to help radiologists or even take their place when they are not available, computer-aided detection (CAD) systems are being developed. In order to reach a performance comparable to that of human experts, the pattern recognition algorithms of these systems are typically trained on large CXR databases that have been manually annotated to indicate the abnormal lung regions. However, manually outlining those regions constitutes a time-consuming process that, besides, is prone to inconsistencies and errors introduced by interobserver variability and the absence of an external reference standard. In this paper, we investigate an alternative pattern classi cation method, namely multiple-instance learning (MIL), that does not require such detailed information for a CAD system to be trained. We have applied this alternative approach to a CAD system aimed at detecting textural lesions associated with TB. Only the case (or image) condition (normal or abnormal) was provided in the training stage. We compared the resulting performance with those achieved by several variations of a conventional system trained with detailed annotations. A database of 917 CXRs was constructed for experimentation. It was divided into two roughly equal parts that were used as training and test sets. The area under the receiver operating characteristic curve was utilized as a performance measure. Our experiments show that, by applying the investigated MIL approach, comparable results as with the aforementioned conventional systems are obtained in most cases, without requiring condition information at the lesion level.

  15. Gaussian Multiple Instance Learning Approach for Mapping the Slums of the World Using Very High Resolution Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Vatsavai, Raju [ORNL

    2013-01-01

    In this paper, we present a computationally efficient algo- rithm based on multiple instance learning for mapping infor- mal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, infor- mal settlements share unique spatial characteristics that dis- tinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classi- fiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contigu- ous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experi- ments on very high-resolution satellite imagery, represent- ing four unique geographic regions across the world. Our method showed consistent improvement in accurately iden- tifying informal settlements.

  16. Multi-instance learning based on instance consistency for image retrieval

    Science.gov (United States)

    Zhang, Miao; Wu, Zhize; Wan, Shouhong; Yue, Lihua; Yin, Bangjie

    2017-07-01

    Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.

  17. Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

    Directory of Open Access Journals (Sweden)

    Qinli Yang

    2015-03-01

    Full Text Available The ambiguity of diverse functions of sustainable flood retention basins (SFRBs may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty, and the MIML-support vector machine (SVM algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty. Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%. The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

  18. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

    Science.gov (United States)

    Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

    2017-06-01

    Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

  19. An instance theory of associative learning.

    Science.gov (United States)

    Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

    2012-03-01

    We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

  20. Learning concept mappings from instance similarity

    NARCIS (Netherlands)

    Wang, S.; Englebienne, G.; Schlobach, S.

    2008-01-01

    Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods

  1. Automatic Knowledge Base Evolution by Learning Instances

    OpenAIRE

    Kim, Sundong

    2016-01-01

    Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose ful...

  2. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan

    2016-12-29

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  3. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin

    2016-01-01

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  4. Automated gastric cancer diagnosis on H&E-stained sections; ltraining a classifier on a large scale with multiple instance machine learning

    Science.gov (United States)

    Cosatto, Eric; Laquerre, Pierre-Francois; Malon, Christopher; Graf, Hans-Peter; Saito, Akira; Kiyuna, Tomoharu; Marugame, Atsushi; Kamijo, Ken'ichi

    2013-03-01

    We present a system that detects cancer on slides of gastric tissue sections stained with hematoxylin and eosin (H&E). At its heart is a classi er trained using the semi-supervised multi-instance learning framework (MIL) where each tissue is represented by a set of regions-of-interest (ROI) and a single label. Such labels are readily obtained because pathologists diagnose each tissue independently as part of the normal clinical work ow. From a large dataset of over 26K gastric tissue sections from over 12K patients obtained from a clinical load spanning several months, we train a MIL classi er on a patient-level partition of the dataset (2/3 of the patients) and obtain a very high performance of 96% (AUC), tested on the remaining 1/3 never-seen before patients (over 8K tissues). We show this level of performance to match the more costly supervised approach where individual ROIs need to be labeled manually. The large amount of data used to train this system gives us con dence in its robustness and that it can be safely used in a clinical setting. We demonstrate how it can improve the clinical work ow when used for pre-screening or quality control. For pre-screening, the system can diagnose 47% of the tissues with a very low likelihood (cancers, thus halving the clinicians' caseload. For quality control, compared to random rechecking of 33% of the cases, the system achieves a three-fold increase in the likelihood of catching cancers missed by pathologists. The system is currently in regular use at independent pathology labs in Japan where it is used to double-check clinician's diagnoses. At the end of 2012 it will have analyzed over 80,000 slides of gastric and colorectal samples (200,000 tissues).

  5. Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Ying Yin

    2016-05-01

    Full Text Available Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1 the user-specific parameter for the number of clusters may incur the effective problem; (2 SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML-extreme learning machine (ELM, to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

  6. Dual-Layer Density Estimation for Multiple Object Instance Detection

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2016-01-01

    Full Text Available This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC. The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application.

  7. Instance Selection for Classifier Performance Estimation in Meta Learning

    Directory of Open Access Journals (Sweden)

    Marcin Blachnik

    2017-11-01

    Full Text Available Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression level obtained from the instance selection methods at the data-preprocessing stage. To verify their suitability, two types of experiments on real-world datasets have been conducted. In the first one, 11 instance selection methods were examined in order to validate the compression–accuracy relation for three classifiers: k-nearest neighbors (kNN, support vector machine (SVM, and random forest. From this analysis, two methods are recommended (instance-based learning type 2 (IB2, and edited nearest neighbor (ENN which are then compared with the state-of-the-art metaset descriptors. The obtained results confirm that the two suggested compression-based meta-features help to predict accuracy of the base model much more accurately than the state-of-the-art solution.

  8. Image annotation based on positive-negative instances learning

    Science.gov (United States)

    Zhang, Kai; Hu, Jiwei; Liu, Quan; Lou, Ping

    2017-07-01

    Automatic image annotation is now a tough task in computer vision, the main sense of this tech is to deal with managing the massive image on the Internet and assisting intelligent retrieval. This paper designs a new image annotation model based on visual bag of words, using the low level features like color and texture information as well as mid-level feature as SIFT, and mixture the pic2pic, label2pic and label2label correlation to measure the correlation degree of labels and images. We aim to prune the specific features for each single label and formalize the annotation task as a learning process base on Positive-Negative Instances Learning. Experiments are performed using the Corel5K Dataset, and provide a quite promising result when comparing with other existing methods.

  9. Fast generation of multiple resolution instances of raster data sets

    NARCIS (Netherlands)

    Arge, L.; Haverkort, H.J.; Tsirogiannis, C.P.

    2012-01-01

    In many GIS applications it is important to study the characteristics of a raster data set at multiple resolutions. Often this is done by generating several coarser resolution rasters from a fine resolution raster. In this paper we describe efficient algorithms for different variants of this

  10. Fast generation of multiple resolution instances of raster data sets

    DEFF Research Database (Denmark)

    Arge, Lars; Haverkort, Herman; Tsirogiannis, Constantinos

    2012-01-01

    In many GIS applications it is important to study the characteristics of a raster data set at multiple resolutions. Often this is done by generating several coarser resolution rasters from a fine resolution raster. In this paper we describe efficient algorithms for different variants of this prob......In many GIS applications it is important to study the characteristics of a raster data set at multiple resolutions. Often this is done by generating several coarser resolution rasters from a fine resolution raster. In this paper we describe efficient algorithms for different variants...... in the main memory of the computer. We also provide two algorithms that solve this problem in external memory, that is when the input raster is larger than the main memory. The first external algorithm is very easy to implement and requires O(sort(N)) data block transfers from/to the external memory....... For this variant we describe an algorithm that runs in (U logN) time in internal memory, where U is the size of the output. We show how this algorithm can be adapted to perform efficiently in the external memory using O(sort(U)) data transfers from the disk. We have also implemented two of the presented algorithms...

  11. Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison

    OpenAIRE

    Fan, Jihong; Liang, Ru-Ze

    2016-01-01

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stoc...

  12. Multiple Time-Instances Features of Degraded Speech for Single Ended Quality Measurement

    Directory of Open Access Journals (Sweden)

    Rajesh Kumar Dubey

    2017-01-01

    Full Text Available The use of single time-instance features, where entire speech utterance is used for feature computation, is not accurate and adequate in capturing the time localized information of short-time transient distortions and their distinction from plosive sounds of speech, particularly degraded by impulsive noise. Hence, the importance of estimating features at multiple time-instances is sought. In this, only active speech segments of degraded speech are used for features computation at multiple time-instances on per frame basis. Here, active speech means both voiced and unvoiced frames except silence. The features of different combinations of multiple contiguous active speech segments are computed and called multiple time-instances features. The joint GMM training has been done using these features along with the subjective MOS of the corresponding speech utterance to obtain the parameters of GMM. These parameters of GMM and multiple time-instances features of test speech are used to compute the objective MOS values of different combinations of multiple contiguous active speech segments. The overall objective MOS of the test speech utterance is obtained by assigning equal weight to the objective MOS values of the different combinations of multiple contiguous active speech segments. This algorithm outperforms the Recommendation ITU-T P.563 and recently published algorithms.

  13. Learning through Work: Exploring Instances of Relational Interdependencies

    Science.gov (United States)

    Billett, Stephen

    2008-01-01

    This paper provides an account of the inter-psychological processes that constitute learning through work. It does this by drawing on deliberations about the relative contributions of the immediate social world (i.e., workplace setting) that individuals encounter and the personal premises for individuals' learning. This account is realised through…

  14. Instance Selection for Classifier Performance Estimation in Meta Learning

    OpenAIRE

    Marcin Blachnik

    2017-01-01

    Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be inform...

  15. A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video Surveillance

    Directory of Open Access Journals (Sweden)

    MIRONICA, I.

    2015-11-01

    Full Text Available This paper presents an automated surveillance system that exploits the Fisher Kernel representation in the context of multiple-instance object retrieval task. The proposed algorithm has the main purpose of tracking a list of persons in several video sources, using only few training examples. In the first step, the Fisher Kernel representation describes a set of features as the derivative with respect to the log-likelihood of the generative probability distribution that models the feature distribution. Then, we learn the generative probability distribution over all features extracted from a reduced set of relevant frames. The proposed approach shows significant improvements and we demonstrate that Fisher kernels are well suited for this task. We demonstrate the generality of our approach in terms of features by conducting an extensive evaluation with a broad range of keypoints features. Also, we evaluate our method on two standard video surveillance datasets attaining superior results comparing to state-of-the-art object recognition algorithms.

  16. Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

    Science.gov (United States)

    Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

    2017-12-01

    In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

  17. Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

    Science.gov (United States)

    Wagstaff, Kiri L.; Lane, Terran

    2010-01-01

    An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

  18. Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

    Science.gov (United States)

    Bing, Lu; Wang, Wei

    2017-01-01

    We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

  19. The boundaries of instance-based learning theory for explaining decisions from experience.

    Science.gov (United States)

    Gonzalez, Cleotilde

    2013-01-01

    Most demonstrations of how people make decisions in risky situations rely on decisions from description, where outcomes and their probabilities are explicitly stated. But recently, more attention has been given to decisions from experience where people discover these outcomes and probabilities through exploration. More importantly, risky behavior depends on how decisions are made (from description or experience), and although prospect theory explains decisions from description, a comprehensive model of decisions from experience is yet to be found. Instance-based learning theory (IBLT) explains how decisions are made from experience through interactions with dynamic environments (Gonzalez et al., 2003). The theory has shown robust explanations of behavior across multiple tasks and contexts, but it is becoming unclear what the theory is able to explain and what it does not. The goal of this chapter is to start addressing this problem. I will introduce IBLT and a recent cognitive model based on this theory: the IBL model of repeated binary choice; then I will discuss the phenomena that the IBL model explains and those that the model does not. The argument is for the theory's robustness but also for clarity in terms of concrete effects that the theory can or cannot account for. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

    Science.gov (United States)

    Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

    2013-02-01

    Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

  1. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei

    2013-01-01

    as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL

  2. Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

    Directory of Open Access Journals (Sweden)

    Lu Bing

    2017-01-01

    Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

  3. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  4. An anomaly detection and isolation scheme with instance-based learning and sequential analysis

    International Nuclear Information System (INIS)

    Yoo, T. S.; Garcia, H. E.

    2006-01-01

    This paper presents an online anomaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance. (authors)

  5. Landmark-based deep multi-instance learning for brain disease diagnosis.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

    2018-01-01

    In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

    KAUST Repository

    Fan, Jihong

    2016-09-17

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD-based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification. © 2016 The Natural Computing Applications Forum

  7. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  8. Horror Image Recognition Based on Context-Aware Multi-Instance Learning.

    Science.gov (United States)

    Li, Bing; Xiong, Weihua; Wu, Ou; Hu, Weiming; Maybank, Stephen; Yan, Shuicheng

    2015-12-01

    Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.

  9. The role of inertia in modeling decisions from experience with instance-based learning.

    Science.gov (United States)

    Dutt, Varun; Gonzalez, Cleotilde

    2012-01-01

    One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

  10. Cyber situation awareness: modeling detection of cyber attacks with instance-based learning theory.

    Science.gov (United States)

    Dutt, Varun; Ahn, Young-Suk; Gonzalez, Cleotilde

    2013-06-01

    To determine the effects of an adversary's behavior on the defender's accurate and timely detection of network threats. Cyber attacks cause major work disruption. It is important to understand how a defender's behavior (experience and tolerance to threats), as well as adversarial behavior (attack strategy), might impact the detection of threats. In this article, we use cognitive modeling to make predictions regarding these factors. Different model types representing a defender, based on Instance-Based Learning Theory (IBLT), faced different adversarial behaviors. A defender's model was defined by experience of threats: threat-prone (90% threats and 10% nonthreats) and nonthreat-prone (10% threats and 90% nonthreats); and different tolerance levels to threats: risk-averse (model declares a cyber attack after perceiving one threat out of eight total) and risk-seeking (model declares a cyber attack after perceiving seven threats out of eight total). Adversarial behavior is simulated by considering different attack strategies: patient (threats occur late) and impatient (threats occur early). For an impatient strategy, risk-averse models with threat-prone experiences show improved detection compared with risk-seeking models with nonthreat-prone experiences; however, the same is not true for a patient strategy. Based upon model predictions, a defender's prior threat experiences and his or her tolerance to threats are likely to predict detection accuracy; but considering the nature of adversarial behavior is also important. Decision-support tools that consider the role of a defender's experience and tolerance to threats along with the nature of adversarial behavior are likely to improve a defender's overall threat detection.

  11. The effects of single instance, multiple instance, and general case training on generalized vending machine use by moderately and severely handicapped students.

    OpenAIRE

    Sprague, J R; Horner, R H

    1984-01-01

    This report provides an experimental analysis of generalized vending machine use by six moderately or severely retarded high school students. Dependent variables were training trials to criterion and performance on 10 nontrained "generalization" vending machines. A multiple-baseline design across subjects was used to compare three strategies for teaching generalized vending machine use. Training occurred with (a) a single vending machine, (b) three similar machines, or (c) three machines that...

  12. Multiple Instance Fuzzy Inference

    Science.gov (United States)

    2015-12-02

    Fashion models 5.19 14 Sunset scenes 3.52 15 Cars 4.93 16 Waterfalls 2.56 17 Antique furniture 2.30 18 Battle ships 4.32 19 Skiing 3.34 20 Desserts ...1000, in addition 10% of the Desserts category images were confused with Beach and 20.9% of Mountains and glaciers images were misclassified as

  13. Instance-based Policy Learning by Real-coded Genetic Algorithms and Its Application to Control of Nonholonomic Systems

    Science.gov (United States)

    Miyamae, Atsushi; Sakuma, Jun; Ono, Isao; Kobayashi, Shigenobu

    The stabilization control of nonholonomic systems have been extensively studied because it is essential for nonholonomic robot control problems. The difficulty in this problem is that the theoretical derivation of control policy is not necessarily guaranteed achievable. In this paper, we present a reinforcement learning (RL) method with instance-based policy (IBP) representation, in which control policies for this class are optimized with respect to user-defined cost functions. Direct policy search (DPS) is an approach for RL; the policy is represented by parametric models and the model parameters are directly searched by optimization techniques including genetic algorithms (GAs). In IBP representation an instance consists of a state and an action pair; a policy consists of a set of instances. Several DPSs with IBP have been previously proposed. In these methods, sometimes fail to obtain optimal control policies when state-action variables are continuous. In this paper, we present a real-coded GA for DPSs with IBP. Our method is specifically designed for continuous domains. Optimization of IBP has three difficulties; high-dimensionality, epistasis, and multi-modality. Our solution is designed for overcoming these difficulties. The policy search with IBP representation appears to be high-dimensional optimization; however, instances which can improve the fitness are often limited to active instances (instances used for the evaluation). In fact, the number of active instances is small. Therefore, we treat the search problem as a low dimensional problem by restricting search variables only to active instances. It has been commonly known that functions with epistasis can be efficiently optimized with crossovers which satisfy the inheritance of statistics. For efficient search of IBP, we propose extended crossover-like mutation (extended XLM) which generates a new instance around an instance with satisfying the inheritance of statistics. For overcoming multi-modality, we

  14. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Science.gov (United States)

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  15. Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

    KAUST Repository

    Fan, Jihong; Liang, Ru-Ze

    2016-01-01

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However

  16. Chi-squared: A simpler evaluation function for multiple-instance learning

    National Research Council Canada - National Science Library

    McGovern, Amy; Jensen, David

    2003-01-01

    ...) but finds the best concept using the chi-square statistic. This approach is simpler than diverse density and allows us to search more extensively by using properties of the contingency table to prune in a guaranteed manner...

  17. Object instance recognition using motion cues and instance specific appearance models

    Science.gov (United States)

    Schumann, Arne

    2014-03-01

    In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

  18. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

    NARCIS (Netherlands)

    Cheplygina, Veronika; de Bruijne, Marleen; Pluim, Josien P. W.

    2018-01-01

    Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn

  19. Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

    Science.gov (United States)

    Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

    2017-01-01

    Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

  20. Facilitating Multiple Intelligences through Multimodal Learning Analytics

    Science.gov (United States)

    Perveen, Ayesha

    2018-01-01

    This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner's 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as…

  1. Facilitating Multiple Intelligences Through Multimodal Learning Analytics

    Directory of Open Access Journals (Sweden)

    Ayesha PERVEEN

    2018-01-01

    Full Text Available This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner’s 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as multiple intelligences by online education systems and then suggests a framework of the advanced form of learning analytics i.e., multimodal learning analytics for tracing and facilitating multiple intelligences while they are engaged in online ubiquitous learning. As multimodal learning analytics is still an evolving area, it poses many challenges for technologists, educationists as well as organizational managers. Learning analytics make machines meet humans, therefore, the educationists with an expertise in learning theories can help technologists devise latest technological methods for multimodal learning analytics and organizational managers can implement them for the improvement of online education. Therefore, a careful instructional design based on a deep understanding of students’ learning abilities, is required to develop teaching plans and technological possibilities for monitoring students’ learning paths. This is how learning analytics can help design an adaptive instructional design based on a quick analysis of the data gathered. Based on that analysis, the academicians can critically reflect upon the quick or delayed implementation of the existing instructional design based on students’ cognitive abilities or even about the single or double loop learning design. The researcher concludes that the online education is multimodal in nature, has the capacity to endorse multiliteracies and, therefore, multiple intelligences can be tracked and facilitated through multimodal learning analytics in an online mode. However, online teachers’ training both in technological implementations and

  2. Multiple Kernel Learning with Data Augmentation

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

  3. Generalized multiple kernel learning with data-dependent priors.

    Science.gov (United States)

    Mao, Qi; Tsang, Ivor W; Gao, Shenghua; Wang, Li

    2015-06-01

    Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.

  4. Veto-Consensus Multiple Kernel Learning

    NARCIS (Netherlands)

    Zhou, Y.; Hu, N.; Spanos, C.J.

    2016-01-01

    We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

  5. The multiple reals of workplace learning

    Directory of Open Access Journals (Sweden)

    Kerry Harman

    2014-04-01

    Full Text Available The multiple reals of workplace learning are explored in this paper. Drawing on a Foucauldian conceptualisation of power as distributed, relational and productive, networks that work to produce particular objects and subjects as seemingly natural and real are examined. This approach enables different reals of workplace learning to be traced. Data from a collaborative industry-university research project is used to illustrate the approach, with a focus on the intersecting practices of a group of professional developers and a group of workplace learning researchers. The notion of multiple reals holds promise for research on workplace learning as it moves beyond a view of reality as fixed and singular to a notion of reality as performed in and through a diversity of practices, including the practices of workplace learning researchers.

  6. Multiple reversal olfactory learning in honeybees

    Directory of Open Access Journals (Sweden)

    Theo Mota

    2010-07-01

    Full Text Available In multiple reversal learning, animals trained to discriminate a reinforced from a non-reinforced stimulus are subjected to various, successive reversals of stimulus contingencies (e.g. A+ vs. B-, A- vs. B+, A+ vs. B-. This protocol is useful to determine whether or not animals learn to learn and solve successive discriminations faster (or with fewer errors with increasing reversal experience. Here we used the olfactory conditioning of proboscis extension reflex to study how honeybees Apis mellifera perform in a multiple reversal task. Our experiment contemplated four consecutive differential conditioning phases involving the same odors (A+ vs. B- to A- vs. B+ to A+ vs. B- to A- vs. B+. We show that bees in which the weight of reinforced or non-reinforced stimuli was similar mastered the multiple olfactory reversals. Bees which failed the task exhibited asymmetric responses to reinforced and non-reinforced stimuli, thus being unable to rapidly reverse stimulus contingencies. Efficient reversers did not improve their successive discriminations but rather tended to generalize their choice to both odors at the end of conditioning. As a consequence, both discrimination and reversal efficiency decreasedalong experimental phases. This result invalidates a learning-to-learn effect and indicates that bees do not only respond to the actual stimulus contingencies but rather combine these with an average of past experiences with the same stimuli.  

  7. Multiple goals, motivation and academic learning.

    Science.gov (United States)

    Valle, Antonio; Cabanach, Ramón G; Núnez, José C; González-Pienda, Julio; Rodríguez, Susana; Piñeiro, Isabel

    2003-03-01

    The type of academic goals pursued by students is one of the most important variables in motivational research in educational contexts. Although motivational theory and research have emphasised the somewhat exclusive nature of two types of goal orientation (learning goals versus performance goals), some studies (Meece, 1994; Seifert, 1995, 1996) have shown that the two kinds of goals are relatively complementary and that it is possible for students to have multiple goals simultaneously, which guarantees some flexibility to adapt more efficaciously to various contexts and learning situations. The principal aim of this study is to determine the academic goals pursued by university students and to analyse the differences in several very significant variables related to motivation and academic learning. Participants were 609 university students (74% women and 26% men) who filled in several questionnaires about the variables under study. We used cluster analysis ('quick cluster analysis' method) to establish the different groups or clusters of individuals as a function of the three types of goals (learning goals, performance goals, and social reinforcement goals). By means of MANOVA, we determined whether the groups or clusters identified were significantly different in the variables that are relevant to motivation and academic learning. Lastly, we performed ANOVA on the variables that revealed significant effects in the previous analysis. Using cluster analysis, three groups of students with different motivational orientations were identified: a group with predominance of performance goals (Group PG: n = 230), a group with predominance of multiple goals (Group MG: n = 238), and a group with predominance of learning goals (Group LG: n = 141). Groups MG and LG attributed their success more to ability, they had higher perceived ability, they took task characteristics into account when planning which strategies to use in the learning process, they showed higher persistence

  8. Multiple systems for motor skill learning.

    Science.gov (United States)

    Clark, Dav; Ivry, Richard B

    2010-07-01

    Motor learning is a ubiquitous feature of human competence. This review focuses on two particular classes of model tasks for studying skill acquisition. The serial reaction time (SRT) task is used to probe how people learn sequences of actions, while adaptation in the context of visuomotor or force field perturbations serves to illustrate how preexisting movements are recalibrated in novel environments. These tasks highlight important issues regarding the representational changes that occur during the course of motor learning. One important theme is that distinct mechanisms vary in their information processing costs during learning and performance. Fast learning processes may require few trials to produce large changes in performance but impose demands on cognitive resources. Slower processes are limited in their ability to integrate complex information but minimally demanding in terms of attention or processing resources. The representations derived from fast systems may be accessible to conscious processing and provide a relatively greater measure of flexibility, while the representations derived from slower systems are more inflexible and automatic in their behavior. In exploring these issues, we focus on how multiple neural systems may interact and compete during the acquisition and consolidation of new behaviors. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Psychology > Motor Skill and Performance. Copyright © 2010 John Wiley & Sons, Ltd.

  9. Multiple Chaotic Central Pattern Generators with Learning for Legged Locomotion and Malfunction Compensation

    DEFF Research Database (Denmark)

    Ren, Guanjiao; Chen, Weihai; Dasgupta, Sakyasingha

    2015-01-01

    on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs...... in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body...... chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based...

  10. Memory and learning disturbances in multiple sclerosis

    International Nuclear Information System (INIS)

    Izquierdo, Guillermo; Mir, Jordi; Gonzalez, Manuel; Martinez-Parra, Carlos; Campoy, Francisco Jr

    1991-01-01

    Thirty-five patients with definite multiple sclerosis (MS) were studied. They underwent neuropsychological testing and magnetic resonance imaging (MRI). The MRI findings at different brain areas levels were compared with the neuropsychological findings. A quantitative system was used to measure MRI-MS lesions. In this series, a positive correlation was established between memory and learning disturbances measured by Battery 144, and the lesions measured by MRI (total, hemispheric and , particularly, periventricular lesions). MRI can detect MS lesions, and this study shows that a correlation between MRI and neuropsychological findings is possible if quantitative methods are used to distinguish different MS involvement areas in relation to neuropsychological tasks. These findings suggest that hemispheric lesions in MS produce cognitive disturbances and MRI could be a useful tool in predicting memory and learning impairment. (author). 20 refs.; 1 fig.; 2 tabs

  11. Instance-specific algorithm configuration

    CERN Document Server

    Malitsky, Yuri

    2014-01-01

    This book presents a modular and expandable technique in the rapidly emerging research area of automatic configuration and selection of the best algorithm for the instance at hand. The author presents the basic model behind ISAC and then details a number of modifications and practical applications. In particular, he addresses automated feature generation, offline algorithm configuration for portfolio generation, algorithm selection, adaptive solvers, online tuning, and parallelization.    The author's related thesis was honorably mentioned (runner-up) for the ACP Dissertation Award in 2014,

  12. Instance-Based Question Answering

    Science.gov (United States)

    2006-12-01

    cluster-based query expan- sion, learning answering strategies, machine learning in NLP To my wife Monica Abstract During recent years, question...process is typically tedious and involves expertise in crafting and implement- ing these models (e.g. rule-based), utilizing NLP resources, and...questions. For languages that use capitalization (e.g. not Chinese or Arabic ) for named entities, IBQA can make use of NE classing (e.g. “Bob Marley

  13. An Investigation between Multiple Intelligences and Learning Styles

    Science.gov (United States)

    Sener, Sabriye; Çokçaliskan, Ayten

    2018-01-01

    Exploring learning style and multiple intelligence type of learners can enable the students to identify their strengths and weaknesses and learn from them. It is also very important for teachers to understand their learners' learning styles and multiple intelligences since they can carefully identify their goals and design activities that can…

  14. Reduced multiple empirical kernel learning machine.

    Science.gov (United States)

    Wang, Zhe; Lu, MingZhe; Gao, Daqi

    2015-02-01

    Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

  15. Pareto-path multitask multiple kernel learning.

    Science.gov (United States)

    Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2015-01-01

    A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

  16. Learning Recursion: Multiple Nested and Crossed Dependencies

    Directory of Open Access Journals (Sweden)

    Meinou de Vries

    2011-06-01

    Full Text Available Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help deter-mine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed, as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.

  17. A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

    OpenAIRE

    Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

    2013-01-01

    Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

  18. Boosting instance prototypes to detect local dermoscopic features.

    Science.gov (United States)

    Situ, Ning; Yuan, Xiaojing; Zouridakis, George

    2010-01-01

    Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

  19. Multiple systems for motor skill learning

    OpenAIRE

    Clark, Dav; Ivry, Richard B.

    2010-01-01

    Motor learning is a ubiquitous feature of human competence. This review focuses on two particular classes of model tasks for studying skill acquisition. The serial reaction time (SRT) task is used to probe how people learn sequences of actions, while adaptation in the context of visuomotor or force field perturbations serves to illustrate how preexisting movements are recalibrated in novel environments. These tasks highlight important issues regarding the representational changes that occur d...

  20. Handling multiple metadata streams regarding digital learning material

    NARCIS (Netherlands)

    Roes, J.B.M.; Vuuren, J. van; Verbeij, N.; Nijstad, H.

    2010-01-01

    This paper presents the outcome of a study performed in the Netherlands on handling multiple metadata streams regarding digital learning material. The paper describes the present metadata architecture in the Netherlands, the present suppliers and users of metadata and digital learning materials. It

  1. Multiple Learning Tracks: For Training Multinational Managers

    Science.gov (United States)

    Harvey, Michael G.; Kerin, Roger A.

    1977-01-01

    The problem of identifying and training college students to be effective multinational marketing managers is investigated in three parts: (1) Identification of multinational manager attributes, (2) selection of multinational managers, and (3) multiple "track" training programs. (TA)

  2. Development of Multiple Thinking and Creativity in Organizational Learning

    Science.gov (United States)

    Cheng, Yin Cheong

    2005-01-01

    Purpose: Based on a typology of contextualized multiple thinking, this paper aims to elaborate how the levels of thinking (data, information, knowledge, and intelligence), and the types of thinking as a whole, can be used to profile the characteristics of multiple thinking in organizational learning, re-conceptualize the nature of creativity in…

  3. Localized Multiple Kernel Learning A Convex Approach

    Science.gov (United States)

    2016-11-22

    data. All the aforementioned approaches to localized MKL are formulated in terms of non-convex optimization problems, and deep the- oretical...learning. IEEE Transactions on Neural Networks, 22(3):433–446, 2011. Jingjing Yang, Yuanning Li, Yonghong Tian, Lingyu Duan, and Wen Gao. Group-sensitive

  4. Multiple-choice pretesting potentiates learning of related information.

    Science.gov (United States)

    Little, Jeri L; Bjork, Elizabeth Ligon

    2016-10-01

    Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.

  5. The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

    Science.gov (United States)

    Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

    2014-01-01

    In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

  6. A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

    NARCIS (Netherlands)

    Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

    2009-01-01

    Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

  7. Learning of Rule Ensembles for Multiple Attribute Ranking Problems

    Science.gov (United States)

    Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

    In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

  8. Multiple mentoring relationships facilitate learning during fieldwork.

    Science.gov (United States)

    Nolinske, T

    1995-01-01

    Fieldwork provides a means by which students are socialized into their profession and their careers. During Level I and Level II fieldwork, students acquire and apply the knowledge, skills, and attitudes that will enable them to achieve entry-level competence The experiences that students have during Level II fieldwork influence their subsequent career choices. To support these experiences, students form a variety of helping relationships with faculty members, clinicians, peers, family, and friends. This article examines the role and responsibilities of the student as protégé and of the clinical educator as information peer, collegial peer, special peer, and mentor. In light of the challenges faced by most clinicians secondary to health care reform, an alternative to the one-to-one supervision model is presented. The multiple mentoring model of fieldwork supervision has several advantages: (a) fieldwork educators work with students according to their strengths and interests; (b) the model promotes collegiality and clinical reasoning skills because students use each other as resources and observe different fieldwork educators approaching similar situations; and (c) the model allows a fieldwork site to accept more students at one time, while minimizing stress on any one fieldwork educator. A framework defining the functions of the mentor-protégé relationship is provided, with an emphasis on the effect that clinical educators have in their roles as mentors, guides, role models, and teachers who provide opportunities for the student to develop entry-level competency in a chosen profession.

  9. Cross-platform learning: on the nature of children's learning from multiple media platforms.

    Science.gov (United States)

    Fisch, Shalom M

    2013-01-01

    It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several recent studies to explore cross-platform learning (i.e., learning from combined use of multiple media platforms) and how such learning compares to learning from one medium. The paper discusses unique benefits of cross-platform learning, a theoretical mechanism to explain how these benefits might arise, and questions for future research in this emerging field. Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company.

  10. Optimizing Multiple-Choice Tests as Learning Events

    Science.gov (United States)

    Little, Jeri Lynn

    2011-01-01

    Although generally used for assessment, tests can also serve as tools for learning--but different test formats may not be equally beneficial. Specifically, research has shown multiple-choice tests to be less effective than cued-recall tests in improving the later retention of the tested information (e.g., see meta-analysis by Hamaker, 1986),…

  11. Learning Multiplication Using Indonesian Traditional game in Third Grade

    Directory of Open Access Journals (Sweden)

    Rully Charitas Indra Prahmana

    2012-07-01

    Full Text Available Several previous researches showed that students had difficulty inunderstanding the basic concept of multiplication. Students are morelikely to be introduced by using formula without involving the conceptitself. This underlies the researcher to design a learning trajectory oflearning multiplication using Permainan Tradisional Tepuk Bergambar(PT2B as a context based on the student experience. The purpose ofthis research is to look at the role of PT2B in helping students'understanding in learning multiplication, which evolved from theinformal to formal level in third grade with Pendidikan MatematikaRealistik Indonesia (PMRI approach. The method used is designresearch starting from preliminary design, teaching experiments, andretrospective analysis. This research describes how PT2B make a realcontribution to the third grade students of SDN 179 Palembang tounderstand the concept of multiplication. The results showed PT2Bcontext can stimulate students to understand their knowledge of themultiplication concept. The whole strategy and model that studentsdiscover, describe, and discuss shows how the students construction orcontribution can uses to help their initial understanding of that concept.The stages in the learning trajectory of student have an important rolein understanding the concept of the operation number from informal tothe formal level.Keyword: Design Research, PMRI, Multiplication, Permainan TradisionalTepuk Bergambar DOI: http://dx.doi.org/10.22342/jme.3.2.1931.115-132

  12. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Ling-Yu Duan

    2010-01-01

    Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  13. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Tian Yonghong

    2010-01-01

    Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  14. Clustering with Instance and Attribute Level Side Information

    Directory of Open Access Journals (Sweden)

    Jinlong Wang

    2010-12-01

    Full Text Available Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

  15. Kernel Methods for Mining Instance Data in Ontologies

    Science.gov (United States)

    Bloehdorn, Stephan; Sure, York

    The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

  16. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

    Science.gov (United States)

    He, Dan; Kuhn, David; Parida, Laxmi

    2016-06-15

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

  17. Cross-Platform Learning: On the Nature of Children's Learning from Multiple Media Platforms

    Science.gov (United States)

    Fisch, Shalom M.

    2013-01-01

    It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several…

  18. Training Lp norm multiple kernel learning in the primal.

    Science.gov (United States)

    Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

    2013-10-01

    Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Reward-related learning via multiple memory systems.

    Science.gov (United States)

    Delgado, Mauricio R; Dickerson, Kathryn C

    2012-07-15

    The application of a neuroeconomic approach to the study of reward-related processes has provided significant insights in our understanding of human learning and decision making. Much of this research has focused primarily on the contributions of the corticostriatal circuitry, involved in trial-and-error reward learning. As a result, less consideration has been allotted to the potential influence of different neural mechanisms such as the hippocampus or to more common ways in human society in which information is acquired and utilized to reach a decision, such as through explicit instruction rather than trial-and-error learning. This review examines the individual contributions of multiple learning and memory neural systems and their interactions during human decision making in both normal and neuropsychiatric populations. Specifically, the anatomical and functional connectivity across multiple memory systems are highlighted to suggest that probing the role of the hippocampus and its interactions with the corticostriatal circuitry via the application of model-based neuroeconomic approaches may provide novel insights into neuropsychiatric populations that suffer from damage to one of these structures and as a consequence have deficits in learning, memory, or decision making. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  20. Domain Adaptation for Machine Translation with Instance Selection

    Directory of Open Access Journals (Sweden)

    Biçici Ergun

    2015-04-01

    Full Text Available Domain adaptation for machine translation (MT can be achieved by selecting training instances close to the test set from a larger set of instances. We consider 7 different domain adaptation strategies and answer 7 research questions, which give us a recipe for domain adaptation in MT. We perform English to German statistical MT (SMT experiments in a setting where test and training sentences can come from different corpora and one of our goals is to learn the parameters of the sampling process. Domain adaptation with training instance selection can obtain 22% increase in target 2-gram recall and can gain up to 3:55 BLEU points compared with random selection. Domain adaptation with feature decay algorithm (FDA not only achieves the highest target 2-gram recall and BLEU performance but also perfectly learns the test sample distribution parameter with correlation 0:99. Moses SMT systems built with FDA selected 10K training sentences is able to obtain F1 results as good as the baselines that use up to 2M sentences. Moses SMT systems built with FDA selected 50K training sentences is able to obtain F1 point better results than the baselines.

  1. Multiple-Machine Scheduling with Learning Effects and Cooperative Games

    Directory of Open Access Journals (Sweden)

    Yiyuan Zhou

    2015-01-01

    Full Text Available Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

  2. General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.

    Science.gov (United States)

    Chung, Wooyong; Kim, Jisu; Lee, Heejin; Kim, Euntai

    2015-11-01

    Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

  3. Basics of XBRL Instance for Financial Reporting

    Directory of Open Access Journals (Sweden)

    Mihaela Enachi

    2011-10-01

    Full Text Available The development of XBRL (eXtensible Business Reporting Language for financial reporting has significantly changed the way which financial statements are presented to differentusers and implicitly the quantity and quality of information provided through such a modern format. Following a standard structure, but adaptable to the regulations from different countriesor regions of the world, we can communicate and process financial accounting information more efficient and effective. This paper tries to clarify the manner of preparation and presentation ofthe financial statements if using XBRL as reporting tool.Keywords: XML, XBRL, financial reporting, specification, taxonomy, instance

  4. The Effect of Multiple Intelligences on DDL Vocabulary Learning

    Directory of Open Access Journals (Sweden)

    Asma’a Abdulrazzaq Al-Mahbashi

    2017-01-01

    Full Text Available Over the past decades, the potential for the direct use of corpora known as data driven learning (DDL has gained great prominence in English language classrooms. A substantial number of empirical studies demonstrated that DDL instruction positively affects students’ learning. As learning outcomes can be affected by individual differences, some researchers have investigated the efficiency of DDL in the light of learners’ different characteristics to determine the type of learners who were more responsive to DDL. The DDL literature has indicated the need for more research addressing for whom DDL best suits. Therefore, the aim of the current study was to examine whether or not learners’ predominant intelligences were significant predictors of DDL learning outcomes. The sample for this study included 30 female EFL Yemeni students at Sana’a University. The study used three primary instruments:  a multiple intelligence questionnaire, a posttest and a delayed test on the vocabulary that was taught using DDL. The result of the correlation analyses between the participants’ three identified predominant intelligences and their performances in the posttest and delayed test showed an insignificant relationship between the variables. The regression analyses results also revealed that the predominant intelligences insignificantly predicted the participants’ posttest and delayed test performances.  Based on these findings, learners’ needs and preferences should be activated and addressed by classroom instructions for creating a diverse and motivating learning environment.

  5. Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers

    Science.gov (United States)

    Gracious, F. L. Antony; Shyla, F. L. Jasmine Anne

    2012-01-01

    The present study Multiple Intelligence and Digital Learning Awareness of prospective B.Ed teachers was probed to find the relationship between Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers. Data for the study were collected using self made Multiple Intelligence Inventory and Digital Learning Awareness Scale.…

  6. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Science.gov (United States)

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  7. Multiple intelligences and underachievement: lessons from individuals with learning disabilities.

    Science.gov (United States)

    Hearne, D; Stone, S

    1995-01-01

    The field of learning disabilities, like education in the main, is undergoing calls for reform and restructuring, an upheaval brought on in great part by the forces of opposing paradigms--reductionism and constructivism. In reexamining our past, we must begin to address the failures of traditional deficit models and their abysmally low "cure" rate. Several new theories have arisen that challenge traditional practices in both general and special education classrooms. Particularly influential has been the work of Howard Gardner, whose theory of multiple intelligences calls for a restructuring of our schools to accommodate modes of learning and inquiry with something other than deficit approaches. At least some current research in the field of learning disabilities has begun to focus on creativity and nontraditional strengths and talents that have not been well understood or highly valued by the schools. In this article, we briefly summarize the findings in our search for the talents of students labeled learning disabled, evidence of their abilities, implications of these for the schools, and a beginning set of practical recommendations.

  8. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    Energy Technology Data Exchange (ETDEWEB)

    Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)

    1999-03-01

    The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

  9. Simultaneous processing of information on multiple errors in visuomotor learning.

    Science.gov (United States)

    Kasuga, Shoko; Hirashima, Masaya; Nozaki, Daichi

    2013-01-01

    The proper association between planned and executed movements is crucial for motor learning because the discrepancies between them drive such learning. Our study explored how this association was determined when a single action caused the movements of multiple visual objects. Participants reached toward a target by moving a cursor, which represented the right hand's position. Once every five to six normal trials, we interleaved either of two kinds of visual perturbation trials: rotation of the cursor by a certain amount (±15°, ±30°, and ±45°) around the starting position (single-cursor condition) or rotation of two cursors by different angles (+15° and -45°, 0° and 30°, etc.) that were presented simultaneously (double-cursor condition). We evaluated the aftereffects of each condition in the subsequent trial. The error sensitivity (ratio of the aftereffect to the imposed visual rotation) in the single-cursor trials decayed with the amount of rotation, indicating that the motor learning system relied to a greater extent on smaller errors. In the double-cursor trials, we obtained a coefficient that represented the degree to which each of the visual rotations contributed to the aftereffects based on the assumption that the observed aftereffects were a result of the weighted summation of the influences of the imposed visual rotations. The decaying pattern according to the amount of rotation was maintained in the coefficient of each imposed visual rotation in the double-cursor trials, but the value was reduced to approximately 40% of the corresponding error sensitivity in the single-cursor trials. We also found a further reduction of the coefficients when three distinct cursors were presented (e.g., -15°, 15°, and 30°). These results indicated that the motor learning system utilized multiple sources of visual error information simultaneously to correct subsequent movement and that a certain averaging mechanism might be at work in the utilization process.

  10. Optimizing multiple-choice tests as tools for learning.

    Science.gov (United States)

    Little, Jeri L; Bjork, Elizabeth Ligon

    2015-01-01

    Answering multiple-choice questions with competitive alternatives can enhance performance on a later test, not only on questions about the information previously tested, but also on questions about related information not previously tested-in particular, on questions about information pertaining to the previously incorrect alternatives. In the present research, we assessed a possible explanation for this pattern: When multiple-choice questions contain competitive incorrect alternatives, test-takers are led to retrieve previously studied information pertaining to all of the alternatives in order to discriminate among them and select an answer, with such processing strengthening later access to information associated with both the correct and incorrect alternatives. Supporting this hypothesis, we found enhanced performance on a later cued-recall test for previously nontested questions when their answers had previously appeared as competitive incorrect alternatives in the initial multiple-choice test, but not when they had previously appeared as noncompetitive alternatives. Importantly, however, competitive alternatives were not more likely than noncompetitive alternatives to be intruded as incorrect responses, indicating that a general increased accessibility for previously presented incorrect alternatives could not be the explanation for these results. The present findings, replicated across two experiments (one in which corrective feedback was provided during the initial multiple-choice testing, and one in which it was not), thus strongly suggest that competitive multiple-choice questions can trigger beneficial retrieval processes for both tested and related information, and the results have implications for the effective use of multiple-choice tests as tools for learning.

  11. A critique of medicalisation: three instances.

    Science.gov (United States)

    Ryang, Sonia

    2017-12-01

    By briefly exploring three different examples where the existence of mental illness and developmental delay has been presumed, this paper sheds light on the way what Foucault calls the emergence of a regime of truth, i.e. where something that does not exist is made to exist through the construction of a system of truth around it. The first example concerns the direct marketing of pharmaceutical products to consumers in the US, the second the use of psychology in semi-post-Cold War Korea, and the third the persisting authority of psychology in the treatment of the developmentally delayed. While these instances are not innately connected, looking at these as part of the process by which the authoritative knowledge is established will help us understand, albeit partially, the mechanism by which mental illness penetrates our lives as truth, and how this regime of truth is supported by the authority of psychology, psychiatry and psychoanalysis, what Foucault calls the 'psy-function,' reinforcing the medicalisation of our lives.

  12. Metabolite identification through multiple kernel learning on fragmentation trees.

    Science.gov (United States)

    Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

    2014-06-15

    Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

  13. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    Science.gov (United States)

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed

    2012-01-01

    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

  15. Automatic analysis of online image data for law enforcement agencies by concept detection and instance search

    Science.gov (United States)

    de Boer, Maaike H. T.; Bouma, Henri; Kruithof, Maarten C.; ter Haar, Frank B.; Fischer, Noëlle M.; Hagendoorn, Laurens K.; Joosten, Bart; Raaijmakers, Stephan

    2017-10-01

    The information available on-line and off-line, from open as well as from private sources, is growing at an exponential rate and places an increasing demand on the limited resources of Law Enforcement Agencies (LEAs). The absence of appropriate tools and techniques to collect, process, and analyze the volumes of complex and heterogeneous data has created a severe information overload. If a solution is not found, the impact on law enforcement will be dramatic, e.g. because important evidence is missed or the investigation time is too long. Furthermore, there is an uneven level of capabilities to deal with the large volumes of complex and heterogeneous data that come from multiple open and private sources at national level across the EU, which hinders cooperation and information sharing. Consequently, there is a pertinent need to develop tools, systems and processes which expedite online investigations. In this paper, we describe a suite of analysis tools to identify and localize generic concepts, instances of objects and logos in images, which constitutes a significant portion of everyday law enforcement data. We describe how incremental learning based on only a few examples and large-scale indexing are addressed in both concept detection and instance search. Our search technology allows querying of the database by visual examples and by keywords. Our tools are packaged in a Docker container to guarantee easy deployment on a system and our tools exploit possibilities provided by open source toolboxes, contributing to the technical autonomy of LEAs.

  16. Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

    Science.gov (United States)

    Bas, Gokhan

    2008-01-01

    This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

  17. Unsupervised multiple kernel learning for heterogeneous data integration.

    Science.gov (United States)

    Mariette, Jérôme; Villa-Vialaneix, Nathalie

    2018-03-15

    Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

  18. Inference and learning in sparse systems with multiple states

    International Nuclear Information System (INIS)

    Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.

    2011-01-01

    We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

  19. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    Science.gov (United States)

    Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

    2014-01-01

    Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

  20. Optimizing Multiple Kernel Learning for the Classification of UAV Data

    Directory of Open Access Journals (Sweden)

    Caroline M. Gevaert

    2016-12-01

    Full Text Available Unmanned Aerial Vehicles (UAVs are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM. A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

  1. Insights from Classifying Visual Concepts with Multiple Kernel Learning

    Science.gov (United States)

    Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki

    2012-01-01

    Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970

  2. Coarse-Grain QoS-Aware Dynamic Instance Provisioning for Interactive Workload in the Cloud

    Directory of Open Access Journals (Sweden)

    Jianxiong Wan

    2014-01-01

    Full Text Available Cloud computing paradigm renders the Internet service providers (ISPs with a new approach to deliver their service with less cost. ISPs can rent virtual machines from the Infrastructure-as-a-Service (IaaS provided by the cloud rather than purchasing them. In addition, commercial cloud providers (CPs offer diverse VM instance rental services in various time granularities, which provide another opportunity for ISPs to reduce cost. We investigate a Coarse-grain QoS-aware Dynamic Instance Provisioning (CDIP problem for interactive workload in the cloud from the perspective of ISPs. We formulate the CDIP problem as an optimization problem where the objective is to minimize the VM instance rental cost and the constraint is the percentile delay bound. Since the Internet traffic shows a strong self-similar property, it is hard to get an analytical form of the percentile delay constraint. To address this issue, we purpose a lookup table structure together with a learning algorithm to estimate the performance of the instance provisioning policy. This approach is further extended with two function approximations to enhance the scalability of the learning algorithm. We also present an efficient dynamic instance provisioning algorithm, which takes full advantage of the rental service diversity, to determine the instance rental policy. Extensive simulations are conducted to validate the effectiveness of the proposed algorithms.

  3. Connecting Multiple Intelligences through Open and Distance Learning: Going towards a Collective Intelligence?

    Science.gov (United States)

    Medeiros Vieira, Leandro Mauricio; Ferasso, Marcos; Schröeder, Christine da Silva

    2014-01-01

    This theoretical essay is a learning approach reflexion on Howard Gardner's Theory of Multiple Intelligences and the possibilities provided by the education model known as open and distance learning. Open and distance learning can revolutionize traditional pedagogical practice, meeting the needs of those who have different forms of cognitive…

  4. Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan; Gao, Xin

    2014-01-01

    Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.

  5. Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-02-01

    Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.

  6. Using mini-games for learning multiplication and division: A longitudinal effect study

    NARCIS (Netherlands)

    Bakker, M.

    2014-01-01

    This thesis reports the findings of a three-year longitudinal research project set up to investigate the effectiveness of employing online mini-games for the learning of multiplication and division, including multiplicative fact knowledge (declarative knowledge), multiplicative operation skills

  7. The effect of multiple intelligence-based learning towards students’ concept mastery and interest in learning matter

    Science.gov (United States)

    Pratiwi, W. N.; Rochintaniawati, D.; Agustin, R. R.

    2018-05-01

    This research was focused on investigating the effect of multiple intelligence -based learning as a learning approach towards students’ concept mastery and interest in learning matter. The one-group pre-test - post-test design was used in this research towards a sample which was according to the suitable situation of the research sample, n = 13 students of the 7th grade in a private school in Bandar Seri Begawan. The students’ concept mastery was measured using achievement test and given at the pre-test and post-test, meanwhile the students’ interest level was measured using a Likert Scale for interest. Based on the analysis of the data, the result shows that the normalized gain was .61, which was considered as a medium improvement. in other words, students’ concept mastery in matter increased after being taught using multiple intelligence-based learning. The Likert scale of interest shows that most students have a high interest in learning matter after being taught by multiple intelligence-based learning. Therefore, it is concluded that multiple intelligence – based learning helped in improving students’ concept mastery and gain students’ interest in learning matter.

  8. Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.

    Science.gov (United States)

    Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

    2016-01-01

    Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

  9. Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support

    International Nuclear Information System (INIS)

    Mazurowski, Maciej A; Tourassi, Georgia D; Malof, Jordan M

    2011-01-01

    When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.

  10. Human learning: Power laws or multiple characteristic time scales?

    Directory of Open Access Journals (Sweden)

    Gottfried Mayer-Kress

    2006-09-01

    Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

  11. Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

    Science.gov (United States)

    Rau, Martina A.; Scheines, Richard

    2012-01-01

    Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…

  12. Multiple Intelligences, Motivations and Learning Experience Regarding Video-Assisted Subjects in a Rural University

    Science.gov (United States)

    Hajhashemi, Karim; Caltabiano, Nerina; Anderson, Neil; Tabibzadeh, Seyed Asadollah

    2018-01-01

    This study investigates multiple intelligences in relation to online video experiences, age, gender, and mode of learning from a rural Australian university. The inter-relationships between learners' different intelligences and their motivations and learning experience with the supplementary online videos utilised in their subjects are…

  13. The MORPG-Based Learning System for Multiple Courses: A Case Study on Computer Science Curriculum

    Science.gov (United States)

    Liu, Kuo-Yu

    2015-01-01

    This study aimed at developing a Multiplayer Online Role Playing Game-based (MORPG) Learning system which enabled instructors to construct a game scenario and manage sharable and reusable learning content for multiple courses. It used the curriculum of "Introduction to Computer Science" as a study case to assess students' learning…

  14. Collaborative E-Learning with Multiple Imaginary Co-Learner: Design, Issues and Implementation

    OpenAIRE

    Melvin Ballera; Mosbah Mohamed Elssaedi; Ahmed Khalil Zohdy

    2013-01-01

    Collaborative problem solving in e-learning can take in the form of discussion among learner, creating a highly social learning environment and characterized by participation and interactivity. This paper, designed a collaborative learning environment where agent act as co-learner, can play different roles during interaction. Since different roles have been assigned to the agent, learner will assume that multiple co-learner exists to help and guide him all throughout the ...

  15. Multimodality and children's participation in classrooms: Instances of ...

    African Journals Online (AJOL)

    Multimodality and children's participation in classrooms: Instances of research. ... deficit models of children, drawing on their everyday experiences and their existing ... It outlines the theoretical framework supporting the pedagogical approach, ...

  16. Memory for Instances and Categories in Children and Adults

    Science.gov (United States)

    Tighe, Thomas J.; And Others

    1975-01-01

    Two studies of 7-year-olds and college students tested the hypothesis of a developmental difference in the degree to which subjects' memory performance was controlled by categorical properties vs. specific instance properties of test items. (GO)

  17. Learning Effects of Interactive Whiteboard Pedagogy for Students in Taiwan from the Perspective of Multiple Intelligences

    Science.gov (United States)

    Chen, Hong-Ren; Chiang, Chih-Hao; Lin, Wen-Shan

    2013-01-01

    With the rapid progress in information technology, interactive whiteboards have become IT-integrated in teaching activities. The theory of multiple intelligences argues that every person possesses multiple intelligences, emphasizing learners' cognitive richness and the possible role of these differences in enhanced learning. This study is the…

  18. A Mobile Service Oriented Multiple Object Tracking Augmented Reality Architecture for Education and Learning Experiences

    Science.gov (United States)

    Rattanarungrot, Sasithorn; White, Martin; Newbury, Paul

    2014-01-01

    This paper describes the design of our service-oriented architecture to support mobile multiple object tracking augmented reality applications applied to education and learning scenarios. The architecture is composed of a mobile multiple object tracking augmented reality client, a web service framework, and dynamic content providers. Tracking of…

  19. Enhancing Undergraduate Chemistry Learning by Helping Students Make Connections among Multiple Graphical Representations

    Science.gov (United States)

    Rau, Martina A.

    2015-01-01

    Multiple representations are ubiquitous in chemistry education. To benefit from multiple representations, students have to make connections between them. However, connection making is a difficult task for students. Prior research shows that supporting connection making enhances students' learning in math and science domains. Most prior research…

  20. Studying different tasks of implicit learning across multiple test sessions conducted on the web

    Directory of Open Access Journals (Sweden)

    Werner eSævland

    2016-06-01

    Full Text Available Implicit learning is usually studied through individual performance on a single task, with the most common tasks being Serial Reaction Time task (SRT; Nissen and Bullemer, 1987, Dynamic System Control task (DSC; (Berry and Broadbent, 1984 and artificial Grammar Learning task (AGL; (Reber, 1967. Few attempts have been made to compare performance across different implicit learning tasks within the same experiment. The current experiment was designed study the relationship between performance on the DSC Sugar factory task (Berry and Broadbent, 1984 and the Alternating Serial Reaction Time task (ASRT; (Howard and Howard, 1997. We also addressed another limitation to traditional implicit learning experiments, namely that implicit learning is usually studied in laboratory settings over a restricted time span lasting for less than an hour (Berry and Broadbent, 1984; Nissen and Bullemer, 1987; Reber, 1967. In everyday situations, implicit learning is assumed to involve a gradual accumulation of knowledge across several learning episodes over a larger time span (Norman and Price, 2012. One way to increase the ecological validity of implicit learning experiments could be to present the learning material repeatedly across shorter experimental sessions (Howard and Howard, 1997; Cleeremans and McClelland, 1991. This can most easily be done by using a web-based setup that participants can access from home. We therefore created an online web-based system for measuring implicit learning that could be administered in either single or multiple sessions. Participants (n = 66 were assigned to either a single-session or a multi-session condition. Learning and the degree of conscious awareness of the learned regularities was compared across condition (single vs. multiple sessions and tasks (DSC vs. ASRT. Results showed that learning on the two tasks was not related. However, participants in the multiple sessions condition did show greater improvements in reaction

  1. Negotiating Multiple Identities through eTandem Learning Experiences

    Science.gov (United States)

    Yang, Se Jeong; Yi, Youngjoo

    2017-01-01

    Much of eTandem research has investigated either linguistic or cross-cultural aspects of second language (L2) learning, but relatively little is known about issues of identity construction in an eTandem context. Situating the study within theories and research of language learner identity, we examined ways in which two adult L2 learners (a Korean…

  2. Multiple Learning Strategies Project. Small Engine Repair. Visually Impaired.

    Science.gov (United States)

    Foster, Don; And Others

    This instructional package designed for visually impaired students, focuses on the vocational area of small engine repair. Contained in this document are forty learning modules organized into fourteen units: engine block; starters; fuel tank, lines, filters and pumps; carburetors; electrical; test equipment; motorcycle; machining; tune-ups; short…

  3. A Multiple Cross-Cultural Comparison of Approaches to Learning

    Science.gov (United States)

    Bowden, Mark P.; Abhayawansa, Subhash; Manzin, Gregoria

    2015-01-01

    This study compares learning approaches of local English-speaking students and students from Asian countries studying at an Australian metropolitan university. The sample consists of students across 13 different countries. Unlike previous studies, students from Asian countries are subdivided into two categories: students from Confucian Heritage…

  4. Amidst Multiple Theories of Learning in Mathematics Education

    Science.gov (United States)

    Simon, Martin A.

    2009-01-01

    Currently, there are more theories of learning in use in mathematics education research than ever before (Lerman & Tsatsaroni, 2004). Although this is a positive sign for the field, it also has brought with it a set of challenges. In this article, I identify some of these challenges and consider how mathematics education researchers might think…

  5. Multiple Learning Strategies Project. Building Maintenance & Engineering. Visually Impaired.

    Science.gov (United States)

    Smith, Dwight; And Others

    This instructional package is designed for visually impaired students in the vocational area of building maintenance and engineering. The twenty-eight learning modules are organized into six units: floor care, general maintenance tasks; restrooms; carpet care; power and hand tools; and cabinet construction. Each module, printed in large block…

  6. Multiplicity in public health supply systems: a learning agenda.

    Science.gov (United States)

    Bornbusch, Alan; Bates, James

    2013-08-01

    Supply chain integration-merging products for health programs into a single supply chain-tends to be the dominant model in health sector reform. However, multiplicity in a supply system may be justified as a risk management strategy that can better ensure product availability, advance specific health program objectives, and increase efficiency.

  7. Intermediality: How the Use of Multiple Media Enhances Learning.

    Science.gov (United States)

    Lapp, Diane; Flood, James; Fisher, Douglas

    1999-01-01

    Describes how one fourth-grade teacher used television, videos, computers, classroom guests, and lots of reading and writing to engage his students in their language-arts unit, focusing on the book "Shiloh." Discusses how such "intermediality" (using multiple media) increases students' comprehension and understanding of text, capturing their…

  8. Practical Usage of Multiple-Choice Questions as Part of Learning and Self-Evaluation

    Directory of Open Access Journals (Sweden)

    Paula Kangasniemi

    2016-12-01

    Full Text Available The poster describes how the multiple-choice questions could be a part of learning, not only assessing. We often think of the role of questions only in order to test the student's skills. We have tested how questions could be a part of learning in our web-based course of information retrieval in Lapland University. In web-based learning there is a need for high-quality mediators. Mediators are learning promoters which trigger, support, and amplify learning. Mediators can be human mediators or tool mediators. The tool mediators are for example; tests, tutorials, guides and diaries. The multiple-choice questions can also be learning promoters which select, interpret and amplify objects for learning. What do you have to take into account when you are preparing multiple-choice questions as mediators? First you have to prioritize teaching objectives: what must be known and what should be known. According to our experience with contact learning, you can assess what the things are that students have problems with and need more guidance on. The most important addition to the questions is feedback during practice. The questions’ answers (wrong or right are not important. The feedback on the answers are important to guide students on how to search. The questions promote students’ self-regulation and self-evaluation. Feedback can be verbal, a screenshot or a video. We have added a verbal feedback for every question and also some screenshots and eight videos in our web-based course.

  9. The amygdala complex: multiple roles in associative learning and attention.

    OpenAIRE

    Gallagher, M; Holland, P C

    1994-01-01

    Although certain neurophysiological functions of the amygdala complex in learning seem well established, the purpose of this review is to propose that an additional conceptualization of amygdala function is now needed. The research we review provides evidence that a subsystem within the amygdala provides a coordinated regulation of attentional processes. An important aspect of this additional neuropsychology of the amygdala is that it may aid in understanding the importance of connections bet...

  10. Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

    Science.gov (United States)

    Ernst, Benjamin; Steinhauser, Marco

    2012-06-01

    Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

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

    Science.gov (United States)

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

    2017-12-04

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

  12. Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis.

    Science.gov (United States)

    Sumowski, James F; Wylie, Glenn R; Chiaravalloti, Nancy; DeLuca, John

    2010-06-15

    Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis.

  13. Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.

    Science.gov (United States)

    Velickovski, Filip; Ceccaroni, Luigi; Marti, Robert; Burgos, Felip; Gistau, Concepcion; Alsina-Restoy, Xavier; Roca, Josep

    2018-01-01

    Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus, a need to expand the availability of high-quality spirometry assessment beyond specialist pulmonary centres has arisen. In this paper, we propose a method to select and optimise a classifier using supervised learning techniques by learning from previously classified forced spirometry tests from a group of experts. Such a method is able to take into account the shape of the curve as an expert would during visual inspection. We evaluated the final classifier on a dataset put aside for evaluation yielding an area under the receiver operating characteristic curve of 0.88 and specificities of 0.91 and 0.86 for sensitivities of 0.60 and 0.82. Furthermore, other specificities and sensitivities along the receiver operating characteristic curve were close to the level of the experts when compared against each-other, and better than an earlier rules-based method assessed on the same dataset. We foresee key benefits in raising diagnostic quality, saving time, reducing cost, and also improving remote care and monitoring services for patients with chronic respiratory diseases in the future if a clinical decision support system with the encapsulated classifier is to be integrated into the work-flow of forced spirometry testing.

  14. Resource Planning for Massive Number of Process Instances

    Science.gov (United States)

    Xu, Jiajie; Liu, Chengfei; Zhao, Xiaohui

    Resource allocation has been recognised as an important topic for business process execution. In this paper, we focus on planning resources for a massive number of process instances to meet the process requirements and cater for rational utilisation of resources before execution. After a motivating example, we present a model for planning resources for process instances. Then we design a set of heuristic rules that take both optimised planning at build time and instance dependencies at run time into account. Based on these rules we propose two strategies, one is called holistic and the other is called batched, for resource planning. Both strategies target a lower cost, however, the holistic strategy can achieve an earlier deadline while the batched strategy aims at rational use of resources. We discuss how to find balance between them in the paper with a comprehensive experimental study on these two approaches.

  15. "More Confident Going into College": Lessons Learned from Multiple Stakeholders in a New Blended Learning Initiative

    Science.gov (United States)

    Whiteside, Aimee L.; Garrett Dikkers, Amy; Lewis, Somer

    2016-01-01

    This article examined a blended learning initiative in a large suburban high school in the Midwestern region of the United States. It employed a single-case exploratory design approach to learn about the experience of administrators, teachers, students, and parents. Using Zimmerman's Self-Regulated Learning (SRL) Theory as a guiding framework,…

  16. Multiple Pathways to Learning: An Examination of Universal Design and Online Strategic Learning in Higher Education

    Science.gov (United States)

    Hicks, Maryruth Wilks

    2010-01-01

    The purpose of this study was to examine the effectiveness of universally designed (UD) instruction on strategic learning in an online, interactive learning environment (ILE). The research focused on the premise that the customizable, media-based framework of UD instruction might influence diverse online learning strategies. This study…

  17. Multiple Modes in Corporate Learning: Propelling Business IQ with Formal, Informal and Social Learning

    Science.gov (United States)

    Ambrose, John; Ogilvie, Julie

    2010-01-01

    Recognizing that the shifting corporate environment is placing ever greater stresses on learning organizations, this paper reports how companies are increasingly offering employees a wide choice of learning options beyond conventional classroom training, including online, social learning, and other modalities in "blended" programs. Identifying a…

  18. Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

    DEFF Research Database (Denmark)

    Madsen, Jens; Jensen, Bjørn Sand; Larsen, Jan

    2015-01-01

    Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified...... and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test...

  19. Time and activity sequence prediction of business process instances

    DEFF Research Database (Denmark)

    Polato, Mirko; Sperduti, Alessandro; Burattin, Andrea

    2018-01-01

    The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict...... future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed...

  20. Move to learn: Integrating spatial information from multiple viewpoints.

    Science.gov (United States)

    Holmes, Corinne A; Newcombe, Nora S; Shipley, Thomas F

    2018-05-11

    Recalling a spatial layout from multiple orientations - spatial flexibility - is challenging, even when the global configuration can be viewed from a single vantage point, but more so when it must be viewed piecemeal. In the current study, we examined whether experiencing the transition between multiple viewpoints enhances spatial memory and flexible recall for a spatial configuration viewed simultaneously (Exp. 1) and sequentially (Exp. 2), whether the type of transition matters, and whether action provides an additional advantage over passive experience. In Experiment 1, participants viewed an array of dollhouse furniture from four viewpoints, but with all furniture simultaneously visible. In Experiment 2, participants viewed the same array piecemeal, from four partitioned viewpoints that allowed for viewing only a segment at a time. The transition between viewpoints involved rotation of the array or participant movement around it. Rotation and participant movement were passively experienced or actively generated. The control condition presented the dollhouse as a series of static views. Across both experiments, participant movement significantly enhanced spatial memory relative to array rotation or static views. However, in Exp. 2, there was a further advantage for actively walking around the array compared to being passively pushed. These findings suggest that movement around a stable environment is key to spatial memory and flexible recall, with action providing an additional boost to the integration of temporally segmented spatial events. Thus, spatial memory may be more flexible than prior data indicate, when studied under more natural acquisition conditions. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. Mixed Methods: Incorporating multiple learning styles into a measurements course

    Science.gov (United States)

    Pallone, Arthur

    2010-03-01

    The best scientists and engineers regularly combine creative and critical skill sets. As faculty, we are responsible to provide future scientists and engineers with those skills sets. EGR 390: Engineering Measurements at Murray State University is structured to actively engage students in the processes that develop and enhance those skills. Students learn through a mix of traditional lecture and homework, active discussion of open-ended questions, small group activities, structured laboratory exercises, oral and written communications exercises, student chosen team projects, and peer evaluations. Examples of each of these activities, the skill set addressed by each activity, outcomes from and effectiveness of each activity and recommendations for future directions in the EGR 390 course as designed will be presented.

  2. Working Memory Capacity Limits Motor Learning When Implementing Multiple Instructions

    Directory of Open Access Journals (Sweden)

    Tim Buszard

    2017-08-01

    Full Text Available Although it is generally accepted that certain practice conditions can place large demands on working memory (WM when performing and learning a motor skill, the influence that WM capacity has on the acquisition of motor skills remains unsubstantiated. This study examined the role of WM capacity in a motor skill practice context that promoted WM involvement through the provision of explicit instructions. A cohort of 90 children aged 8 to 10 years were assessed on measures of WM capacity and attention. Children who scored in the lowest and highest thirds on the WM tasks were allocated to lower WM capacity (n = 24 and higher WM capacity (n = 24 groups, respectively. The remaining 42 participants did not participate in the motor task. The motor task required children to practice basketball shooting for 240 trials in blocks of 20 shots, with pre- and post-tests occurring before and after the intervention. A retention test was administered 1 week after the post-test. Prior to every practice block, children were provided with five explicit instructions that were specific to the technique of shooting a basketball. Results revealed that the higher WM capacity group displayed consistent improvements from pre- to post-test and through to the retention test, while the opposite effect occurred in the lower WM capacity group. This implies that the explicit instructions had a negative influence on learning by the lower WM capacity children. Results are discussed in relation to strategy selection for dealing with instructions and the role of attention control.

  3. Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.

    Science.gov (United States)

    Chen, Chi-Hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

    2017-08-01

    Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co-occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. Copyright © 2016 Cognitive Science Society, Inc.

  4. Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

    OpenAIRE

    Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during ...

  5. TNO at TRECVID 2013: Multimedia Event Detection and Instance Search

    NARCIS (Netherlands)

    Bouma, H.; Azzopardi, G.; Spitters, M.M.; Wit, J.J. de; Versloot, C.A.; Zon, R.W.L. van der; Eendebak, P.T.; Baan, J.; Hove, R.J.M. ten; Eekeren, A.W.M. van; Haar, F.B. ter; Hollander, R.J.M. den; Huis, R.J. van; Boer, M.H.T. de; Antwerpen, G. van; Broekhuijsen, B.J.; Daniele, L.M.; Brandt, P.; Schavemaker, J.G.M.; Kraaij, W.; Schutte, K.

    2013-01-01

    We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level

  6. Locality in Generic Instance Search from One Example

    NARCIS (Netherlands)

    Tao, R.; Gavves, E.; Snoek, C.G.M.; Smeulders, A.W.M.

    2014-01-01

    This paper aims for generic instance search from a single example. Where the state-of-the-art relies on global image representation for the search, we proceed by including locality at all steps of the method. As the first novelty, we consider many boxes per database image as candidate targets to

  7. Data-aware remaining time prediction of business process instances

    NARCIS (Netherlands)

    Polato, M.; Sperduti, A.; Burattin, A.; Leoni, de M.

    2014-01-01

    Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process

  8. musical mnemonics aid verbal memory and induce learning related brain plasticity in multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Michael eThaut

    2014-06-01

    Full Text Available Recent research in music and brain function has suggested that the temporal pattern structure in music andrhythm can enhance cognitive functions. To further elucidate this question specifically for memory weinvestigated if a musical template can enhance verbal learning in patients with multiple sclerosis and ifmusic assisted learning will also influence short-term, system-level brain plasticity. We measuredsystems-level brain activity with oscillatory network synchronization during music assisted learning.Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG in alpha andbeta frequency bands in 54 patients with multiple sclerosis (MS. The study sample was randomlydivided into 2 groups, either hearing a spoken or musical (sung presentation of Rey’s Auditory VerbalLearning Test (RAVLT. We defined the learning-related synchronization (LRS as the percent changein EEG spectral power from the first time the word was presented to the average of the subsequent wordencoding trials. LRS differed significantly between the music and spoken conditions in low alpha andupper beta bands. Patients in the music condition showed overall better word memory and better wordorder memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. Theevidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization inprefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicitin musical stimuli enhances ‘deep encoding’ during verbal learning and sharpens the timing of neuraldynamics in brain networks degraded by demyelination in MS

  9. An Intrinsic Value System for Developing Multiple Invariant Representations with Incremental Slowness Learning

    Directory of Open Access Journals (Sweden)

    Matthew David Luciw

    2013-05-01

    Full Text Available Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA;~cite{cdmisfa} is a recently introduced model of intrinsically-motivated invariance learning, which shows how curiosity enables the orderly formation of multiple stable sensory representations, through which the agent can simplify its complex sensory input. Here, we first discuss the computational properties of the CD-MISFA model itself, followed by a discussion of neurophysiological analogs fulfilling similar functional roles. CD-MISFA combines 1. unsupervised representation learning through the slowness principle, 2. generation of an intrinsic reward signal through the learning progress of the developing features, and 3. balancing of exploration and exploitation in order to maximize learning progress and quickly learn multiple feature sets for perceptual simplification. Experimental results on synthetic observations and on the iCub robot show that the intrinsic value system is an essential component to representation learning, further, the model explores such that the representations are typically learned in order from least to most costly, as predicted by the theory of Artificial Curiosity.

  10. Student-Generated Content: Enhancing Learning through Sharing Multiple-Choice Questions

    Science.gov (United States)

    Hardy, Judy; Bates, Simon P.; Casey, Morag M.; Galloway, Kyle W.; Galloway, Ross K.; Kay, Alison E.; Kirsop, Peter; McQueen, Heather A.

    2014-01-01

    The relationship between students' use of PeerWise, an online tool that facilitates peer learning through student-generated content in the form of multiple-choice questions (MCQs), and achievement, as measured by their performance in the end-of-module examinations, was investigated in 5 large early-years science modules (in physics, chemistry and…

  11. The Learning Styles and Multiple Intelligences of EFL College Students in Kuwait

    Science.gov (United States)

    Alrabah, Sulaiman; Wu, Shu-hua; Alotaibi, Abdullah M.

    2018-01-01

    The study aimed to investigate the learning styles and multiple intelligences of English as foreign language (EFL) college-level students. "Convenience sampling" (Patton, 2015) was used to collect data from a population of 250 students enrolled in seven different academic departments at the College of Basic Education in Kuwait. The data…

  12. A Conceptual Framework for Error Remediation with Multiple External Representations Applied to Learning Objects

    Science.gov (United States)

    Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim

    2014-01-01

    This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…

  13. Investigating the Role of Multiple Intelligences in Determining Vocabulary Learning Strategies for L2 Learners

    Science.gov (United States)

    Sistani, Mahsa; Hashemian, Mahmood

    2016-01-01

    This study, first, examined whether there was any relationship between Iranian L2 learners' vocabulary learning strategies (VLSs), on the one hand, and their multiple intelligences (MI) types, on the other hand. In so doing, it explored the extent to which MI would predict L2 learners' VLSs. To these ends, 40 L2 learners from Isfahan University of…

  14. Learner Differences among Children Learning a Foreign Language: Language Anxiety, Strategy Use, and Multiple Intelligences

    Science.gov (United States)

    Liu, Hui-ju; Chen, Ting-Han

    2014-01-01

    This study mainly investigates language anxiety and its relationship to the use of learning strategies and multiple intelligences among young learners in an EFL educational context. The participants were composed of 212 fifth- and sixth-graders from elementary schools in central Taiwan. Findings indicated that most participants generally…

  15. A Cross-Cultural Study of Taiwanese and Kuwaiti EFL Students' Learning Styles and Multiple Intelligences

    Science.gov (United States)

    Wu, Shu-hua; Alrabah, Sulaiman

    2009-01-01

    The purpose of the present study was to relate the findings of a survey of learning styles and multiple intelligences that was distributed among two different cultural groups of Freshman-level EFL students in Taiwan and Kuwait in order to confirm its consistency for developing teaching techniques appropriate for each group's general profiles. Data…

  16. The Relationship between Perceptual Learning Style Preferences and Multiple Intelligences among Iranian EFL Learners

    Science.gov (United States)

    Baleghizadeh, Sasan; Shayeghi, Rose

    2014-01-01

    The purpose of the present study is to investigate the relationships between preferences of Multiple Intelligences and perceptual/social learning styles. Two self-report questionnaires were administered to a total of 207 male and female participants. Pearson correlation results revealed statistically significant positive relations between…

  17. The Role of CLEAR Thinking in Learning Science from Multiple-Document Inquiry Tasks

    Science.gov (United States)

    Griffin, Thomas D.; Wiley, Jennifer; Britt, M. Anne; Salas, Carlos R.

    2012-01-01

    The main goal for the current study was to investigate whether individual differences in domain-general thinking dispositions might affect learning from multiple-document inquiry tasks in science. Middle school students were given a set of documents and were tasked with understanding how and why recent patterns in global temperature might be…

  18. Learning with Multiple Representations: An Example of a Revision Lesson in Mathematics

    Science.gov (United States)

    Wong, Darren; Poo, Sng Peng; Hock, Ng Eng; Kang, Wee Loo

    2011-01-01

    We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion…

  19. Helping Children Learn Mathematics through Multiple Intelligences and Standards for School Mathematics.

    Science.gov (United States)

    Adams, Thomasenia Lott

    2001-01-01

    Focuses on the National Council of Teachers of Mathematics 2000 process-oriented standards of problem solving, reasoning and proof, communication, connections, and representation as providing a framework for using the multiple intelligences that children bring to mathematics learning. Presents ideas for mathematics lessons and activities to…

  20. An explicit statistical model of learning lexical segmentation using multiple cues

    NARCIS (Netherlands)

    Çöltekin, Ça ̆grı; Nerbonne, John; Lenci, Alessandro; Padró, Muntsa; Poibeau, Thierry; Villavicencio, Aline

    2014-01-01

    This paper presents an unsupervised and incremental model of learning segmentation that combines multiple cues whose use by children and adults were attested by experimental studies. The cues we exploit in this study are predictability statistics, phonotactics, lexical stress and partial lexical

  1. Touching Mercury in Community Media: Identifying Multiple Literacy Learning through Digital Arts Production

    Science.gov (United States)

    Arndt, Angela E.

    2011-01-01

    Educational paradigm shifts call for 21st century learners to possess the knowledge, skills, abilities, values, and experiences associated with multiple forms of literacy in a participatory learning culture. Contemporary educational systems are slow to adapt. Outside of school, people have to be self-motivated and have access to resources in order…

  2. Using Combinatorial Approach to Improve Students' Learning of the Distributive Law and Multiplicative Identities

    Science.gov (United States)

    Tsai, Yu-Ling; Chang, Ching-Kuch

    2009-01-01

    This article reports an alternative approach, called the combinatorial model, to learning multiplicative identities, and investigates the effects of implementing results for this alternative approach. Based on realistic mathematics education theory, the new instructional materials or modules of the new approach were developed by the authors. From…

  3. First instance competence of the Higher Administrative Court

    International Nuclear Information System (INIS)

    Anon.

    1988-01-01

    (1) An interlocutory judgement can determine the admissibility of a legal action, also with regard to single procedural prerequisites (following BVerwG decision 14, 273). (2) The first instance competence for disputes about the dismantling of a decommissioned nuclear installation lies with the administrative courts and not with the higher administrative courts. Federal Administrative Court, decision of May 19, 1988 - 7 C 43.88 - (VGH Munich). (orig.) [de

  4. SIFT Meets CNN: A Decade Survey of Instance Retrieval.

    Science.gov (United States)

    Zheng, Liang; Yang, Yi; Tian, Qi

    2018-05-01

    In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  7. ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

    KAUST Repository

    Wu, Baoyuan; Lyu, Siwei; Ghanem, Bernard

    2015-01-01

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some

  8. Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

    KAUST Repository

    Wu, Baoyuan; Jia, Fan; Liu, Wei; Ghanem, Bernard; Lyu, Siwei

    2018-01-01

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e., some

  9. Music mnemonics aid Verbal Memory and Induce Learning - Related Brain Plasticity in Multiple Sclerosis.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey's auditory verbal learning test. We defined the "learning-related synchronization" (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances "deep encoding" during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS.

  10. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

    Science.gov (United States)

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

  11. Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

    Science.gov (United States)

    Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

    2017-08-01

    Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7Mental Modelling Ability (M-MMA) for 3Mental Modelling Ability (L-MMA) for 0 ≤ x ≤ 3 score. The result shows that problem solving based learning model with multiple representations approach can be an alternative to be applied in improving students' MMA.

  12. Le contentieux camerounais devant les instances sportives internationales

    Directory of Open Access Journals (Sweden)

    Dikoume François Claude

    2016-01-01

    Les acteurs du sport depuis lors, utilisent donc les voies de recours au niveau international soit vers les fédérations sportives internationales ou encore et surtout vers le TAS qui s'occupe des litiges de toutes les disciplines sportives. Il est question ici de faire un inventaire casuistique descriptif non exhaustif des requêtes contentieuses camerounaises portées devant les diverses instances sportives internationales ; ceci permettra de questionner l'esprit processuel et la qualité technique de leurs réclamations juridiques en matière sportive.

  13. Learning with multiple representations: an example of a revision lesson in mechanics

    Science.gov (United States)

    Wong, Darren; Poo, Sng Peng; Eng Hock, Ng; Loo Kang, Wee

    2011-03-01

    We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion using various representations: motion diagrams, vector diagrams, free-body diagrams, verbal description, equations and graphs, drawn against time as well as against displacement. Overall, feedback from students about the lesson was positive. We further discuss the benefits of using computer simulation to support and extend student learning.

  14. A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

    Institute of Scientific and Technical Information of China (English)

    Duksan Ryu; Jong-In Jang; Jongmoon Baik; Member; ACM; IEEE

    2015-01-01

    Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires suffcient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via na¨ıve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

  15. Instance selection in digital soil mapping: a study case in Rio Grande do Sul, Brazil

    Directory of Open Access Journals (Sweden)

    Elvio Giasson

    2015-09-01

    Full Text Available A critical issue in digital soil mapping (DSM is the selection of data sampling method for model training. One emerging approach applies instance selection to reduce the size of the dataset by drawing only relevant samples in order to obtain a representative subset that is still large enough to preserve relevant information, but small enough to be easily handled by learning algorithms. Although there are suggestions to distribute data sampling as a function of the soil map unit (MU boundaries location, there are still contradictions among research recommendations for locating samples either closer or more distant from soil MU boundaries. A study was conducted to evaluate instance selection methods based on spatially-explicit data collection using location in relation to soil MU boundaries as the main criterion. Decision tree analysis was performed for modeling digital soil class mapping using two different sampling schemes: a selecting sampling points located outside buffers near soil MU boundaries, and b selecting sampling points located within buffers near soil MU boundaries. Data was prepared for generating classification trees to include only data points located within or outside buffers with widths of 60, 120, 240, 360, 480, and 600m near MU boundaries. Instance selection methods using both spatial selection of methods was effective for reduced size of the dataset used for calibrating classification tree models, but failed to provide advantages to digital soil mapping because of potential reduction in the accuracy of classification tree models.

  16. The Role of CLEAR Thinking in Learning Science from Multiple-Document Inquiry Tasks

    Directory of Open Access Journals (Sweden)

    Thomas D. GRIFFIN

    2012-10-01

    Full Text Available The main goal for the current study was to investigate whether individual differences in domaingeneral thinking dispositions might affect learning from multiple-document inquiry tasks in science.Middle school students were given a set of documents and were tasked with understanding how and why recent patterns in global temperature might be different from what has been observed in the past from those documents. Understanding was assessed with two measures: an essay task and an inference verification task. Domain-general thinking dispositions were assessed with a Commitment to Logic, Evidence, and Reasoning (CLEAR thinking scale. The measures of understanding wereuniquely predicted by both reading skills and CLEAR thinking scores, and these effects were not attributable to prior knowledge or interest. The results suggest independent roles for thinkingdispositions and reading ability when students read to learn from multiple-document inquiry tasks in science.

  17. Development and validity of mathematical learning assessment instruments based on multiple intelligence

    Directory of Open Access Journals (Sweden)

    Helmiah Suryani

    2017-06-01

    Full Text Available This study was aimed to develop and produce an assessment instrument of mathematical learning results based on multiple intelligence. The methods in this study used Borg & Gall-Research and Development approach (Research & Development. The subject of research was 289 students. The results of research: (1 Result of Aiken Analysis showed 58 valid items were between 0,714 to 0,952. (2 Result of the Exploratory on factor analysis indicated the instrument consist of three factors i.e. mathematical logical intelligence-spatial intelligence-and linguistic intelligence. KMO value was 0.661 df 0.780 sig. 0.000 with valid category. This research succeeded to developing the assessment instrument of mathematical learning results based on multiple intelligence of second grade in elementary school with characteristics of logical intelligence of mathematics, spatial intelligence, and linguistic intelligence.

  18. Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

    Science.gov (United States)

    Yuan, Chengzhi; Licht, Stephen; He, Haibo

    2017-09-26

    In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

  19. MULTIPLE INTELLIGENCE THEORY AND FOREIGN LANGUAGE LEARNING:A BRAIN-BASED PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Jane Arnold

    2004-06-01

    Full Text Available Gardner's Multiple Intelligences theory is presented as a cognitive perspective on intelligence which has profound implications for education in general. More specifically, it has led to the application of eight of these frames to language teaching and learning. In this chapter, we will argue in favour of the application of MIT to the EFL classroom, using as support some of the major insights for language teaching from brain science.

  20. Attributional Style and Depression in Multiple Sclerosis: The Learned Helplessness Model

    OpenAIRE

    Vargas, Gray A.; Arnett, Peter A.

    2013-01-01

    Several etiologic theories have been proposed to explain depression in the general population. Studying these models and modifying them for use in the multiple sclerosis (MS) population may allow us to better understand depression in MS. According to the reformulated learned helplessness (LH) theory, individuals who attribute negative events to internal, stable, and global causes are more vulnerable to depression. This study differentiated attributional style that was or was not related to MS...

  1. Learned helplessness, discouraged workers, and multiple unemployment equilibria in a search model

    OpenAIRE

    Bjørnstad, Roger

    2001-01-01

    Abstract: Unemployment varies strongly between countries with comparable economic structure. Some economists have tried to explain these differences with institutional differences in the labour market. Instead, this paper focuses on a model with multiple equilibria so that the same socioeconomic structure can give rise to different levels of unemployment. Unemployed workers' search efficiency are modelled within an equilibrium search model and lay behind these results. In the model learned...

  2. Exploration of machine learning techniques in predicting multiple sclerosis disease course

    OpenAIRE

    Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

    2017-01-01

    Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

  3. Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

    Science.gov (United States)

    Chen, Tianle; Zeng, Donglin

    2015-01-01

    Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

  4. The application of multiple intelligence approach to the learning of human circulatory system

    Science.gov (United States)

    Kumalasari, Lita; Yusuf Hilmi, A.; Priyandoko, Didik

    2017-11-01

    The purpose of this study is to offer an alternative teaching approach or strategies which able to accommodate students’ different ability, intelligence and learning style. Also can gives a new idea for the teacher as a facilitator for exploring how to teach the student in creative ways and more student-center activities, for a lesson such as circulatory system. This study was carried out at one private school in Bandung involved eight students to see their responses toward the lesson that delivered by using Multiple Intelligence approach which is include Linguistic, Logical-Mathematical, Visual-Spatial, Musical, Bodily-Kinesthetic, Interpersonal, Intrapersonal, and Naturalistic. Students were test by using MI test based on Howard Gardner’s MI model to see their dominant intelligence. The result showed the percentage of top three ranks of intelligence are Bodily-Kinesthetic (73%), Visual-Spatial (68%), and Logical-Mathematical (61%). The learning process is given by using some different multimedia and activities to engaged their learning style and intelligence such as mini experiment, short clip, and questions. Student response is given by using self-assessment and the result is all students said the lesson gives them a knowledge and skills that useful for their life, they are clear with the explanation given, they didn’t find difficulties to understand the lesson and can complete the assignment given. At the end of the study, it is reveal that the students who are learned by Multiple Intelligence instructional approach have more enhance to the lesson given. It’s also found out that the students participated in the learning process which Multiple Intelligence approach was applied enjoyed the activities and have great fun.

  5. Basic life support and children with profound and multiple learning disabilities.

    Science.gov (United States)

    Cash, Stefan; Shinnick-Page, Andrea

    2008-10-01

    Nurses and other carers of people with learning disabilities must be able to manage choking events and perform basic life support effectively. UK guidelines for assessment of airway obstruction and for resuscitation do not take account of the specific needs of people with profound multiple learning disability. For example, they fail to account for inhibited gag and coughing reflexes, limited body movements or chest deformity. There are no national guidelines to assist in clinical decisions and training for nurses and carers. Basic life support training for students of learning disability nursing at Birmingham City University is supplemented to address these issues. The authors ask whether such training should be provided for all nurses including those caring for children and young people. They also invite comment and discussion on questions related to chest compression and training in basic life support for a person in a seated position.

  6. External validity of individual differences in multiple cue probability learning: The case of pilot training

    Directory of Open Access Journals (Sweden)

    Nadine Matton

    2013-09-01

    Full Text Available Individuals differ in their ability to deal with unpredictable environments. Could impaired performances on learning an unpredictable cue-criteria relationship in a laboratory task be associated with impaired learning of complex skills in a natural setting? We focused on a multiple-cue probability learning (MCPL laboratory task and on the natural setting of pilot training. We used data from three selection sessions and from the three corresponding selected pilot student classes of a national airline pilot selection and training system. First, applicants took an MCPL task at the selection stage (N=556; N=701; N=412. Then, pilot trainees selected from the applicant pools (N=44; N=60; N=28 followed the training for 2.5 to 3 yrs. Differences in final MCPL performance were associated with pilot training difficulties. Indeed, poor MCPL performers experienced almost twice as many pilot training difficulties as better MCPL performers (44.0% and 25.0%, respectively.

  7. A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

    Science.gov (United States)

    Tsumori, Kenji; Ozawa, Seiichi

    When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  8. Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

    Science.gov (United States)

    Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

    2018-03-01

    Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

  9. Integrated model of multiple kernel learning and differential evolution for EUR/USD trading.

    Science.gov (United States)

    Deng, Shangkun; Sakurai, Akito

    2014-01-01

    Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

  10. Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading

    Directory of Open Access Journals (Sweden)

    Shangkun Deng

    2014-01-01

    Full Text Available Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL with differential evolution (DE for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI, while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

  11. Which is the best intrinsic motivation signal for learning multiple skills?

    Directory of Open Access Journals (Sweden)

    Vieri Giuliano Santucci

    2013-11-01

    Full Text Available Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e. motivations not connected to reward-related stimuli play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a cinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show a that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and b that the stronger the link between the IM signal and the competence of the system, the better the performance.

  12. Expressions of multiple neuronal dynamics during sensorimotor learning in the motor cortex of behaving monkeys.

    Directory of Open Access Journals (Sweden)

    Yael Mandelblat-Cerf

    Full Text Available Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis, during adaptation to force-field perturbations. Perturbed trials (reaching to one direction were practiced along with unperturbed trials (to other directions. The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.

  13. Breaking Newton’s third law: electromagnetic instances

    International Nuclear Information System (INIS)

    Kneubil, Fabiana B

    2016-01-01

    In this work, three instances are discussed within electromagnetism which highlight failures in the validity of Newton’s third law, all of them related to moving charged particles. It is well known that electromagnetic theory paved the way for relativity and that it disclosed new phenomena which were not compatible with the laws of mechanics. However, even if widely known in its generality, this issue is not clearly approached in introductory textbooks and it is difficult for students to perceive by themselves. Three explicit concrete situations involving the breaking of Newton’s third law are presented in this paper, together with a didactical procedure to construct graphically the configurations of electric field lines, which allow pictures produced by interactive radiation simulators available in websites to be better understood. (paper)

  14. Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations

    Energy Technology Data Exchange (ETDEWEB)

    Tamagnini, Paolo; Krause, Josua W.; Dasgupta, Aritra; Bertini, Enrico

    2017-05-14

    To realize the full potential of machine learning in diverse real- world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytic interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treating a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.

  15. Multiple kernel learning using single stage function approximation for binary classification problems

    Science.gov (United States)

    Shiju, S.; Sumitra, S.

    2017-12-01

    In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

  16. Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

    Science.gov (United States)

    Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

    2014-01-01

    Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey’s auditory verbal learning test. We defined the “learning-related synchronization” (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances “deep encoding” during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS. PMID:24982626

  17. CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Q. Wang

    2017-10-01

    Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

  18. Design of multiple representations e-learning resources based on a contextual approach for the basic physics course

    Science.gov (United States)

    Bakri, F.; Muliyati, D.

    2018-05-01

    This research aims to design e-learning resources with multiple representations based on a contextual approach for the Basic Physics Course. The research uses the research and development methods accordance Dick & Carey strategy. The development carried out in the digital laboratory of Physics Education Department, Mathematics and Science Faculty, Universitas Negeri Jakarta. The result of the process of product development with Dick & Carey strategy, have produced e-learning design of the Basic Physics Course is presented in multiple representations in contextual learning syntax. The appropriate of representation used in the design of learning basic physics include: concept map, video, figures, data tables of experiment results, charts of data tables, the verbal explanations, mathematical equations, problem and solutions example, and exercise. Multiple representations are presented in the form of contextual learning by stages: relating, experiencing, applying, transferring, and cooperating.

  19. Integrating Multiple Intelligences and Learning Styles on Solving Problems, Achievement in, and Attitudes towards Math in Six Graders with Learning Disabilities in Cooperative Groups

    Science.gov (United States)

    Eissa, Mourad Ali; Mostafa, Amaal Ahmed

    2013-01-01

    This study investigated the effect of using differentiated instruction by integrating multiple intelligences and learning styles on solving problems, achievement in, and attitudes towards math in six graders with learning disabilities in cooperative groups. A total of 60 students identified with LD were invited to participate. The sample was…

  20. Multimodality and Children's Participation in Classrooms: Instances of Research

    Science.gov (United States)

    Newfield, Denise

    2011-01-01

    This paper describes how language and literacy classrooms became more participatory, agentive spaces through addressing a central issue in teaching and learning: the forms of representation through which children make their meanings. It reconsiders pedagogic research in under-resourced Gauteng classrooms during the period 1994-2005, during the…

  1. Multiple metal accumulation as a factor in learning achievement within various New Orleans elementary school communities

    International Nuclear Information System (INIS)

    Mielke, H.W.; Berry, K.J..; Mielke, P.W.; Powell, E.T.; Gonzales, C.R.

    2005-01-01

    In New Orleans, the elementary school system is divided into attendance districts with established boundaries that define student enrollment among schools. This study concerns environmental quality as defined by amount of soil metals (Pb, Zn, Cd, Ni, Mn, Cu, Co, Cr, and V) in attendance district elementary school communities (n=111) paired with learning achievement as measured by individual test scores (n=32,741) of students enrolled at each school. The Louisiana Educational Assessment Program (LEAP) 4th grade scores measure learning achievement for English language arts, social studies, mathematics, and science. The best fit between environmental quality and higher learning achievement is found to be inversely associated with the sum of the metals or multiple metal accumulations (MMA) in New Orleans communities. The P values for MMA partitions for ELA, SOC, MAT, and SCI are 0.57x10 -7 , 0.29x10 -8 , 0.41x10 -6 , and 0.17x10 -8 , respectively. Efforts to prevent childhood metal exposure should improve New Orleanians' learning achievement as measured by the LEAP scores and thereby enhance the socioeconomic situation in contaminated communities. This study establishes global relationships between LEAP scores in schools and soil metal concentrations in school neighborhoods. However, these data do not allow relating of the LEAP scores with metal levels for individual students

  2. Marx, Materialism and the Brain: Determination in the Last Instance?

    Directory of Open Access Journals (Sweden)

    Joss Hands

    2018-05-01

    Full Text Available It is well acknowledged that there is not one but many Marxes, and one area where this has been most evident is in the question of technological and economic determinism. This article traces some key moments in this debate and attempts to locate their most recent iteration in disagreements over the place of the human brain in both historical agency and value creation in so called ‘cognitive’ or ‘post-Fordist’ capitalism. Of significant interest in the current configuration – or rather composition – of capital is the place of the digitisation of the labour process and its relation to, and integration with, human cognition and volition. Arguments over the attention economy and the power of post-Fordist capitalism to distract and direct is a significant variation of the question of ideology and the latest variation of the base/superstructure debate. This article will unpack the aforesaid issues to offer an articulated perspective in order to make the argument that taking a balanced view of determination will allow us to acknowledge that – drawing on the argument of determination in the last instance – we can hold both of these ‘Marxes’ to be simultaneously valid. Here, a revisiting of Marx’s concept of General Intellect will be undertaken, wherein the productive capacity of living labour is employed in both active agency and the capture of value, in which the plasticity of the living brain becomes the pivot point for both exploitation by, and resistance to, capital.

  3. Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.

    Science.gov (United States)

    Li, Ke; Pang, Kaiyue; Song, Yi-Zhe; Hospedales, Timothy M; Xiang, Tao; Zhang, Honggang

    2017-08-25

    We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address these challenges, we propose to bridge the image-sketch gap both at the high-level via parts and attributes, as well as at the low-level, via introducing a new domain alignment method. More specifically, (i) we contribute a dataset with 304 photos and 912 sketches, where each sketch and image is annotated with its semantic parts and associated part-level attributes. With the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and provide pose-aligned sketch-image comparisons. To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better align the domains. Finally (iv) these are combined in a matching framework integrating aligned low-level features, mid-level geometric structure and high-level semantic attributes. Extensive experiments conducted on our new dataset demonstrate effectiveness of the proposed method.

  4. The role of multiple neuromodulators in reinforcement learning that is based on competition between eligibility traces

    Directory of Open Access Journals (Sweden)

    Marco A Huertas

    2016-12-01

    neuromodulators for expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different neuromodulators contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

  5. The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

    Science.gov (United States)

    Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

    2016-01-01

    expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different these contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

  6. An Empirical Examination of the Association between Multiple Intelligences and Language Learning Self-Efficacy among TEFL University Students

    Science.gov (United States)

    Moafian, Fatemeh; Ebrahimi, Mohammad Reza

    2015-01-01

    The current study investigated the association between multiple intelligences and language learning efficacy expectations among TEFL (Teaching English as a Foreign Language) university students. To fulfill the aim of the study, 108 junior and senior TEFL students were asked to complete the "Multiple Intelligence Developmental Assessment…

  7. An Examination of Multiple Intelligence Domains and Learning Styles of Pre-Service Mathematics Teachers: Their Reflections on Mathematics Education

    Science.gov (United States)

    Ozgen, Kemal; Tataroglu, Berna; Alkan, Huseyin

    2011-01-01

    The present study aims to identify pre-service mathematics teachers' multiple intelligence domains and learning style profiles, and to establish relationships between them. Employing the survey model, the study was conducted with the participation of 243 pre-service mathematics teachers. The study used the "multiple intelligence domains…

  8. DL-sQUAL: A Multiple-Item Scale for Measuring Service Quality of Online Distance Learning Programs

    Science.gov (United States)

    Shaik, Naj; Lowe, Sue; Pinegar, Kem

    2006-01-01

    Education is a service with multiplicity of student interactions over time and across multiple touch points. Quality teaching needs to be supplemented by consistent quality supporting services for programs to succeed under the competitive distance learning landscape. ServQual and e-SQ scales have been proposed for measuring quality of traditional…

  9. Academic Achievement from Using the Learning Medium Via a Tablet Device Based on Multiple Intelligences in Grade 1 Elementary Student.

    Science.gov (United States)

    Nuallaong, Winitra; Nuallaong, Thanya; Preechadirek, Nongluck

    2015-04-01

    To measure academic achievement of the multiple intelligence-based learning medium via a tablet device. This is a quasi-experimental research study (non-randomized control group pretest-posttest design) in 62 grade 1 elementary students (33 males and 29 females). Thirty-one students were included in an experimental group using purposive sampling by choosing a student who had highest multiple intelligence test scores in logical-mathematic. Then, this group learned by the new learning medium via a tablet which the application matched to logical-mathematic multiple intelligence. Another 31 students were included in a control group using simple random sampling and then learning by recitation. Both groups did pre-test and post-test vocabulary. Thirty students in the experimental group and 24 students in the control group increased post-test scores (odds ratio = 8.75). Both groups made significant increasing in post-test scores. The experimental group increased 9.07 marks (95% CI 8.20-9.93) significantly higher than the control group which increased 4.39 marks (95% CI 3.06-5.72) (t = -6.032, df = 51.481, p learning from either multiple intelligence-based learning medium via a tablet or recitation can contribute academic achievement, learningfrom the new medium contributed more achievement than recitation. The new learning medium group had higher post-test scores 8.75 times than the recitation group. Therefore, the new learning medium is more effective than the traditional recitation in terms of academic achievement. This study has limitations because samples came from the same school. However, the previous study in Thailand did notfind a logical-mathematical multiple intelligence difference among schools. In the future, long-term research to find how the new learning medium affects knowledge retention will support the advantage for life-long learning.

  10. Variety Preserved Instance Weighting and Prototype Selection for Probabilistic Multiple Scope Simulations

    Science.gov (United States)

    2017-05-30

    Mailing Address : 8-1, Mihogaoka, Ibarakishi, Osaka, 567-0047, Japan - Phone : +81-6-6879-8540 - Fax : +81-6-6879-8544 Period of Performance: April...distribution of the original data set in a compact fashion by the nature of these measures. In the final year, we worked on the goals (3) and (4). For...measures and preserve the varieties of the distribution of the original data set in compact fashions by the nature of these measures. Figure 2

  11. A recommendation system for predicting risks across multiple business process instances

    NARCIS (Netherlands)

    Conforti, R.; Leoni, de M.; La Rosa, M.; Aalst, van der W.M.P.; Hofstede, ter A.H.M.

    2014-01-01

    This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a

  12. A recommendation system for predicting risks across multiple business process instances

    NARCIS (Netherlands)

    Conforti, R.; Leoni, de M.; La Rosa, M.; Aalst, van der W.M.P.; Hofstede, ter A.H.M.

    2015-01-01

    This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a

  13. Examining the benefits of combining two learning strategies on recall of functional information in persons with multiple sclerosis.

    Science.gov (United States)

    Goverover, Yael; Basso, Michael; Wood, Hali; Chiaravalloti, Nancy; DeLuca, John

    2011-12-01

    Forgetfulness occurs commonly in people with multiple sclerosis (MS), but few treatments alleviate this problem. This study examined the combined effect of two cognitive rehabilitation strategies to improve learning and memory in MS: self-generation and spaced learning. The hypothesis was that the combination of spaced learning and self-generation would yield better learning and memory recall performance than spaced learning alone. Using a within groups design, 20 participants with MS and 18 healthy controls (HC) were presented with three tasks (learning names, appointment, and object location), each in three learning conditions (Massed, Spaced Learning, and combination of spaced and generated information). Participants were required to recall the information they learned in each of these conditions immediately and 30 min following the initial presentation. The combination of spaced learning and self-generation yielded better recall than did spaced learning alone. In turn, spaced learning resulted in better recall than the massed rehearsal condition. These findings reveal that the combination of these two learning strategies may possess utility as a cognitive rehabilitation strategy.

  14. Competence-based multiple learning paths: on the road of implementation

    Directory of Open Access Journals (Sweden)

    María Dolores de Prada

    2014-12-01

    Full Text Available This article presents the results of an action-research implementation project based on a system that weaves together five different routes to facilitate the development of competences through the use of multiple learning paths for primary and secondary teachers. The first and initial results that the article deals with relate to the experience of math teachers for ages 11 to 14. Other levels and other fields are in the process of being developed. The article deals briefly with the justification, the background and the fundamental principles that underpin the research methodology and introduces a number of elements such as the method followed by the research, the resources and the materials used as well as the results obtained at the end of the second year of this experience. It also justifies the model chosen and the criteria and strategies selected for its reliability and verification. In addition, it provides significant elements of reflection about a number of burning issues: The development of a new profile of the “teacher” in a studentcentred system and the implementation system to be followed, the importance of multiple but integrated learning paths and the relevance as well as the reflection on real cases of competence evaluation.

  15. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. A Multiphysics Framework to Learn and Predict in Presence of Multiple Scales

    Science.gov (United States)

    Tomin, P.; Lunati, I.

    2015-12-01

    Modeling complex phenomena in the subsurface remains challenging due to the presence of multiple interacting scales, which can make it impossible to focus on purely macroscopic phenomena (relevant in most applications) and neglect the processes at the micro-scale. We present and discuss a general framework that allows us to deal with the situation in which the lack of scale separation requires the combined use of different descriptions at different scale (for instance, a pore-scale description at the micro-scale and a Darcy-like description at the macro-scale) [1,2]. The method is based on conservation principles and constructs the macro-scale problem by numerical averaging of micro-scale balance equations. By employing spatiotemporal adaptive strategies, this approach can efficiently solve large-scale problems [2,3]. In addition, being based on a numerical volume-averaging paradigm, it offers a tool to illuminate how macroscopic equations emerge from microscopic processes, to better understand the meaning of microscopic quantities, and to investigate the validity of the assumptions routinely used to construct the macro-scale problems. [1] Tomin, P., and I. Lunati, A Hybrid Multiscale Method for Two-Phase Flow in Porous Media, Journal of Computational Physics, 250, 293-307, 2013 [2] Tomin, P., and I. Lunati, Local-global splitting and spatiotemporal-adaptive Multiscale Finite Volume Method, Journal of Computational Physics, 280, 214-231, 2015 [3] Tomin, P., and I. Lunati, Spatiotemporal adaptive multiphysics simulations of drainage-imbibition cycles, Computational Geosciences, 2015 (under review)

  17. Automatic provisioning, deployment and orchestration for load-balancing THREDDS instances

    Science.gov (United States)

    Cofino, A. S.; Fernández-Tejería, S.; Kershaw, P.; Cimadevilla, E.; Petri, R.; Pryor, M.; Stephens, A.; Herrera, S.

    2017-12-01

    THREDDS is a widely used web server to provide to different scientific communities with data access and discovery. Due to THREDDS's lack of horizontal scalability and automatic configuration management and deployment, this service usually deals with service downtimes and time consuming configuration tasks, mainly when an intensive use is done as is usual within the scientific community (e.g. climate). Instead of the typical installation and configuration of a single or multiple independent THREDDS servers, manually configured, this work presents an automatic provisioning, deployment and orchestration cluster of THREDDS servers. This solution it's based on Ansible playbooks, used to control automatically the deployment and configuration setup on a infrastructure and to manage the datasets available in THREDDS instances. The playbooks are based on modules (or roles) of different backends and frontends load-balancing setups and solutions. The frontend load-balancing system enables horizontal scalability by delegating requests to backend workers, consisting in a variable number of instances for the THREDDS server. This implementation allows to configure different infrastructure and deployment scenario setups, as more workers are easily added to the cluster by simply declaring them as Ansible variables and executing the playbooks, and also provides fault-tolerance and better reliability since if any of the workers fail another instance of the cluster can take over it. In order to test the solution proposed, two real scenarios are analyzed in this contribution: The JASMIN Group Workspaces at CEDA and the User Data Gateway (UDG) at the Data Climate Service from the University of Cantabria. On the one hand, the proposed configuration has provided CEDA with a higher level and more scalable Group Workspaces (GWS) service than the previous one based on Unix permissions, improving also the data discovery and data access experience. On the other hand, the UDG has improved its

  18. Extinction produces context inhibition and multiple-context extinction reduces response recovery in human predictive learning.

    Science.gov (United States)

    Glautier, Steven; Elgueta, Tito; Nelson, James Byron

    2013-12-01

    Two experiments with human participants were used to investigate recovery of an extinguished learned response after a context change using ABC designs. In an ABC design, the context changes over the three successive stages of acquisition (context A), extinction (context B), and test (context C). In both experiments, we found reduced recovery in groups that had extinction in multiple contexts, and that the extinction contexts acquired inhibitory strength. These results confirm those of previous investigations, that multiple-context extinction can produce less response recovery than single-context extinction, and they also provide new evidence for the involvement of contextual inhibitory processes in extinction in humans. The foregoing results are broadly in line with a protection-from-extinction account of response recovery. Yet, despite the fact that we detected contextual inhibition, predictions based on protection-from-extinction were not fully reliable for the single- and multiple-context group differences that we observed in (1) rates of extinction and (2) the strength of context inhibition. Thus, although evidence was obtained for a protection-from-extinction account of response recovery, this account can not explain all of the data.

  19. Multiple Learning Approaches in the Professional Development of School Leaders -- Theoretical Perspectives and Empirical Findings on Self-assessment and Feedback

    Science.gov (United States)

    Huber, Stephan Gerhard

    2013-01-01

    This article investigates the use of multiple learning approaches and different modes and types of learning in the (continuous) professional development (PD) of school leaders, particularly the use of self-assessment and feedback. First, formats and multiple approaches to professional learning are described. Second, a possible approach to…

  20. Learning styles and types of multiple intelligences in dental students in their first and tenth semester. Monterrey, Mexico, 2015.

    Directory of Open Access Journals (Sweden)

    Juan Solís-Soto

    2016-04-01

    Full Text Available Introduction: Nowadays the incorporation and validation of learning styles and multiple intelligences enable teachers to obtain positive results in academic performance. This new approach has allowed to appreciate personal differences in dental students and strengthen their underdeveloped aspects, improving teaching and learning skills. Objective: To compare learning styles and multiple intelligences in a sample of Mexican dental students in their first and tenth semester. Materials and Methods: A cross-sectional study using questionnaires on learning styles (Honey-Alonso and Gardner’s multiple intelligences was performed. The study was applied to 123 students in their first semester and 157 in their tenth semester at the School of Dentistry at Universidad Autónoma de Nuevo León, evaluating differences between age and sex. Results: Logical-Mathematical intelligence (p=0.044 and Kinesthetic-Corporal intelligence (p=0.042 showed significant differences between students of both semesters, with intrapersonal and interpersonal intelligences being more prevalent. Within learning styles, the prevalent were Reflexive and Theoretical, showing a significant difference between semesters (p=0.005. Conclusion: The most prevalent learning styles in both groups were Reflexive and Theoretical, with no difference between both sexes. The most prevalent types of multiple intelligences in both sexes and groups were interpersonal and intrapersonal.

  1. An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

    OpenAIRE

    Seddiqui, Md. Hanif; Nath, Rudra Pratap Deb; Aono, Masaki

    2015-01-01

    The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidab...

  2. 28 CFR 51.46 - Reconsideration of objection at the instance of the Attorney General.

    Science.gov (United States)

    2010-07-01

    ... instance of the Attorney General. 51.46 Section 51.46 Judicial Administration DEPARTMENT OF JUSTICE... Processing of Submissions § 51.46 Reconsideration of objection at the instance of the Attorney General. (a... may be reconsidered, if it is deemed appropriate, at the instance of the Attorney General. (b) Notice...

  3. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Sun Xun

    2016-12-01

    Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

  4. Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.

    Science.gov (United States)

    Pham-The, Hai; Nam, Nguyen-Hai; Nga, Doan-Viet; Hai, Dang Thanh; Dieguez-Santana, Karel; Marrero-Poncee, Yovani; Castillo-Garit, Juan A; Casanola-Martin, Gerardo M; Le-Thi-Thu, Huong

    2018-02-09

    Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Lesion classification using clinical and visual data fusion by multiple kernel learning

    Science.gov (United States)

    Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

    2014-03-01

    To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

  6. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Science.gov (United States)

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  7. Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

    Science.gov (United States)

    Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

    2014-01-01

    Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

  8. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

    Science.gov (United States)

    Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

    2017-08-03

    Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

  9. Using reusable learning objects (rlos) in injection skills teaching: Evaluations from multiple user types.

    Science.gov (United States)

    Williams, Julia; O'Connor, Mórna; Windle, Richard; Wharrad, Heather J

    2015-12-01

    Clinical skills are a critical component of pre-registration nurse education in the United Kingdom, yet there is widespread concern about the clinical skills displayed by newly-qualified nurses. Novel means of supporting clinical skills education are required to address this. A package of Reusable Learning Objects (RLOs) was developed to supplement pre-registration teaching on the clinical skill of administering injection medication. RLOs are electronic resources addressing a single learning objective whose interactivity facilitates learning. This article evaluates a package of five injection RLOs across three studies: (1) questionnaires administered to pre-registration nursing students at University of Nottingham (UoN) (n=46) evaluating the RLO package as a whole; (2) individual RLOs evaluated in online questionnaires by educators and students from UoN; from other national and international institutions; and healthcare professionals (n=265); (3) qualitative evaluation of the RLO package by UoN injection skills tutors (n=6). Data from all studies were assessed for (1) access to, (2) usefulness, (3) impact and (4) integration of the RLOs. Study one found that pre-registration nursing students rate the RLO package highly across all categories, particularly underscoring the value of their self-test elements. Study two found high ratings in online assessments of individual RLOs by multiple users. The global reach is particularly encouraging here. Tutors reported insufficient levels of student-RLO access, which might be explained by the timing of their student exposure. Tutors integrate RLOs into teaching and agree on their use as teaching supplements, not substitutes for face-to-face education. This evaluation encompasses the first years postpackage release. Encouraging data on evaluative categories in this early review suggest that future evaluations are warranted to track progress as the package is adopted and evaluated more widely. Copyright © 2015 Elsevier Ltd

  10. Exposure to multiple cholinergic pesticides impairs olfactory learning and memory in honeybees.

    Science.gov (United States)

    Williamson, Sally M; Wright, Geraldine A

    2013-05-15

    Pesticides are important agricultural tools often used in combination to avoid resistance in target pest species, but there is growing concern that their widespread use contributes to the decline of pollinator populations. Pollinators perform sophisticated behaviours while foraging that require them to learn and remember floral traits associated with food, but we know relatively little about the way that combined exposure to multiple pesticides affects neural function and behaviour. The experiments reported here show that prolonged exposure to field-realistic concentrations of the neonicotinoid imidacloprid and the organophosphate acetylcholinesterase inhibitor coumaphos and their combination impairs olfactory learning and memory formation in the honeybee. Using a method for classical conditioning of proboscis extension, honeybees were trained in either a massed or spaced conditioning protocol to examine how these pesticides affected performance during learning and short- and long-term memory tasks. We found that bees exposed to imidacloprid, coumaphos, or a combination of these compounds, were less likely to express conditioned proboscis extension towards an odor associated with reward. Bees exposed to imidacloprid were less likely to form a long-term memory, whereas bees exposed to coumaphos were only less likely to respond during the short-term memory test after massed conditioning. Imidacloprid, coumaphos and a combination of the two compounds impaired the bees' ability to differentiate the conditioned odour from a novel odour during the memory test. Our results demonstrate that exposure to sublethal doses of combined cholinergic pesticides significantly impairs important behaviours involved in foraging, implying that pollinator population decline could be the result of a failure of neural function of bees exposed to pesticides in agricultural landscapes.

  11. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    OpenAIRE

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational app...

  12. USE OF MULTIPLE RESPONSE QUESTIONS (MRQS DURING LECTURE SESSIONS AS A TOOL TO ENHANCE LEARNING

    Directory of Open Access Journals (Sweden)

    Rosh

    2015-12-01

    Full Text Available INTRODUCTION Lecture classes are time tested solid method of teaching and have lot of advantages and few disadvantages. The main drawback is its unidirectional monotonous nature and many a time students fail to concentrate and understand especially when the sessions are long, and from the students’ point of view, many are boring too. Lecture sessions are still continued because of its various advantages. There are many methods tried to improve efficacy and effectiveness of lecture sessions including reinforcement, questions and discussions. There are many studies incorporating multiple choice questions (MCQs in lecture sessions for this purpose, with positive results. These sessions evoke creative thinking and enhance learning. For this purpose MCQs are to be prepared with care considering the areas to be covered. In order to make lecture classes more impressive, interesting and effective, we tried introducing a short multiple response session in between, along with some rewards for correct responses in terms of study materials. AIMS AND OBJECTIVES To study the impact of incorporation of MRQs during theory sessions to enhance the efficacy of teaching- learning process MATERIAL AND METHODS Study was conducted in a private medical college in Calicut. We surveyed 169 MBBS students initially with questionnaire covering various aspects of a lecture classes in general. For the next 6 months we incorporated MRQs in routine theory classes. Survey was then conducted again on the same group using same questionnaire and the results were compared. Scores were given according to performance, a maximum of 5 per question. RESULTS After 6 months the data showed substantial improvement in the understanding pattern of students. The average score regarding the usefulness increased from 3.57 to 3.91. After the intervention a substantial number agreed that the sessions have become more interesting, the score changed from 2.99 to 3.87. This also increased the

  13. No trade-off between learning speed and associative flexibility in bumblebees: a reversal learning test with multiple colonies.

    Directory of Open Access Journals (Sweden)

    Nigel E Raine

    Full Text Available Potential trade-offs between learning speed and memory-related performance could be important factors in the evolution of learning. Here, we test whether rapid learning interferes with the acquisition of new information using a reversal learning paradigm. Bumblebees (Bombus terrestris were trained to associate yellow with a floral reward. Subsequently the association between colour and reward was reversed, meaning bees then had to learn to visit blue flowers. We demonstrate that individuals that were fast to learn yellow as a predictor of reward were also quick to reverse this association. Furthermore, overnight memory retention tests suggest that faster learning individuals are also better at retaining previously learned information. There is also an effect of relatedness: colonies whose workers were fast to learn the association between yellow and reward also reversed this association rapidly. These results are inconsistent with a trade-off between learning speed and the reversal of a previously made association. On the contrary, they suggest that differences in learning performance and cognitive (behavioural flexibility could reflect more general differences in colony learning ability. Hence, this study provides additional evidence to support the idea that rapid learning and behavioural flexibility have adaptive value.

  14. Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations.

    Science.gov (United States)

    Grossberg, Stephen; Pilly, Praveen K

    2014-02-05

    A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

  15. Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

    Science.gov (United States)

    Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

    2018-02-01

    The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

  16. Scripted collaboration in serious gaming for complex learning: Effects of multiple perspectives when acquiring water management skills

    NARCIS (Netherlands)

    Hummel, Hans; Van Houcke, Jasper; Nadolski, Rob; Van der Hiele, Tony; Kurvers, Hub; Löhr, Ansje

    2010-01-01

    Hummel, H. G. K., Van Houcke, J., Nadolski, R. J., Van der Hiele, T., Kurvers, H., & Löhr, A. (2011). Scripted collaboration in gaming for complex learning: Effects of multiple perspectives when acquiring water management skills. British Journal of Educational Technology, 42(6),

  17. Effects of Multiple Intelligences Supported Project-Based Learning on Students' Achievement Levels and Attitudes towards English Lesson

    Science.gov (United States)

    Bas, Gökhan; Beyhan, Ömer

    2010-01-01

    The aim of the research was to investigate the effects of multiple intelligences supported project-based learning and traditional foreign language-teaching environment on students' achievement and their attitude towards English lesson. The research was carried out in 2009-2010 education-instruction year in Karatli Sehit Sahin Yilmaz Elementary…

  18. In the Context of Multiple Intelligences Theory, Intelligent Data Analysis of Learning Styles Was Based on Rough Set Theory

    Science.gov (United States)

    Narli, Serkan; Ozgen, Kemal; Alkan, Huseyin

    2011-01-01

    The present study aims to identify the relationship between individuals' multiple intelligence areas and their learning styles with mathematical clarity using the concept of rough sets which is used in areas such as artificial intelligence, data reduction, discovery of dependencies, prediction of data significance, and generating decision…

  19. Teacher Subject Specialisms and Their Relationships to Learning Styles, Psychological Types and Multiple Intelligences: Implications for Course Development

    Science.gov (United States)

    Perry, Chris; Ball, Ian

    2004-01-01

    This study explores issues in teacher education that increase our understanding of, and response to, the individual differences displayed by learners. A large undergraduate teacher education cohort provided evidence of the range and distribution of preferences in learning styles, psychological types and multiple intelligences. This information…

  20. Connections between Future Time Perspectives and Self-Regulated Learning for Mid-Year Engineering Students: A Multiple Case Study

    Science.gov (United States)

    Chasmar, Justine

    2017-01-01

    This dissertation presents multiple studies with the purpose of understanding the connections between undergraduate engineering students' motivations, specifically students' Future Time Perspectives (FTPs) and Self-Regulated Learning (SRL). FTP refers to the views students hold about the future and how their perceptions of current tasks are…

  1. An Enhanced Genetic Approach to Composing Cooperative Learning Groups for Multiple Grouping Criteria

    Science.gov (United States)

    Hwang, Gwo-Jen; Yin, Peng-Yeng; Hwang, Chi-Wei; Tsai, Chin-Chung

    2008-01-01

    Cooperative learning is known to be an effective educational strategy in enhancing the learning performance of students. The goal of a cooperative learning group is to maximize all members' learning efficacy. This is accomplished via promoting each other's success, through assisting, sharing, mentoring, explaining, and encouragement. To achieve…

  2. Effects of multiple intelligences supported project-based learning on students’ achievement levels and attitudes towards English lesson

    Directory of Open Access Journals (Sweden)

    Gökhan BAŞ

    2010-07-01

    Full Text Available The aim of the research was to investigate the effects of multiple intelligences supported project-based learning and traditional foreign language-teaching environment on students' achievement and their attitude towards English lesson. The research was carried out in 2009 – 2010 education-instruction year in Karatli Sehit Sahin Yilmaz Elementary School, Nigde, Turkey. Totally 50 students in two different classes in the 5th grade of this school participated in the study. The results of the research showed a significant difference between the attitude scores of the experiment group and the control group. It was also found out that the multiple intelligences approach activities were more effective in thepositive development of the students’ attitudes. At the end of the research, it is revealed that the students who are educated by multiple intelligences supported project-based learning method are more successful and have a higher motivation level than the studentswho are educated by the traditional instructional methods.

  3. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

    OpenAIRE

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed...

  4. Time efficient optimization of instance based problems with application to tone onset detection

    OpenAIRE

    Bauer, Nadja; Friedrichs, Klaus; Weihs, Claus

    2016-01-01

    A time efficient optimization technique for instance based problems is proposed, where for each parameter setting the target function has to be evaluated on a large set of problem instances. Computational time is reduced by beginning with a performance estimation based on the evaluation of a representative subset of instances. Subsequently, only promising settings are evaluated on the whole data set. As application a comprehensive music onset detection algorithm is introduce...

  5. Fast automated segmentation of multiple objects via spatially weighted shape learning

    Science.gov (United States)

    Chandra, Shekhar S.; Dowling, Jason A.; Greer, Peter B.; Martin, Jarad; Wratten, Chris; Pichler, Peter; Fripp, Jurgen; Crozier, Stuart

    2016-11-01

    Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice’s similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

  6. Learning from peer feedback on student-generated multiple choice questions: Views of introductory physics students

    Science.gov (United States)

    Kay, Alison E.; Hardy, Judy; Galloway, Ross K.

    2018-06-01

    PeerWise is an online application where students are encouraged to generate a bank of multiple choice questions for their classmates to answer. After answering a question, students can provide feedback to the question author about the quality of the question and the question author can respond to this. Student use of, and attitudes to, this online community within PeerWise was investigated in two large first year undergraduate physics courses, across three academic years, to explore how students interact with the system and the extent to which they believe PeerWise to be useful to their learning. Most students recognized that there is value in engaging with PeerWise, and many students engaged deeply with the system, thinking critically about the quality of their submissions and reflecting on feedback provided to them. Students also valued the breadth of topics and level of difficulty offered by the questions, recognized the revision benefits afforded by the resource, and were often willing to contribute to the community by providing additional explanations and engaging in discussion.

  7. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  8. Attributional style and depression in multiple sclerosis: the learned helplessness model.

    Science.gov (United States)

    Vargas, Gray A; Arnett, Peter A

    2013-01-01

    Several etiologic theories have been proposed to explain depression in the general population. Studying these models and modifying them for use in the multiple sclerosis (MS) population may allow us to better understand depression in MS. According to the reformulated learned helplessness (LH) theory, individuals who attribute negative events to internal, stable, and global causes are more vulnerable to depression. This study differentiated attributional style that was or was not related to MS in 52 patients with MS to test the LH theory in this population and to determine possible differences between illness-related and non-illness-related attributions. Patients were administered measures of attributional style, daily stressors, disability, and depressive symptoms. Participants were more likely to list non-MS-related than MS-related causes of negative events on the Attributional Style Questionnaire (ASQ), and more-disabled participants listed significantly more MS-related causes than did less-disabled individuals. Non-MS-related attributional style correlated with stress and depressive symptoms, but MS-related attributional style did not correlate with disability or depressive symptoms. Stress mediated the effect of non-MS-related attributional style on depressive symptoms. These results suggest that, although attributional style appears to be an important construct in MS, it does not seem to be related directly to depressive symptoms; rather, it is related to more perceived stress, which in turn is related to increased depressive symptoms.

  9. Exploration of machine learning techniques in predicting multiple sclerosis disease course.

    Directory of Open Access Journals (Sweden)

    Yijun Zhao

    Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

  10. Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

    Science.gov (United States)

    He, Xingyu; Tong, Ningning; Hu, Xiaowei

    2018-01-01

    Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation-maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

  11. Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

    Science.gov (United States)

    Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

    2010-01-01

    The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

  12. The Relationship between Multiplication Fact Speed-Recall and Fluency and Higher Level Mathematics Learning with Eighth Grade Middle School Students

    Science.gov (United States)

    Curry, Steven James

    2012-01-01

    This quantitative study investigated relationships between higher level mathematics learning and multiplication fact fluency, multiplication fact speed-recall, and reading grade equivalency of eighth grade students in Algebra I and Pre-Algebra. Higher level mathematics learning was indicated by an average score of 80% or higher on first and second…

  13. Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

    Science.gov (United States)

    Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

    2015-10-30

    Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Perceptual-motor skill learning in Gilles de la Tourette syndrome. Evidence for multiple procedural learning and memory systems.

    Science.gov (United States)

    Marsh, Rachel; Alexander, Gerianne M; Packard, Mark G; Zhu, Hongtu; Peterson, Bradley S

    2005-01-01

    Procedural learning and memory systems likely comprise several skills that are differentially affected by various illnesses of the central nervous system, suggesting their relative functional independence and reliance on differing neural circuits. Gilles de la Tourette syndrome (GTS) is a movement disorder that involves disturbances in the structure and function of the striatum and related circuitry. Recent studies suggest that patients with GTS are impaired in performance of a probabilistic classification task that putatively involves the acquisition of stimulus-response (S-R)-based habits. Assessing the learning of perceptual-motor skills and probabilistic classification in the same samples of GTS and healthy control subjects may help to determine whether these various forms of procedural (habit) learning rely on the same or differing neuroanatomical substrates and whether those substrates are differentially affected in persons with GTS. Therefore, we assessed perceptual-motor skill learning using the pursuit-rotor and mirror tracing tasks in 50 patients with GTS and 55 control subjects who had previously been compared at learning a task of probabilistic classifications. The GTS subjects did not differ from the control subjects in performance of either the pursuit rotor or mirror-tracing tasks, although they were significantly impaired in the acquisition of a probabilistic classification task. In addition, learning on the perceptual-motor tasks was not correlated with habit learning on the classification task in either the GTS or healthy control subjects. These findings suggest that the differing forms of procedural learning are dissociable both functionally and neuroanatomically. The specific deficits in the probabilistic classification form of habit learning in persons with GTS are likely to be a consequence of disturbances in specific corticostriatal circuits, but not the same circuits that subserve the perceptual-motor form of habit learning.

  15. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

    Science.gov (United States)

    Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

    2017-06-19

    A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

  16. Collaborative Learning Using a Project across Multiple Business Courses: A Cognitive Load and Knowledge Convergence Approach

    Science.gov (United States)

    Bhowmick, Sandeep; Chandra, Aruna; Harper, Jeffrey S.; Sweetin, Vernon

    2015-01-01

    Four business professors at a state university in the Midwestern United States launched a collaborative learning project grounded in cognitive learning theory and knowledge convergence theory with the objective of assessing student learning gains in cross-functional knowledge (CFK), course-related knowledge (CRK), and overall satisfaction with…

  17. Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning

    Science.gov (United States)

    Rau, Martina A.

    2017-01-01

    Visual representations play a critical role in enhancing science, technology, engineering, and mathematics (STEM) learning. Educational psychology research shows that adding visual representations to text can enhance students' learning of content knowledge, compared to text-only. But should students learn with a single type of visual…

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

    Science.gov (United States)

    Buckley-Marudas, Mary Frances

    2016-01-01

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

  19. Using Photovoice to Include People with Profound and Multiple Learning Disabilities in Inclusive Research

    Science.gov (United States)

    Cluley, Victoria

    2017-01-01

    Background: It is now expected that projects addressing the lives of people with learning disabilities include people with learning disabilities in the research process. In the past, such research often excluded people with learning disabilities, favouring the opinions of family members, carers and professionals. The inclusion of the voices of…

  20. Scheduling jobs in the cloud using on-demand and reserved instances

    NARCIS (Netherlands)

    Shen, S.; Deng, K.; Iosup, A.; Epema, D.H.J.; Wolf, F.; Mohr, B.; Mey, an D.

    2013-01-01

    Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy — larger or faster instances? on-demand or reserved instances? etc.— and to configure the leasing strategy with

  1. The Use of Case Based Multiple Choice Questions for Assessing Large Group Teaching: Implications on Student's Learning

    Directory of Open Access Journals (Sweden)

    Christina Donnelly

    2014-06-01

    Full Text Available The practice of assessments in third level education is extremely important and a rarely disputed part of the university curriculum as a method to demonstrate a student’s learning. However, assessments to test a student’s knowledge and level of understanding are challenging to apply given recent trends which are showing that student numbers are increasing, student demographics are wide ranging and resources are being stretched. As a result of these emerging challenges, lecturers are required to develop a comprehensive assessment to effectively demonstrate student learning, whilst efficiently managing large class sizes. One form of assessment which has been used for efficient assessment is multiple choice questions (MCQs; however this method has been criticised for encouraging surface learning, in comparison to other methods such as essays or case studies. This research explores the impact of blended assessment methods on student learning. This study adopts a rigorous three-staged qualitative methodology to capture third level lecturers’ and students’ perception to (1 the level of learning when using MCQs; (2 the level of learning when blended assessment in the form of case based MCQs are used. The findings illuminate the positive impact of cased based MCQs as students and lecturers suggest that it leads to a higher level of learning and deeper information processing over that of MCQs without case studies. 2 The implications of this research is that this type of assessment contributes to the current thinking within literature on the use of assessments methods, as well as the blending of assessment methods to reach a higher level of learning. It further serves to reinforce the belief that assessments are the greatest influence on students’ learning, and the requirement for both universities and lecturers to reflect on the best form of assessment to test students’ level of understanding, whilst also balancing the real challenges of

  2. Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

    Directory of Open Access Journals (Sweden)

    Jihoon Oh

    2017-09-01

    Full Text Available Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573 were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC was the highest for 1-month suicide attempts detection (0.93, followed by lifetime (0.89, and 1-year detection (0.87. Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87. Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

  3. Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales.

    Science.gov (United States)

    Oh, Jihoon; Yun, Kyongsik; Hwang, Ji-Hyun; Chae, Jeong-Ho

    2017-01-01

    Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders ( N  = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

  4. Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

    Science.gov (United States)

    Lary, D. J.

    2013-12-01

    A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

  5. Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

    Science.gov (United States)

    Stepanov, Igor I.; Abramson, Charles I.; Hoogs, Marietta; Benedict, Ralph H. B.

    2012-01-01

    The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both. PMID:22745911

  6. Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Igor I. Stepanov

    2012-01-01

    Full Text Available The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males. A group of MS patients included 365 patients (266 females and 99 males with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients 3 and 4 of our mathematical model 3∗exp(−2∗(−1+4∗(1−exp(−2∗(−1 because discriminant functions, calculated separately for 3 and 4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.

  7. Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

    International Nuclear Information System (INIS)

    Wen Fang-Qing; Zhang Gong; Ben De

    2015-01-01

    This paper addresses the direction of arrival (DOA) estimation problem for the co-located multiple-input multiple-output (MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing (CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio (SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification (MUSIC) algorithm and other CS recovery algorithms. (paper)

  8. Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

    Science.gov (United States)

    Wen, Fang-Qing; Zhang, Gong; Ben, De

    2015-11-01

    This paper addresses the direction of arrival (DOA) estimation problem for the co-located multiple-input multiple-output (MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing (CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio (SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification (MUSIC) algorithm and other CS recovery algorithms. Project supported by the National Natural Science Foundation of China (Grant Nos. 61071163, 61271327, and 61471191), the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics, China (Grant No. BCXJ14-08), the Funding of Innovation Program for Graduate Education of Jiangsu Province, China (Grant No. KYLX 0277), the Fundamental Research Funds for the Central Universities, China (Grant No. 3082015NP2015504), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA), China.

  9. The Validity of the earth and space science learning materials with orientation on multiple intelligences and character education

    Science.gov (United States)

    Liliawati, W.; Utama, J. A.; Ramalis, T. R.; Rochman, A. A.

    2018-03-01

    Validation of the Earth and Space Science learning the material in the chapter of the Earth's Protector based on experts (media & content expert and practitioners) and junior high school students' responses are presented. The data came from the development phase of the 4D method (Define, Design, Develop, Dissemination) which consist of two steps: expert appraisal and developmental testing. The instrument employed is rubric of suitability among the book contents with multiple intelligences activities, character education, a standard of book assessment, a questionnaires and close procedure. The appropriateness of the book contents with multiple intelligences, character education and standard of book assessment is in a good category. Meanwhile, students who used the book in their learning process gave a highly positive response; the book was easy to be understood. In general, the result of cloze procedure indicates high readability of the book. As our conclusion is the book chapter of the Earth's Protector can be used as a learning material accommodating students’ multiple intelligences and character internalization.

  10. THE EXECUTION INSTANCE OF THE JUDICIAL JUDGEMENTS SENTENCED IN THE LITIGATIONS OF ADMINISTRATIVE CONTENTIOUS

    Directory of Open Access Journals (Sweden)

    ADRIANA ELENA BELU

    2012-05-01

    Full Text Available The instance which solved the fund of the litigation rising from an administrative contract differs depending on the material competence sanctioned by law, in contrast to the subject of the commercial law where the execution instance is the court. In this matter the High Court stated in a decision1 that in a first case the competence of solving the legal contest against the proper forced execution and of the legal contest that has in view the explanation of the meaning of spreading and applying the enforceable title which does not proceed from a jurisdiction organ is in the authority of the court. The Law of the Administrative Contentious no 554/2004 defines in Article 2 paragraph 1 letter t the notion of execution instance, providing that this is the instance which solved the fund of the litigation of administrative contentious, so even in the case of the administrative contracts the execution instance is the one which solved the litigation rising from the contract. Corroborating this disposal with the ones existing in articles 22 and 25 in the Law, it can be shown that no matter the instance which decision is an enforceable title, the execution of the law will be done by the instance which solved the fund of the litigation regarding the administrative contentious.

  11. Effects of using presentation formats that accommodate the learner's multiple intelligences on the learning of freshman college chemistry concepts

    Science.gov (United States)

    Brown Wright, Gloria Aileen

    Howard Gardner's Theory of Multiple Intelligences identifies linguistic, spatial and logical-mathematical intelligences as necessary for learning in the physical sciences. He has identified nine intelligences which all persons possess to varying degrees, and says that learning is most effective when learners receive information in formats that correspond to their intelligence strengths. This research investigated the importance of the multiple intelligences of students in first-year college chemistry to the learning of chemistry concepts. At three pre-selected intervals during the first-semester course each participant received a tutorial on a chemistry topic, each time in a format corresponding to a different one of the three intelligences, just before the concept was introduced by the class lecturer. At the end of the experiment all subjects had experienced each of the three topics once and each format once, after which they were administered a validated instrument to measure their relative strengths in these three intelligences. The difference between a pre- and post-tutorial quiz administered on each occasion was used as a measure of learning. Most subjects were found to have similar strengths in the three intelligences and to benefit from the tutorials regardless of format. Where a difference in the extent of benefit occurred the difference was related to the chemistry concept. Data which indicate that students' preferences support these findings are also included and recommendations for extending this research to other intelligences are made.

  12. Specialized motor-driven dusp1 expression in the song systems of multiple lineages of vocal learning birds.

    Directory of Open Access Journals (Sweden)

    Haruhito Horita

    Full Text Available Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1 was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits.

  13. Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling

    International Nuclear Information System (INIS)

    Kahouli, Sondes

    2011-01-01

    This paper studies the effects of returns to scale, technological learning, i.e. learning-by-doing and learning-by-searching, and uranium price on the prospects of nuclear cost decrease. We use an extended learning curve specification, named multiple factors learning curve (MFLC). In a first stage, we estimate a single MFLC. In a second stage, we estimate the MFLC under the framework of simultaneous system of equations which takes into account the uranium supply and demand. This permits not only to enhance the reliability of the estimation by incorporating the uranium price formation mechanisms in the MFLC via the price variable, but also to give preliminary insights about uranium supply and demand behaviors and the associated effects on the nuclear expansion. Results point out that the nuclear cost has important prospects for decrease via capacity expansion, i.e. learning-by-doing effects. In contrast, they show that the learning-by-searching as well as the scale effects have a limited effect on the cost decrease prospects. Conversely, results also show that uranium price exerts a positive and significant effect on nuclear cost, implying that when the uranium price increases, the nuclear power generation cost decreases. Since uranium is characterized by important physical availability, and since it represents only a minor part in the total nuclear cost, we consider that in a context of increasing demand for nuclear energy the latter result can be explained by the fact that the positive learning effects on the cost of nuclear act in a way to dissipate the negative ones that an increase in uranium price may exert. Further, results give evidence of important inertia in the supply and demand sides as well as evidence of slow correlation between the uranium market and oil market which may limit the inter-fuels substituability effects, that is, nuclear capacity expansion and associated learning-by-doing benefits. - Highlights: → We study the prospects of nuclear cost

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

    Science.gov (United States)

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

    2015-04-15

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

  15. A Qualitative Exploration of Multiple Perspectives on Transfer of Learning Between Classroom and Clinical Workplace.

    Science.gov (United States)

    Peters, Sanne; Clarebout, Geraldine; van Nuland, Marc; Aertgeerts, Bert; Roex, Ann

    2018-01-01

    Phenomenon: Transfer of learning between classroom and workplace appears to be difficult. Various conceptions about learning in either the classroom or the workplace exist among stakeholders, yet little is known about their conceptions of the transfer of learning between both settings. This study explored stakeholders' conceptions about transfer of learning between classroom-based learning and workplace practice. Homogeneous focus groups with students, medical teachers, and workplace supervisors were conducted using a constructivist grounded theory approach. The 54 participants' conceptions mainly related to their beliefs about who was responsible for (a) preparing for transfer of learning, (b) being at the workplace and connecting back to classroom-based learning, and (c) reflecting on transfer of learning and continuing the process. A continuum was recognized between those who held medical teachers/workplace supervisors responsible and those who held students responsible. Insights: There appears to be a variety of conceptions about who is responsible for enabling the transfer process. These conceptions may influence learning and instructional activities. Hence, it may be necessary to make these beliefs explicit in order to better align stakeholders' conceptions. To this end, the conceptual framework created in this study may be a useful tool.

  16. The Tablet for Second Language Vocabulary Learning: Keyboard, Stylus or Multiple Choice

    Science.gov (United States)

    Van Hove, Stephanie; Vanderhoven, Ellen; Cornillie, Frederik

    2017-01-01

    Mobile technologies are increasingly finding their way into classroom practice. While these technologies can create opportunities that may facilitate learning, including the learning of a second or foreign language (L2), the full potential of these new media often remains underexploited. A case in point concerns tablet applications for language…

  17. Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

    Science.gov (United States)

    Manouselis, Nikos; Sampson, Demetrios

    This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

  18. Learning to Leverage Children's Multiple Mathematical Knowledge Bases in Mathematics Instruction

    Science.gov (United States)

    Turner, Erin E.; Foote, Mary Q.; Stoehr, Kathleen Jablon; McDuffie, Amy Roth; Aguirre, Julia Maria; Bartell, Tonya Gau; Drake, Corey

    2016-01-01

    In this article, the authors explore prospective elementary teachers' engagement with and reflection on activities they conducted to learn about a single child from their practicum classroom. Through these activities, prospective teachers learned about their child's mathematical thinking and the interests, competencies, and resources she or he…

  19. Imaging Evidence for Disturbances in Multiple Learning and Memory Systems in Persons with Autism Spectrum Disorders

    Science.gov (United States)

    Goh, Suzanne; Peterson, Bradley S.

    2012-01-01

    Aim: The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. Method: We conducted a search of PubMed from 1996 to 2010 using the terms "autism,""learning,""memory," and "neuroimaging." We limited our review to studies…

  20. Competition between multiple words for a referent in cross-situational word learning

    Science.gov (United States)

    Benitez, Viridiana L.; Yurovsky, Daniel; Smith, Linda B.

    2016-01-01

    Three experiments investigated competition between word-object pairings in a cross-situational word-learning paradigm. Adults were presented with One-Word pairings, where a single word labeled a single object, and Two-Word pairings, where two words labeled a single object. In addition to measuring learning of these two pairing types, we measured competition between words that refer to the same object. When the word-object co-occurrences were presented intermixed in training (Experiment 1), we found evidence for direct competition between words that label the same referent. Separating the two words for an object in time eliminated any evidence for this competition (Experiment 2). Experiment 3 demonstrated that adding a linguistic cue to the second label for a referent led to different competition effects between adults who self-reported different language learning histories, suggesting both distinctiveness and language learning history affect competition. Finally, in all experiments, competition effects were unrelated to participants’ explicit judgments of learning, suggesting that competition reflects the operating characteristics of implicit learning processes. Together, these results demonstrate that the role of competition between overlapping associations in statistical word-referent learning depends on time, the distinctiveness of word-object pairings, and language learning history. PMID:27087742

  1. The effects of technology on making conjectures: linking multiple representations in learning iterations

    OpenAIRE

    San Diego, Jonathan; Aczel, James; Hodgson, Barbara

    2004-01-01

    Numerous studies have suggested that different technologies have different effects on students' learning of mathematics, particularly in facilitating students' graphing skills and preferences for representations. For example, there are claims that students who prefer algebraic representations can experience discomfort in learning mathematics concepts using computers (Weigand and Weller, 2001; Villarreal, 2000) whilst students using calculators preferred graphical representation (Keller and Hi...

  2. The Effect of Feedback Delay on Perceptual Category Learning and Item Memory: Further Limits of Multiple Systems.

    Science.gov (United States)

    Stephens, Rachel G; Kalish, Michael L

    2018-02-01

    Delayed feedback during categorization training has been hypothesized to differentially affect 2 systems that underlie learning for rule-based (RB) or information-integration (II) structures. We tested an alternative possibility: that II learning requires more precise item representations than RB learning, and so is harmed more by a delay interval filled with a confusable mask. Experiments 1 and 2 examined the effect of feedback delay on memory for RB and II exemplars, both without and with concurrent categorization training. Without the training, II items were indeed more difficult to recognize than RB items, but there was no detectable effect of delay on item memory. In contrast, with concurrent categorization training, there were effects of both category structure and delayed feedback on item memory, which were related to corresponding changes in category learning. However, we did not observe the critical selective impact of delay on II classification performance that has been shown previously. Our own results were also confirmed in a follow-up study (Experiment 3) involving only categorization training. The selective influence of feedback delay on II learning appears to be contingent on the relative size of subgroups of high-performing participants, and in fact does not support that RB and II category learning are qualitatively different. We conclude that a key part of successfully solving perceptual categorization problems is developing more precise item representations, which can be impaired by delayed feedback during training. More important, the evidence for multiple systems of category learning is even weaker than previously proposed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  3. Evaluating Multiple Levels of an Interaction Fidelity Continuum on Performance and Learning in Near-Field Training Simulations.

    Science.gov (United States)

    Bhargava, Ayush; Bertrand, Jeffrey W; Gramopadhye, Anand K; Madathil, Kapil C; Babu, Sabarish V

    2018-04-01

    With costs of head-mounted displays (HMDs) and tracking technology decreasing rapidly, various virtual reality applications are being widely adopted for education and training. Hardware advancements have enabled replication of real-world interactions in virtual environments to a large extent, paving the way for commercial grade applications that provide a safe and risk-free training environment at a fraction of the cost. But this also mandates the need to develop more intrinsic interaction techniques and to empirically evaluate them in a more comprehensive manner. Although there exists a body of previous research that examines the benefits of selected levels of interaction fidelity on performance, few studies have investigated the constituent components of fidelity in a Interaction Fidelity Continuum (IFC) with several system instances and their respective effects on performance and learning in the context of a real-world skills training application. Our work describes a large between-subjects investigation conducted over several years that utilizes bimanual interaction metaphors at six discrete levels of interaction fidelity to teach basic precision metrology concepts in a near-field spatial interaction task in VR. A combined analysis performed on the data compares and contrasts the six different conditions and their overall effects on performance and learning outcomes, eliciting patterns in the results between the discrete application points on the IFC. With respect to some performance variables, results indicate that simpler restrictive interaction metaphors and highest fidelity metaphors perform better than medium fidelity interaction metaphors. In light of these results, a set of general guidelines are created for developers of spatial interaction metaphors in immersive virtual environments for precise fine-motor skills training simulations.

  4. Evidence for preserved novel word learning in Down syndrome suggests multiple routes to vocabulary acquisition.

    Science.gov (United States)

    Mosse, Emma K; Jarrold, Christopher

    2011-08-01

    Three studies investigated novel word learning, some requiring phonological production, each involving between 11 and 17 individuals with Down syndrome, and between 15 and 24 typically developing individuals matched for receptive vocabulary. The effect of stimuli wordlikeness and incidental procedure-based memory demands were examined to see whether these may account for an apparent impairment in word learning in Down syndrome demonstrated in earlier research. Paired associate word and nonword learning tasks were presented, requiring participants to learn the names of novel characters. The nonword stimuli varied in the degree of wordlikeness in 2 studies. A third study investigated extraneous task demand. Across 3 studies, there was no suggestion of a word learning deficit associated with Down syndrome (η(2)(p) for the main effect of group of .03, .11, and .03, respectively), despite the level of phonological representation required. There was evidence that novel word learning by participants with Down syndrome exceeded that which their verbal short-term memory capacity would predict. Vocabulary acquisition in Down syndrome may not rely on verbal short-term memory to the same extent as in typically developing children, lending support to the suggestion that new word learning may be underpinned by an additional memory process.

  5. Learning Physics Teaching through Collaborative Design of Conceptual Multiple-Choice Questions

    Science.gov (United States)

    Milner-Bolotin, Marina

    2015-01-01

    Increasing student engagement through Electronic Response Systems (clickers) has been widely researched. Its success largely depends on the quality of multiple-choice questions used by instructors. This paper describes a pilot project that focused on the implementation of online collaborative multiple-choice question repository, PeerWise, in a…

  6. Using Multiple-Choice Questions to Evaluate In-Depth Learning of Economics

    Science.gov (United States)

    Buckles, Stephen; Siegfried, John J.

    2006-01-01

    Multiple-choice questions are the basis of a significant portion of assessment in introductory economics courses. However, these questions, as found in course assessments, test banks, and textbooks, often fail to evaluate students' abilities to use and apply economic analysis. The authors conclude that multiple-choice questions can be used to…

  7. Learning multiple variable-speed sequences in striatum via cortical tutoring.

    Science.gov (United States)

    Murray, James M; Escola, G Sean

    2017-05-08

    Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

  8. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach

    Directory of Open Access Journals (Sweden)

    Moustafa Abdelaziz Moustafa

    2017-04-01

    Conclusion: “Alex Difficult Laryngoscopy Software” (ADLS is a machine learning program for prediction of difficult laryngoscopy. New cases can be entered to the training set thus improving the accuracy of the software.

  9. Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

    Science.gov (United States)

    Chen, Ke; Wang, Shihai

    2011-01-01

    Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

  10. Effects of multiple intelligences supported project-based learning on students’ achievement levels and attitudes towards English lesson

    Directory of Open Access Journals (Sweden)

    Gökhan Baş

    2007-07-01

    Full Text Available The aim of the research was to investigate the effects of multiple intelligences supported project-based learning and traditional foreign language-teaching environment on students’ achievement and their attitude towards English lesson. The research was carried out in 2009 – 2010 education-instruction year in Karatli Sehit Sahin Yilmaz Elementary School, Nigde, Turkey. Totally 50 students in two different classes in the 5th grade of this school participated in the study. The results of the research showed a significant difference between the attitude scores of the experiment group and the control group. It was also found out that the multiple intelligences approach activities were more effective in the positive development of the students’ attitudes. At the end of the research, it is revealed that the students who are educated by multiple intelligences supported project-based learning method are more successful and have a higher motivation level than the students who are educated by the traditional instructional methods.

  11. MaMiCo: Transient multi-instance molecular-continuum flow simulation on supercomputers

    Science.gov (United States)

    Neumann, Philipp; Bian, Xin

    2017-11-01

    coupling algorithmics are abstracted and incorporated in MaMiCo. Once an algorithm is set up in MaMiCo, it can be used and extended, even if other solvers are used (as soon as the respective interfaces are implemented/available). Reasons for the new version: We have incorporated a new algorithm to simulate transient molecular-continuum systems and to automatically sample data over multiple MD runs that can be executed simultaneously (on, e.g., a compute cluster). MaMiCo has further been extended by an interface to incorporate boundary forcing to account for open molecular dynamics boundaries. Besides support for coupling with various MD and CFD frameworks, the new version contains a test case that allows to run molecular-continuum Couette flow simulations out-of-the-box. No external tools or simulation codes are required anymore. However, the user is free to switch from the included MD simulation package to LAMMPS. For details on how to run the transient Couette problem, see the file README in the folder coupling/tests, Remark on MaMiCo V1.1. Summary of revisions: Open boundary forcing; Multi-instance MD sampling; support for transient molecular-continuum systems Restrictions: Currently, only single-centered systems are supported. For access to the LAMMPS-based implementation of DPD boundary forcing, please contact Xin Bian, xin.bian@tum.de. Additional comments: Please see file license_mamico.txt for further details regarding distribution and advertising of this software.

  12. An ecological method for the sampling of nonverbal signalling behaviours of young children with profound and multiple learning disabilities (PMLD).

    Science.gov (United States)

    Atkin, Keith; Lorch, Marjorie Perlman

    2016-08-01

    Profound and multiple learning disabilities (PMLD) are a complex range of disabilities that affect the general health and well-being of the individual and their capacity to interact and learn. We developed a new methodology to capture the non-symbolic signalling behaviours of children with PMLD within the context of a face-to-face interaction with a caregiver to provide analysis at a micro-level of descriptive detail incorporating the use of the ELAN digital video software. The signalling behaviours of participants in a natural, everyday interaction can be better understood with the use of this innovation in methodology, which is predicated on the ecology of communication. Recognition of the developmental ability of the participants is an integral factor within that ecology. The method presented establishes an advanced account of the modalities through which a child affected by PMLD is able to communicate.

  13. Multiple-Choice Testing Using Immediate Feedback--Assessment Technique (IF AT®) Forms: Second-Chance Guessing vs. Second-Chance Learning?

    Science.gov (United States)

    Merrel, Jeremy D.; Cirillo, Pier F.; Schwartz, Pauline M.; Webb, Jeffrey A.

    2015-01-01

    Multiple choice testing is a common but often ineffective method for evaluating learning. A newer approach, however, using Immediate Feedback Assessment Technique (IF AT®, Epstein Educational Enterprise, Inc.) forms, offers several advantages. In particular, a student learns immediately if his or her answer is correct and, in the case of an…

  14. Imaging evidence for disturbances in multiple learning and memory systems in persons with autism spectrum disorders.

    Science.gov (United States)

    Goh, Suzanne; Peterson, Bradley S

    2012-03-01

    The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. We conducted a search of PubMed from 1996 to 2010 using the terms 'autism,''learning,''memory,' and 'neuroimaging.' We limited our review to studies correlating learning and memory function with neuroimaging features of the brain. The early literature supports the following preliminary hypotheses: (1) abnormalities of hippocampal subregions may contribute to autistic deficits in episodic and relational memory; (2) disturbances to an amygdala-based network (which may include the fusiform gyrus, superior temporal cortex, and mirror neuron system) may contribute to autistic deficits in socio-emotional learning and memory; and (3) abnormalities of the striatum may contribute to developmental dyspraxia in individuals with ASD. Characterizing the disturbances to learning and memory systems in ASD can inform our understanding of the neural bases of autistic behaviors and the phenotypic heterogeneity of ASD. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

  15. Millennial generation student nurses' perceptions of the impact of multiple technologies on learning.

    Science.gov (United States)

    Montenery, Susan M; Walker, Marjorie; Sorensen, Elizabeth; Thompson, Rhonda; Kirklin, Dena; White, Robin; Ross, Carl

    2013-01-01

    To determine how millennial nursing students perceive the effects of instructional technology on their attentiveness, knowledge, critical thinking, and satisfaction. BACKGROUND Millennial learners develop critical thinking through experimentation, active participation, and multitasking with rapid shifts between technological devices. They desire immediate feedback. METHOD; A descriptive, longitudinal, anonymous survey design was used with a convenience sample of 108 sophomore, junior, and senior baccalaureate nursing students (participation rates 95 percent, winter, 85 percent, spring). Audience response, virtual learning, simulation, and computerized testing technologies were used. An investigator-designed instrument measured attentiveness, knowledge, critical thinking, and satisfaction (Cronbach's alphas 0.73, winter; 0.84, spring). Participants positively rated the audience response, virtual learning, and simulation instructional technologies on their class participation, learning, attention, and satisfaction. They strongly preferred computerized testing. Consistent with other studies, these students engaged positively with new teaching strategies using contemporary instructional technology. Faculty should consider using instructional technologies.

  16. All together now: concurrent learning of multiple structures in an artificial language.

    Science.gov (United States)

    Romberg, Alexa R; Saffran, Jenny R

    2013-01-01

    Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants' knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition. © 2013 Cognitive Science Society, Inc.

  17. Progressive practice promotes motor learning and repeated transient increases in corticospinal excitability across multiple days

    DEFF Research Database (Denmark)

    Christiansen, Lasse; Madsen, Mads Alexander Just; Bojsen-Møller, Emil

    2018-01-01

    Background: A session of motor skill learning is accompanied by transient increases in corticospinal excitability (CSE), which are thought to reflect acute changes in neuronal connectivity associated with improvements in sensorimotor performance. Factors influencing changes in excitability...... and motor skill with continued practice remain however to be elucidated. Objective/Hypothesis: Here we investigate the hypothesis that progressive motor practice during consecutive days can induce repeated transient increases in corticospinal excitability and promote motor skill learning. Methods: Changes...... in motor performance and CSE were assessed during 4 consecutive days of skill learning and 8 days after the last practice session. CSE was assessed as area under recruitment curves (RC) using transcranial magnetic stimulation (TMS). Two groups of participants (n = 12) practiced a visuomotor tracking...

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

    Science.gov (United States)

    Liao, Ying; Lin, Wen-He

    2016-01-01

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

  19. Learning hardware using multiple-valued logic - Part 2: Cube calculus and architecture

    NARCIS (Netherlands)

    Perkowski, M.A.; Foote, D.; Chen, Qihong; Al-Rabadi, A.; Jozwiak, L.

    2002-01-01

    For Part 1 see ibid. vol.22, no.3 (2002). A massively parallel reconfigurable processor speeds up the logic operators performed in the learning hardware. The approach uses combinatorial synthesis methods developed within the framework of the logic synthesis approach in digital-circuit-design

  20. Multiple Learning Strategies Project. Small Engine Repair Service. Regular Vocational. [Vol. 1.

    Science.gov (United States)

    Pitts, Jim; And Others

    This instructional package is one of two designed for use by regular vocational students in the vocational area of small engine repair service. Contained in this document are forty-four learning modules organized into ten units: engine block; air cleaner; starters; fuel tanks; lines, filters, and pumps; carburetors; electrical; magneto systems;…

  1. Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

    Science.gov (United States)

    Paterson, John W.

    2017-01-01

    Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

  2. Fostering Creativity: A Multiple Intelligences Approach to Designing Learning in Undergraduate Fine Art

    Science.gov (United States)

    Clarke, Angela; Cripps, Peter

    2012-01-01

    Curriculum and pedagogy in undergraduate fine art can promote an approach to learning creativity that is more about being an artist than knowing about art. Lecturers can provide a road map for developing particular dispositions, in relation to student ideas and perceptions, to foster personalised creativity. This requires that lecturers have an…

  3. The Multicultural Mathematics Classroom: Culturally Aware Teaching through Cooperative Learning & Multiple Representations

    Science.gov (United States)

    Jao, Limin

    2012-01-01

    The National Council of Teachers of Mathematics (NCTM, 2000) has created a set of standards to reform mathematics teaching procedures to ensure that all students understand mathematics and learn to think mathematically. The standards also require teachers to use strategies that allow all students to reason and communicate mathematically and…

  4. Tell us our story : Understanding 'religion and violence' in multiple contexts of learning

    NARCIS (Netherlands)

    Van Liere, Lucien

    2014-01-01

    This article raises the question about how definitions of religion and violence can be understood as links to the context in which they are formulated. The focus is on the context of academic learning. Understanding a definition as a micro-narrative that reflects the cultural 'archive', the author

  5. ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics

    Science.gov (United States)

    Graesser, Arthur C.; Hu, Xiangen; Nye, Benjamin D.; VanLehn, Kurt; Kumar, Rohit; Heffernan, Cristina; Heffernan, Neil; Woolf, Beverly; Olney, Andrew M.; Rus, Vasile; Andrasik, Frank; Pavlik, Philip; Cai, Zhiqiang; Wetzel, Jon; Morgan, Brent; Hampton, Andrew J.; Lippert, Anne M.; Wang, Lijia; Cheng, Qinyu; Vinson, Joseph E.; Kelly, Craig N.; McGlown, Cadarrius; Majmudar, Charvi A.; Morshed, Bashir; Baer, Whitney

    2018-01-01

    Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics,…

  6. The Role of Metacognitive Strategies in Learning Music: A Multiple Case Study

    Science.gov (United States)

    Colombo, Barbara; Antonietti, Alessandro

    2017-01-01

    The positive role of metacognition in music learning and practice is well assessed, but the role of musicians' metacognitive skills in such a context is not yet clear. Teachers often state that they apply a metacognitive approach during their lessons, but students fail to acknowledge it and report that they become metacognitive learners thanks to…

  7. Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

    Science.gov (United States)

    Engelbrecht, Jeffrey C.

    2003-01-01

    Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

  8. Developing Multiple Literacies in Academic English through Service-Learning and Community Engagement

    Science.gov (United States)

    Askildson, Lance R.; Kelly, Annie Cahill; Mick, Connie Snyder

    2013-01-01

    Research on service-learning offers compelling evidence of the advances student learners make in moral development, orientation to prosocial behavior, and curricular content retention. But who are those student learners? Most studies focus on native, dominant-culture, dominant-language students serving marginalized populations. Studies of the…

  9. Distance Education in a Cost Accounting Course: Instruction, Interaction, and Multiple Measures of Learning Outcomes

    Science.gov (United States)

    Chen, Clement C.; Jones, Keith T.; Moreland, Keith

    2010-01-01

    Students in online and traditional classroom sections of an intermediate-level cost accounting course responded to a survey about their experiences in the course. Specifically, several items related to the instruction and learning outcomes were addressed. Additionally, student examination performance in the two types of sections was compared. The…

  10. Teacher Agency and Professional Learning: Rethinking Fidelity of Implementation as Multiplicities of Enactment

    Science.gov (United States)

    Buxton, Cory A.; Allexsaht-Snider, Martha; Kayumova, Shakhnoza; Aghasaleh, Rouhollah; Choi, Youn-Jeng; Cohen, Allan

    2015-01-01

    In this paper we use practice theory, with its focus on the interplay of structure and agency, to theorize about teacher engagement in professional learning and teacher enactment of pedagogical practices as an alternative to framing implementation research in terms of program adherence and fidelity of implementation. Practice theory allowed us to…

  11. Multiple Learning Strategies Project. Building Maintenance & Engineering. Low Reader. [Vol. 1.

    Science.gov (United States)

    Smith, Dwight; And Others

    This instructional package is one of two designed for low reader students (those reading at a 3-6 grade level) in the vocational area of building maintenance and engineering. The forty-six learning modules are organized into three units: floor care; general maintenance tasks; and restrooms. Each module includes these elements: a performance…

  12. Multiple Learning Strategies Project. Building Maintenance & Engineering. Educable Mentally Impaired. [Vol. 2.

    Science.gov (United States)

    Constantini, Debra; And Others

    This instructional package is one of three designed for educable mentally impaired students in the vocational area of building maintenance and engineering. The thirty-four learning modules are organized into six units: general maintenance tasks; restrooms; chalkboards; carpet care; office cleaning; and grounds. Each module includes these elements:…

  13. Multiple Learning Strategies Project. Building Maintenance & Engineering. Low Reader. [Vol. 2.

    Science.gov (United States)

    Steinberg, A.; And Others

    This instructional package is one of two designed for low reader students (those reading at grade level 3-6) in the vocational area of building maintenance and engineering. The forty-four learning modules are organized into eleven units: chalkboards; carpet care; office cleaning; grounds; sanitation; boiler maintenance and operation; power and…

  14. Multiple Learning Strategies Project. Building Maintenance & Engineering. Regular Vocational. [Vol. 2.

    Science.gov (United States)

    Smith, Dwight; And Others

    This instructional package is one of two designed for regular vocational students in the vocational area of building maintenance and engineering. The fifty-three learning modules are organized into ten units: office cleaning; grounds; sanitation; boiler maintenance and operation; power and hand tools; cabinet construction; repair of damaged…

  15. Multiple Learning Strategies Project. Building Maintenance & Engineering. Educable Mentally Impaired. [Vol. 1.

    Science.gov (United States)

    Smith, Dwight; And Others

    This instructional package is one of three designed for educable mentally impaired students in the vocational area of building maintenance and engineering. The thirty learning modules are organized into two units: floor care and general maintenance tasks. Each module includes these elements: a performance objective page which tells the student…

  16. Learning from TESS-India's Approach to OER Localisation across Multiple Indian States

    Science.gov (United States)

    Perryman, Leigh-Anne; Hemmings-Buckler, Alison; Seal, Tim

    2014-01-01

    Arguably, the benefits of open educational resources (OER) are greatest in low- and middle-income countries, where they have the potential to increase access to learning for those who may otherwise be excluded. However, for OER to be truly useful to educators and learners they need to be adapted to suit the contexts in which they are to be…

  17. Comparison of Chemistry Learning Outcomes with Inquiry Learning Model and Learning Cycle 5E in Material Solubility and Solubility Multiplication Results

    Directory of Open Access Journals (Sweden)

    Nur Indah Firdausi

    2015-04-01

    Full Text Available Perbandingan Hasil Belajar Kimia dengan Model Pembelajaran Inquiry dan Learning Cycle 5E pada Materi Kelarutan dan Hasil Kali Kelarutan   Abstract: This research is aimed to compare the effectiveness between inquiry and LC 5E in solubility equilibria and the solubility product for students with different prior knowledge. The effectiveness of both learning models is measured from students learning outcome. This quasi experimental research uses factorial2x2 with posttest only design. Research samples are chosen using cluster random sampling. They are two classes of XI IPA SMAN 1 Kepanjen in the 2012/2013 academic year which consist of 31 students in each class. Cognitive learning outcome is measured by test items consist of four objective items and nine subjective items. Technique of data analysis in this research is two way ANOVA. Research results show that: (1 cognitive learning outcome and higher cognitive learning outcome of students in inquiry class is higher than students in LC 5E class; (2 cognitive learning outcome and higher cognitive learning outcome of students who have upper prior knowledge is higher than students who have lower prior knowledge in both inquiry and LC 5E. Key Words: learning outcome, inquiry, learning cycle 5E, solubility equilibria and the solubility product   Abstrak: Penelitian ini bertujuan membandingkan keefektifan model inquiry dan LC 5E pada materi kelarutan dan hasil kali kelarutan untuk siswa dengan kemampuan awal berbeda. Keefektifan model pembelajaran dilihat dari hasil belajar kognitif siswa. Penelitian ini menggunakan rancangan eksperimen semu dengan desain faktorial 2x2. Subjek penelitian dipilih secara cluster random sampling yaitu dua kelas XI IPA SMAN 1 Kepanjen dengan jumlah masing-masing kelas sebanyak 31 siswa. Instrumen perlakuan yang digunakan adalah silabus dan RPP sedangkan instrumen pengukuran berupa soal tes terdiri dari empat soal objektif dan sembilan soal subjektif. Teknik analisis data

  18. Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Xin Wang

    2018-02-01

    Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

  19. Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

    Science.gov (United States)

    Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

    2016-02-01

    Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

  20. A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

    Science.gov (United States)

    Martin, David

    2008-01-01

    This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

  1. Magic Finger Teaching Method in Learning Multiplication Facts among Deaf Students

    Science.gov (United States)

    Thai, Liong; Yasin, Mohd. Hanafi Mohd

    2016-01-01

    Deaf students face problems in mastering multiplication facts. This study aims to identify the effectiveness of Magic Finger Teaching Method (MFTM) and students' perception towards MFTM. The research employs a quasi experimental with non-equivalent pre-test and post-test control group design. Pre-test, post-test and questionnaires were used. As…

  2. Collaborative learning in local partnerships for public value : a multiple case study

    NARCIS (Netherlands)

    Bos, Eltje; de Zeeuw, Anna; van Vliet, Pieter; de Kreek, Mike

    2017-01-01

    In our recently started multiple case study - funded by the Netherlands Scientific Board (NWO) - on these processes of collaboration, we focused on the dynamics in growing partnerships between citizens, community social workers and civil servants. Three cases of social neighbourhood initiatives are

  3. Enriching Student Concept Images: Teaching and Learning Fractions through a Multiple-Embodiment Approach

    Science.gov (United States)

    Zhang, Xiaofen; Clements, M. A.; Ellerton, Nerida F.

    2015-01-01

    This study investigated how fifth-grade children's concept images of the unit fractions represented by the symbols 1/2, 1/3/ and 1/4 changed as a result of their participation in an instructional intervention based on multiple embodiments of fraction concepts. The participants' concept images were examined through pre- and post-teaching written…

  4. Why Is an Application of Multiple Intelligences Theory Important for Language Learning and Teaching Speaking Ability?

    Science.gov (United States)

    Boonma, Malai; Phaiboonnugulkij, Malinee

    2014-01-01

    This article calls for a strong need to propose the theoretical framework of the Multiple Intelligences theory (MI) and provide a suitable answer of the doubt in part of foreign language teaching. The article addresses the application of MI theory following various sources from Howard Gardner and the authors who revised this theory for use in the…

  5. Exploring the Use of Multiple Analogical Models when Teaching and Learning Chemical Equilibrium

    Science.gov (United States)

    Harrison, Allan G.; De Jong, Onno

    2005-01-01

    This study describes the multiple analogical models used to introduce and teach Grade 12 chemical equilibrium. We examine the teacher's reasons for using models, explain each model's development during the lessons, and analyze the understandings students derived from the models. A case study approach was used and the data were drawn from the…

  6. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

  7. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

  8. Young children’s learning of relational categories:multiple comparisons and their cognitive constraints

    Directory of Open Access Journals (Sweden)

    Jean-Pierre eThibaut

    2015-05-01

    Full Text Available Relational categories are notoriously difficult to learn because they are not defined by intrinsic stable properties. We studied the impact of comparisons on relational concept learning with a novel word learning task in 42-month-old children. Capitalizing on Gentner et al. (2011, two, three or four pairs of stimuli were introduced with a novel relational word. In a given trial, the set of pairs was composed of either close or far pairs (e.g., close pair: knife1-watermelon, knife2-orange, knife3-slice of bread and knife4-meat; far pair: ax-evergreen tree, saw-log, cutter-cardboard and knife-slice of bread, for the cutter for relation. Close pairs (2 vs. 3 vs. 4 pairs led to random generalizations whereas comparisons with far pairs gave the expected relational generalization. The 3 pair case gave the best results. It is argued that far pairs promote deeper comparisons than close pairs. As shown by a control experiment, this was the case only when far pairs display well known associations.

  9. Picture this: The value of multiple visual representations for student learning of quantum concepts in general chemistry

    Science.gov (United States)

    Allen, Emily Christine

    Mental models for scientific learning are often defined as, "cognitive tools situated between experiments and theories" (Duschl & Grandy, 2012). In learning, these cognitive tools are used to not only take in new information, but to help problem solve in new contexts. Nancy Nersessian (2008) describes a mental model as being "[loosely] characterized as a representation of a system with interactive parts with representations of those interactions. Models can be qualitative, quantitative, and/or simulative (mental, physical, computational)" (p. 63). If conceptual parts used by the students in science education are inaccurate, then the resulting model will not be useful. Students in college general chemistry courses are presented with multiple abstract topics and often struggle to fit these parts into complete models. This is especially true for topics that are founded on quantum concepts, such as atomic structure and molecular bonding taught in college general chemistry. The objectives of this study were focused on how students use visual tools introduced during instruction to reason with atomic and molecular structure, what misconceptions may be associated with these visual tools, and how visual modeling skills may be taught to support students' use of visual tools for reasoning. The research questions for this study follow from Gilbert's (2008) theory that experts use multiple representations when reasoning and modeling a system, and Kozma and Russell's (2005) theory of representational competence levels. This study finds that as students developed greater command of their understanding of abstract quantum concepts, they spontaneously provided additional representations to describe their more sophisticated models of atomic and molecular structure during interviews. This suggests that when visual modeling with multiple representations is taught, along with the limitations of the representations, it can assist students in the development of models for reasoning about

  10. Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

    Directory of Open Access Journals (Sweden)

    Faimison Porto

    2016-12-01

    Full Text Available The defect prediction models can be a good tool on organizing the project's test resources. The models can be constructed with two main goals: 1 to classify the software parts - defective or not; or 2 to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP. The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables. We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments. The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

  11. Instances or Sequences? Improving the State of the Art of Qualitative Research

    Directory of Open Access Journals (Sweden)

    David Silverman

    2005-09-01

    Full Text Available Numbers apparently talk. With few numbers, qualitative researchers appear to rely on examples or instances to support their analysis. Hence research reports routinely display data extracts which serve as telling instances of some claimed phenomenon. However, the use of such an evidential base rightly provokes the charge of (possible anecdotalism, i.e. choosing just those extracts which support your argument. I suggest that this methodological problem is best addressed by returning to those features of our theoretical roots which tend to distinguish what we do from the work of quantitative social scientists. Although SAUSSURE is most cited in linguistics and structural anthropology, he provides a simple rule that applies to us all. In a rebuke to our reli­ance on instances, SAUSSURE tells us "no mean­ing exists in a single item". Everything depends upon how single items (elements are articulated. One everyday activity in which the social world is articulated is through the construction of se­quences. Just as participants attend to the se­quential placing of interactional "events", so should social scientists. Using examples drawn from focus groups, fieldnotes and audiotapes, I argue that the identification of such sequences rather than the citing of instances should constitute a prime test for the adequacy of any claim about qualitative data. URN: urn:nbn:de:0114-fqs0503301

  12. A practical approximation algorithm for solving massive instances of hybridization number

    NARCIS (Netherlands)

    Iersel, van L.J.J.; Kelk, S.M.; Lekic, N.; Scornavacca, C.; Raphael, B.; Tang, J.

    2012-01-01

    Reticulate events play an important role in determining evolutionary relationships. The problem of computing the minimum number of such events to explain discordance between two phylogenetic trees is a hard computational problem. In practice, exact solvers struggle to solve instances with

  13. Anaphoric Reference to Instances, Instantiated and Non-Instantiated Categories: A Reading Time Study.

    Science.gov (United States)

    Garnham, Alan

    1981-01-01

    Experiments using memory paradigms have shown that general terms receive context-dependent encodings. This experiment investigates the encoding of category and instance nouns. The results indicate that representations set up during reading are the product of both the linguistic input and of general knowledge. (Author/KC)

  14. Microsoft and the Court of First Instance: What Does it All Mean?

    OpenAIRE

    Renata Hesse

    2007-01-01

    As someone who has spent a considerable portion of the last five years working on issues involving Microsoft’s conduct and the competition laws, I read with interest the commentary that followed the issuance of the Court of First Instance’s decision on September 17.

  15. Increasing the detection of minority class instances in financial statement fraud

    CSIR Research Space (South Africa)

    Moepya, Stephen

    2017-04-01

    Full Text Available -1 Asian Conference on Intelligent Information and Database Systems, 3-5 April 2017, Kanazawa, Japan Increasing the detection of minority class instances in financial statement fraud Stephen Obakeng Moepya1,2(B), Fulufhelo V. Nelwamondo1...

  16. Solving large instances of the quadratic cost of partition problem on dense graphs by data correcting algorithms

    NARCIS (Netherlands)

    Goldengorin, Boris; Vink, Marius de

    1999-01-01

    The Data-Correcting Algorithm (DCA) corrects the data of a hard problem instance in such a way that we obtain an instance of a well solvable special case. For a given prescribed accuracy of the solution, the DCA uses a branch and bound scheme to make sure that the solution of the corrected instance

  17. Connections Between Future Time Perspectives and Self-Regulated Learning for Mid-Year Engineering Students: A Multiple Case Study

    Science.gov (United States)

    Chasmar, Justine

    This dissertation presents multiple studies with the purpose of understanding the connections between undergraduate engineering students' motivations, specifically students' Future Time Perspectives (FTPs) and Self-Regulated Learning (SRL). FTP refers to the views students hold about the future and how their perceptions of current tasks are affected by these views. SRL connects the behaviors, metacognition, and motivation of students in their learning. The goals of this research project were to 1) qualitatively describe and document engineering students' SRL strategies, 2) examine interactions between engineering students' FTPs and SRL strategy use, and 3) explore goal-setting as a bridge between FTP and SRL. In an exploratory qualitative study with mid-year industrial engineering students to examine the SRL strategies used before and after an SRL intervention, results showed that students intended to use more SRL strategies than they attempted. However, students self-reported using new SRL strategies from the intervention. Students in this population also completed a survey and a single interview about FTP and SRL. Results showed perceptions of instrumentality of coursework and skills as motivation for using SRL strategies, and a varied use of SRL strategies for students with different FTPs. Overall, three types of student FTP were seen: students with a single realistic view of the future, conflicting ideal and realistic future views, or open views of the future. A sequential explanatory mixed methods study was conducted with mid-year students from multiple engineering majors. First a cluster analysis of survey results of FTP items compared to FTP interview responses was used for participant selection. Then a multiple case study was conducted with data collected through surveys, journal entries, course performance, and two interviews. Results showed that students with a well-defined FTP self-regulated in the present based on their varied perceptions of

  18. Exploiting multiple sources of information in learning an artificial language: human data and modeling.

    Science.gov (United States)

    Perruchet, Pierre; Tillmann, Barbara

    2010-03-01

    This study investigates the joint influences of three factors on the discovery of new word-like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word-likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word-like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of different models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad-hoc modifications, while relying exclusively on general-purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. Copyright © 2009 Cognitive Science Society, Inc.

  19. Multiple instances of paraphyletic species and cryptic taxa revealed by mitochondrial and nuclear RAD data for Calandrella larks (Aves: Alaudidae).

    Science.gov (United States)

    Stervander, Martin; Alström, Per; Olsson, Urban; Ottosson, Ulf; Hansson, Bengt; Bensch, Staffan

    2016-09-01

    The avian genus Calandrella (larks) was recently suggested to be non-monophyletic, and was divided into two genera, of which Calandrella sensu stricto comprises 4-5 species in Eurasia and Africa. We analysed mitochondrial cytochrome b (cytb) and nuclear Restriction-site Associated DNA (RAD) sequences from all species, and for cytb we studied 21 of the 22 recognised subspecies, with the aim to clarify the phylogenetic relationships within the genus and to compare large-scale nuclear sequence patterns with a widely used mitochondrial marker. Cytb indicated deep splits among the currently recognised species, although it failed to support the interrelationships among most of these. It also revealed unexpected deep divergences within C. brachydactyla, C. blanfordi/C. erlangeri, C. cinerea, and C. acutirostris. It also suggested that both C. brachydactyla and C. blanfordi, as presently circumscribed, are paraphyletic. In contrast, most of the many subspecies of C. brachydactyla and C. cinerea were unsupported by cytb, although two populations of C. cinerea were found to be genetically distinct. The RAD data corroborated the cytb tree (for the smaller number of taxa analysed) and recovered strongly supported interspecific relationships. However, coalescence analyses of the RAD data, analysed in SNAPP both with and without an outgroup, received equally strong support for two conflicting topologies. We suggest that the tree rooted with an outgroup - which is not recommended for SNAPP - is more trustworthy, and suggest that the reliability of analyses performed without any outgroup species should be thoroughly evaluated. We also demonstrate that degraded museum samples can be phylogenetically informative in RAD analyses following careful bioinformatic treatment. We note that the genus Calandrella is in need of taxonomic revision. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Declarative and nondeclarative memory: multiple brain systems supporting learning and memory.

    Science.gov (United States)

    Squire, L R

    1992-01-01

    Abstract The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).

  1. The Effectiveness of learning materials based on multiple intelligence on the understanding of global warming

    Science.gov (United States)

    Liliawati, W.; Purwanto; Zulfikar, A.; Kamal, R. N.

    2018-05-01

    This study aims to examine the effectiveness of the use of teaching materials based on multiple intelligences on the understanding of high school students’ material on the theme of global warming. The research method used is static-group pretest-posttest design. Participants of the study were 60 high school students of XI class in one of the high schools in Bandung. Participants were divided into two classes of 30 students each for the experimental class and control class. The experimental class uses compound-based teaching materials while the experimental class does not use a compound intelligence-based teaching material. The instrument used is a test of understanding of the concept of global warming with multiple choices form amounted to 15 questions and 5 essay items. The test is given before and after it is applied to both classes. Data analysis using N-gain and effect size. The results obtained that the N-gain for both classes is in the medium category and the effectiveness of the use of teaching materials based on the results of effect-size test results obtained in the high category.

  2. FMRI hypoactivation during verbal learning and memory in former high school football players with multiple concussions.

    Science.gov (United States)

    Terry, Douglas P; Adams, T Eric; Ferrara, Michael S; Miller, L Stephen

    2015-06-01

    Multiple concussions before the age of 18 may be associated with late-life memory deficits. This study examined neural activation associated with verbal encoding and memory retrieval in former athletes ages 40-65 who received at least two concussions (median = 3; range = 2-15) playing high school football and a group of former high school football players with no reported history of concussions matched on age, education, and pre-morbid IQ. Functional magnetic resonance imaging data collected during a modified verbal paired associates paradigm indicated that those with concussive histories had hypoactivation in left hemispheric language regions, including the inferior/middle frontal gyri and angular gyrus compared with controls. However, concussive history was not associated with worse memory functioning on neuropsychological tests or worse behavioral performance during the paradigm, suggesting that multiple early-life concussions may be associated with subtle changes in the verbal encoding system that limits one from accessing higher-order semantic networks, but this difference does not translate into measurable cognitive performance deficits. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  3. Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.

    Science.gov (United States)

    Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

    2013-02-01

    This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

  4. The nature of verbal memory impairment in multiple sclerosis: a list-learning and meta-analytic study.

    Science.gov (United States)

    Lafosse, Jose M; Mitchell, Sandra M; Corboy, John R; Filley, Christopher M

    2013-10-01

    The primary purpose of this study was to test the hypothesis that multiple sclerosis (MS) patients have impaired acquisition rather than a retrieval deficit. Verbal memory impairment in MS was examined in 53 relapsing-remitting MS patients and 31 healthy controls (HC), and in a meta-analysis of studies that examined memory functioning in MS with list-learning tasks. The MS group demonstrated significantly lower acquisition and delayed recall performance than the HC group, and the meta-analysis revealed that the largest effect sizes were obtained for acquisition measures relative to delayed recall and recognition. Our data argue against a retrieval deficit as the sole explanation for verbal memory impairment in MS, and make a consistent case for the position that deficient acquisition contributes to the memory dysfunction of MS patients. Deficient acquisition may result from demyelination in relevant white matter tracts that reduces encoding efficiency as a result of impaired speed of information processing.

  5. Learned Helplessness: Theory and Evidence

    Science.gov (United States)

    Maier, Steven F.; Seligman, Martin E. P.

    1976-01-01

    Authors believes that three phenomena are all instances of "learned helplessness," instances in which an organism has learned that outcomes are uncontrollable by his responses and is seriously debilitated by this knowledge. This article explores the evidence for the phenomena of learned helplessness, and discussed a variety of theoretical…

  6. Multi-Instance Quotation System (SaaS) Based on Docker Containerizing Platform

    OpenAIRE

    Shekhamis, Anass

    2016-01-01

    This thesis covers the development of a quotation system that is built as a multi-instance SaaS. Quotation systems usually come as part of customer relationship management systems, but not necessarily included. They also tend to have invoicing alongside the original functionality; creating quotations for customers. The system uses Microservices Architecture were each service is a replaceable and upgradeable component that achieve certain functionality and easily integrate with other third-par...

  7. FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule

    OpenAIRE

    Lu Si; Jie Yu; Shasha Li; Jun Ma; Lei Luo; Qingbo Wu; Yongqi Ma; Zhengji Liu

    2017-01-01

    Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rul...

  8. Extension of instance search technique by geometric coding and quantization error compensation

    OpenAIRE

    García Del Molino, Ana

    2013-01-01

    [ANGLÈS] This PFC analyzes two ways of improving the video retrieval techniques for instance search problem. In one hand, "Pairing Interest Points for a better Signature using Sparse Detector's Spatial Information", allows the Bag-of-Words model to keep some spatial information. In the other, "Study of the Hamming Embedding Signature Symmetry in Video Retrieval" provides binary signatures that refine the matching based on visual words, and aims to find the best way of matching taking into acc...

  9. Instances of Use of United States Armed Forces Abroad, 1798-2014

    Science.gov (United States)

    2014-09-15

    Garcia, and Thomas J. Nicola . Instances of Use of United States Armed Forces Abroad, 1798-2014 Congressional Research Service Contents...landing zones near the U.S. Embassy in Saigon and the Tan Son Nhut Airfield. Mayaguez incident. On May 15, 1975, President Ford reported he had ordered...Report R41989, Congressional Authority to Limit Military Operations, by Jennifer K. Elsea, Michael John Garcia and Thomas J. Nicola . CRS Report R43344

  10. A medley of meanings: Insights from an instance of gameplay in League of Legends

    Directory of Open Access Journals (Sweden)

    Max Watson

    2015-06-01

    Full Text Available This article engages with the notion of insightful gameplay. It recounts debates about what, if anything, makes play meaningful. Through these, it contends that while some games are explicitly designed to foster insightful gameplay, most are not and many might even be considered utterly meaningless. It notes how discussions about what makes playing games meaningful raise concomitant questions about what playing means. It then strives to reconcile these two interrelated questions by offering the notion of a medley of meanings. A medley of meanings is the notion that each player brings their own subjective disposition to playing to a particular instance of gameplay; no participant to gameplay should be considered as in a state that is “not playing”. Because these subjective dispositions to playing can be quite divergent, players can and often do clash in instances of gameplay. This article then contends that these clashes can in turn render the most seemingly meaningless games potential hotbeds of insightful gameplay. The second half of this article discusses the ethnographic example of an instance of gameplay in the digital game League of Legends in order to explicate the notion of a medley of meanings.

  11. A practical approximation algorithm for solving massive instances of hybridization number for binary and nonbinary trees.

    Science.gov (United States)

    van Iersel, Leo; Kelk, Steven; Lekić, Nela; Scornavacca, Celine

    2014-05-05

    Reticulate events play an important role in determining evolutionary relationships. The problem of computing the minimum number of such events to explain discordance between two phylogenetic trees is a hard computational problem. Even for binary trees, exact solvers struggle to solve instances with reticulation number larger than 40-50. Here we present CycleKiller and NonbinaryCycleKiller, the first methods to produce solutions verifiably close to optimality for instances with hundreds or even thousands of reticulations. Using simulations, we demonstrate that these algorithms run quickly for large and difficult instances, producing solutions that are very close to optimality. As a spin-off from our simulations we also present TerminusEst, which is the fastest exact method currently available that can handle nonbinary trees: this is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All three methods are based on extensions of previous theoretical work (SIDMA 26(4):1635-1656, TCBB 10(1):18-25, SIDMA 28(1):49-66) and are publicly available. We also apply our methods to real data.

  12. Why is an Application of Multiple Intelligences Theory Important for Language Learning and Teaching Speaking Ability?

    Directory of Open Access Journals (Sweden)

    Malai Boonma

    2014-10-01

    Full Text Available This article calls for a strong need to propose the theoretical framework of the Multiple Intelligences theory (MI and provide a suitable answer of the doubt in part of foreign language teaching. The article addresses the application of MI theory following various sources from Howard Gardner and the authors who revised this theory for using in the field of the English speaking improvement domain. In other word, this article combines and summarizes appropriate elements for the person on how to start teaching with this theory. The article also describes sequences and implication of the theory into practice. MI theory with the description of eight intelligences characteristic is presented. Following is the parts of activities catering and the processes of teaching with MI are provided. This article ends with the reviews of the ways for assessment and examples of lesson plan integrated with MI theory.

  13. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; van Soest, Johan; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Lambin, Philippe; Dekker, Andre

    2016-12-01

    One of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital. Clinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)). A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer. We show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51-0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets. Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

  14. The meaning of spasticity to people with multiple sclerosis: what can health professionals learn?

    Science.gov (United States)

    Morley, Alex; Tod, Angela; Cramp, Mary; Mawson, Sue

    2013-07-01

    Multiple sclerosis (MS) is the most common disabling neurological condition affecting young adults. One third of people on an American registry of people with MS (PWMS) reported having activities affected by spasticity. The psychosocial effects of spasticity in people with MS have been shown to be distressing and detrimental to emotional and social relationships when investigated from a psychology perspective. This paper investigates the impact of spasticity on the lives of people living with MS from a physiotherapeutic perspective. This study involved 12 semi-structured interviews with individuals experiencing MS-related spasticity. Ten sets of data were analyzed following framework analysis principles. Results suggest spasticity effects life experience of these PWMS in diverse and complex ways. Physical, psychological and social consequences of spasticity are closely linked and can be far reaching. Therapists need to be aware of links between specific physical symptoms and their psychosocial consequences if they want to improve peoples' quality of life. This paper provides in depth qualitative research evidence for the complexity of the spasticity experience for each individual, strengthening the argument for a patient-centred approach to treatment. These results also support the case for targeted interventions with effectiveness recorded in a patient-centred way. • Spasticity is suggested here to affect the lives of individuals with multiple sclerosis in diverse and far reaching ways. Therapists need to investigate this fully in subjective assessment to impact on people's quality of life. • Direct links were identified between treatable physical symptoms and far reaching consequences of spasticity. • Knowledge about the complexity of the spasticity experience for each individual will allow therapists to target interventions appropriately and accurately record effectiveness in a patient-centred way.

  15. The Effectiveness of Problem-Based Learning Approach Based on Multiple Intelligences in Terms of Student’s Achievement, Mathematical Connection Ability, and Self-Esteem

    Science.gov (United States)

    Kartikasari, A.; Widjajanti, D. B.

    2017-02-01

    The aim of this study is to explore the effectiveness of learning approach using problem-based learning based on multiple intelligences in developing student’s achievement, mathematical connection ability, and self-esteem. This study is experimental research with research sample was 30 of Grade X students of MIA III MAN Yogyakarta III. Learning materials that were implemented consisting of trigonometry and geometry. For the purpose of this study, researchers designed an achievement test made up of 44 multiple choice questions with respectively 24 questions on the concept of trigonometry and 20 questions for geometry. The researcher also designed a connection mathematical test and self-esteem questionnaire that consisted of 7 essay questions on mathematical connection test and 30 items of self-esteem questionnaire. The learning approach said that to be effective if the proportion of students who achieved KKM on achievement test, the proportion of students who achieved a minimum score of high category on the results of both mathematical connection test and self-esteem questionnaire were greater than or equal to 70%. Based on the hypothesis testing at the significance level of 5%, it can be concluded that the learning approach using problem-based learning based on multiple intelligences was effective in terms of student’s achievement, mathematical connection ability, and self-esteem.

  16. Health promotion for young people with profound and multiple learning disabilities.

    Science.gov (United States)

    Davis, Kathy; Carter, Simone; Myers, Elizabeth; Rocca, Nicola

    2018-02-07

    Research confirms that children and young people with severe learning disabilities do not have the same level of access to high-quality care, health education and health promotion activities as children and young people without disabilities. This article discusses a quality improvement, action research project to investigate alternative approaches to health promotion that enhance the health and well-being of children and young people with complex neurodisabilities. The project involved assessment of school records and completion by staff of an eight-question survey. It found that the proactive approach of school nurses in raising awareness and understanding through questioning was positively received, and reinforced how meaningful and relevant information could be delivered to these young people. The project also had unexpected benefits, including more integrated team working, increased knowledge, greater awareness and understanding of the importance of health promotion participation, and student satisfaction. ©2018 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

  17. The Concept Mastery in the Perspective of Gender of Junior High School Students on Eclipse Theme in Multiple Intelligences-based of Integrated Earth and Space Science Learning

    Science.gov (United States)

    Liliawati, W.; Utama, J. A.; Mursydah, L. S.

    2017-03-01

    The purpose of this study is to identify gender-based concept mastery differences of junior high school students after the implementation of multiple intelligences-based integrated earth and space science learning. Pretest-posttest group design was employed to two different classes at one of junior high school on eclipse theme in Tasikmalaya West Java: one class for boys (14 students) and one class of girls (18 students). The two-class received same treatment. The instrument of concepts mastery used in this study was open-ended eight essay questions. Reliability test result of this instrument was 0.9 (category: high) while for validity test results were high and very high category. We used instruments of multiple intelligences identification and learning activity observation sheet for our analysis. The results showed that normalized N-gain of concept mastery for boys and girls were improved, respectively 0.39 and 0.65. Concept mastery for both classes differs significantly. The dominant multiple intelligences for boys were in kinesthetic while girls dominated in the rest of multiple intelligences. Therefor we concluded that the concept mastery was influenced by gender and student’s multiple intelligences. Based on this finding we suggested to considering the factor of gender and students’ multiple intelligences given in the learning activity.

  18. Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

    Science.gov (United States)

    Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

    2011-03-01

    In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

  19. Research translation to inform national health policies: learning from multiple perspectives in Uganda

    Directory of Open Access Journals (Sweden)

    Glass Nancy

    2011-03-01

    Full Text Available Abstract Background Research and evidence can have an impact on policy and practice, resulting in positive outcomes. However, research translation is a complex, dynamic and non-linear process. Although universities in Africa play a major role in generating research evidence, their strategic approaches to influence health policies and decision making are weak. This study was conducted with the aim of understanding the process of translating research into policy in order to guide the strategic direction of Makerere University College of Health Sciences (MakCHS and similar institutions in their quest to influence health outcomes nationally and globally. Methods A case study approach using 30 in-depth interviews with stakeholders involved in two HIV prevention research project was purposively selected. The study sought to analyze the research-to-policy discourses for the prevention of mother-to-child transmission (PMTCT and safe male circumcision (SMC. The analysis sought to identify entry points, strengths and challenges for research-to-policy processes by interviewing three major groups of stakeholders in Uganda – researchers (8, policy makers (12 and media practitioners (12. Results Among the factors that facilitated PMTCT policy uptake and continued implementation were: shared platforms for learning and decision making among stakeholders, implementation pilots to assess feasibility of intervention, the emerging of agencies to undertake operations research and the high visibility of policy benefits to child survival. In contrast, SMC policy processes were stalled for over two years after the findings of the Uganda study was made public. Among other factors, policy makers demanded additional research to assess implementation feasibility of SMC within ordinary health system context. High level leaders also publicly contested the SMC evidence and the underlying values and messages – a situation that reduced the coalition of policy champions

  20. Biomedical Ph.D. students enrolled in two elite universities in the United kingdom and the United States report adopting multiple learning relationships.

    Science.gov (United States)

    Kemp, Matthew W; Lazarus, Benjamin M; Perron, Gabriel G; Hanage, William P; Chapman, Elaine

    2014-01-01

    The ability to form multiple learning relationships is a key element of the doctoral learning environment in the biomedical sciences. Of these relationships, that between student and supervisor has long been viewed as key. There are, however, limited data to describe the student perspective on what makes this relationship valuable. In the present study, we discuss the findings of semi-structured interviews with biomedical Ph.D. students from the United Kingdom and the United States to: i) determine if the learning relationships identified in an Australian biomedical Ph.D. cohort are also important in a larger international student cohort; and ii) improve our understanding of student perceptions of value in their supervisory relationships. 32 students from two research intensive universities, one in the United Kingdom (n = 17), and one in the United States (n = 15) were recruited to participate in a semi-structured interview. Verbatim transcripts were transcribed, validated and analysed using a Miles and Huberman method for thematic analysis. Students reported that relationships with other Ph.D. students, post-doctoral scientists and supervisors were all essential to their learning. Effective supervisory relationships were perceived as the primary source of high-level project guidance, intellectual support and confidence. Relationships with fellow students were viewed as essential for the provision of empathetic emotional support. Technical learning was facilitated, almost exclusively, by relationships with postdoctoral staff. These data make two important contributions to the scholarship of doctoral education in the biomedical sciences. Firstly, they provide further evidence for the importance of multiple learning relationships in the biomedical doctorate. Secondly, they clarify the form of a 'valued' supervisory relationship from a student perspective. We conclude that biomedical doctoral programs should be designed to contain a minimum level of formalised

  1. Biomedical Ph.D. students enrolled in two elite universities in the United kingdom and the United States report adopting multiple learning relationships.

    Directory of Open Access Journals (Sweden)

    Matthew W Kemp

    Full Text Available The ability to form multiple learning relationships is a key element of the doctoral learning environment in the biomedical sciences. Of these relationships, that between student and supervisor has long been viewed as key. There are, however, limited data to describe the student perspective on what makes this relationship valuable. In the present study, we discuss the findings of semi-structured interviews with biomedical Ph.D. students from the United Kingdom and the United States to: i determine if the learning relationships identified in an Australian biomedical Ph.D. cohort are also important in a larger international student cohort; and ii improve our understanding of student perceptions of value in their supervisory relationships.32 students from two research intensive universities, one in the United Kingdom (n = 17, and one in the United States (n = 15 were recruited to participate in a semi-structured interview. Verbatim transcripts were transcribed, validated and analysed using a Miles and Huberman method for thematic analysis.Students reported that relationships with other Ph.D. students, post-doctoral scientists and supervisors were all essential to their learning. Effective supervisory relationships were perceived as the primary source of high-level project guidance, intellectual support and confidence. Relationships with fellow students were viewed as essential for the provision of empathetic emotional support. Technical learning was facilitated, almost exclusively, by relationships with postdoctoral staff.These data make two important contributions to the scholarship of doctoral education in the biomedical sciences. Firstly, they provide further evidence for the importance of multiple learning relationships in the biomedical doctorate. Secondly, they clarify the form of a 'valued' supervisory relationship from a student perspective. We conclude that biomedical doctoral programs should be designed to contain a minimum level

  2. Data Management for Flexible Access - Implementation and Lessons Learned from work with Multiple User Communities

    Science.gov (United States)

    Benedict, K. K.; Scott, S.; Hudspeth, W. B.

    2012-12-01

    There is no shortage of community-specific and generic data discovery and download platforms and protocols (e.g. CUAHSI HIS, DataONE, GeoNetwork Open Source, GeoPortal, OGC CSW, OAI PMH), documentation standards (e.g. FGDC, ISO 19115, EML, Dublin Core), data access and visualization standards and models (e.g. OGC WxS, OpenDAP), and general-purpose web service models (i.e. REST & SOAP) upon which Geo-informatics cyberinfrastructure (CI) may be built. When attempting to develop a robust platform that may service a wide variety of users and use cases the challenge is one of identifying which existing platform (if any) may support those current needs while also allowing for future expansion for additional capabilities. In the case of the implementation of a data storage, discovery and delivery platform to support the multiple projects at the Earth Data Analysis Center at UNM, no single platform or protocol met the joint requirements of two initial applications (the New Mexico Resource Geographic Information System [http://rgis.unm.edu] and the New Mexico EPSCoR Data Portal [http://nmepscor.org/dataportal]) and furthermore none met anticipated additional requirements as new applications of the platform emerged. As a result of this assessment three years ago EDAC embarked on the development of the Geographic Storage, Transformation, and Retrieval Engine (GSToRE) platform as a general purpose platform upon which n-tiered geospatially enabled data intensive applications could be built. When initially released in 2010 the focus was on the publication of dynamically generated Open Geospatial Consortium services based upon a PostgreSQL/PostGIS backend database. The identification of additional service interface requirements (implementation of the DataONE API and CUAHSI WaterML services), use cases provided by the NM EPSCoR education working group, and expanded metadata publication needs have led to a significant update to the underlying data management tier for GSToRE - the

  3. On the instance of misuse of unprofitable energy prices under cartel law

    International Nuclear Information System (INIS)

    Schoening, M.

    1993-01-01

    The practice of fixing prices which do not cover the costs can on principle not be considered an instance of misuse pursuant to Articles 22 Section 4 Clause 2 No. 2, 103 Section 5 Clause 2 No. 2 of the GWB (cartel laws). If the authority for the supervision of cartels takes action against companies operating with unprofitable prices, this constitutes a violation not only of cartel law, but also of the constitution. The cartel authorities have no right to dismiss a dominating company's referral to poor business prospects on the ground that its business report is theoretically manipulable. Rather, the burden of proof of concealment is on the authorities. (orig.) [de

  4. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

    Science.gov (United States)

    Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George

    2018-06-01

    Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional

  5. Incorporating Multiple-Choice Questions into an AACSB Assurance of Learning Process: A Course-Embedded Assessment Application to an Introductory Finance Course

    Science.gov (United States)

    Santos, Michael R.; Hu, Aidong; Jordan, Douglas

    2014-01-01

    The authors offer a classification technique to make a quantitative skills rubric more operational, with the groupings of multiple-choice questions to match the student learning levels in knowledge, calculation, quantitative reasoning, and analysis. The authors applied this classification technique to the mid-term exams of an introductory finance…

  6. The Effect of Differentiating Instruction Using Multiple Intelligences on Achievement in and Attitudes towards Science in Middle School Students with Learning Disabilities

    Science.gov (United States)

    Gomaa, Omema Mostafa Kamel

    2014-01-01

    This study investigated the effect of using differentiated instruction using multiple intelligences on achievement in and attitudes towards science in middle school students with learning disabilities. A total of 61 students identified with LD participated. The sample was randomly divided into two groups; experimental (n= 31 boys )and control (n=…

  7. Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems

    NARCIS (Netherlands)

    Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels

    Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem.

  8. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    (NMF) problem. This allows us to derive a novel multiplicative algorithm for solving hard and soft margin SVM. The algorithm follows as a natural extension of the updates for NMF and semi-NMF. No additional parameter setting, such as choosing learning rate, is required. Exploiting the connection......The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...... between SVM and NMF formulation, we show how NMF algorithms can be applied to the SVM problem. Multiplicative updates that we derive for SVM problem also represent novel updates for semi-NMF. Further this unified view yields algorithmic insights in both directions: we demonstrate that the Kernel Adatron...

  9. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

    Science.gov (United States)

    Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

    2017-12-12

    Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

  10. WEIBULL MULTIPLICATIVE MODEL AND MACHINE LEARNING MODELS FOR FULL-AUTOMATIC DARK-SPOT DETECTION FROM SAR IMAGES

    Directory of Open Access Journals (Sweden)

    A. Taravat

    2013-09-01

    Full Text Available As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method, synthetic aperture radar (SAR can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images.

  11. Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection from SAR Images

    Science.gov (United States)

    Taravat, A.; Del Frate, F.

    2013-09-01

    As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images.

  12. Design Research on Mathematics Education: Investigating The Progress of Indonesian Fifth Grade Students’ Learning on Multiplication of Fractions With Natural Numbers

    Directory of Open Access Journals (Sweden)

    Nenden Octavarulia Shanty

    2011-07-01

    Full Text Available This study aimed at investigating the progress of students’ learning onmultiplication fractions with natural numbers through the five activitylevels based on Realistic Mathematics Education (RME approachproposed by Streefland. Design research was chosen to achieve thisresearch goal. In design research, the Hypothetical Learning Trajectory(HLT plays important role as a design and research instrument. ThisHLT tested to thirty-seven students of grade five primary school (i.e.SDN 179 Palembang.The result of the classroom practices showed that measurement (lengthactivity could stimulate students’ to produce fractions as the first levelin learning multiplication of fractions with natural numbers.Furthermore, strategies and tools used by the students in partitioninggradually be developed into a more formal mathematics in whichnumber line be used as the model of measuring situation and the modelfor more formal reasoning. The number line then could bring thestudents to the last activity level, namely on the way to rules formultiplying fractions with natural numbers. Based on this findings, it is suggested that Streefland’s five activity levels can be used as aguideline in learning multiplication of fractions with natural numbers in which the learning process become a more progressive learning.

  13. Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

    Directory of Open Access Journals (Sweden)

    Lin Li

    2016-01-01

    Full Text Available The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA has good performance both in terms of precision and recall for instance matching from different data sets.

  14. Towards a new procreation ethic: the exemplary instance of cleft lip and palate.

    Science.gov (United States)

    Le Dref, Gaëlle; Grollemund, Bruno; Danion-Grilliat, Anne; Weber, Jean-Christophe

    2013-08-01

    The improvement of ultrasound scan techniques is enabling ever earlier prenatal diagnosis of developmental anomalies. In France, apart from cases where the mother's life is endangered, the detection of "particularly serious" conditions, and conditions that are "incurable at the time of diagnosis" are the only instances in which a therapeutic abortion can be performed, this applying up to the 9th month of pregnancy. Thus numerous conditions, despite the fact that they cause distress or pain or are socially disabling, do not qualify for therapeutic abortion, despite sometimes pressing demands from parents aware of the difficulties in store for their child and themselves, in a society that is not very favourable towards the integration and self-fulfilment of people with a disability. Cleft lip and palate (CLP), although it can be completely treated, is one of the conditions that considerably complicates the lives of child and parents. Nevertheless, the recent scope for making very early diagnosis of CLP, before the deadline for legal voluntary abortion, has not led to any wave of abortions. CLP in France has the benefit of a exceptional care plan, targeting both the health and the integration of the individuals affected. This article sets out, via the emblematic instance of CLP, to show how present fears of an emerging "domestic" or liberal eugenic trend could become redundant if disability is addressed politically and medically, so that individuals with a disability have the same social rights as any other citizen.

  15. Reversing Kristeva's first instance of abjection: the formation of self reconsidered.

    Science.gov (United States)

    McCabe, Janet L; Holmes, Dave

    2011-03-01

    Psychoanalyst Julia Kristeva defines the theoretical concept of abjection as an unconscious defence mechanism used to protect the self against threats to one's subjectivity. Kristeva suggests that the first instance of abjection in an individual's life occurs when the child abjects the mother. However, the instance of abjection addressed within this paper is the reverse of this: the abjection of the child, with a disability, by the parent, and more broadly society. Using the contemporary example of prenatal testing, the authors explore how parents of children with disabilities may be influenced in abjecting the child. The implications of abjection of the child are then used to explore normalization, routinization of care and the development of standardized care practices within health-care. Prenatal screening practices and standardized care permeate medical obstetric care and social discourses regarding pregnancy and childbirth, thereby affecting not only healthcare professionals but also parents in their position as consumers of health-care. In a time when the focus of health-care is increasingly placed on disease prevention and broader medical and social discourses glorify normalcy and consistency, the unconscious abjection of those that do not fit within these standards must be identified and addressed. © 2011 Blackwell Publishing Ltd.

  16. Emancipation trough the Artistic Experience and the Meaning of Handicap as Instance of Otherness

    Directory of Open Access Journals (Sweden)

    Robi Kroflič

    2014-03-01

    Full Text Available The key hypothesis of the article is that successful inter-mediation of art to vulnerable groups of people (including children depends on the correct identification of the nature of an artistic act and on the meaning that handicap—as an instance of otherness—has in the life of artists and spectators. A just access to the artistic experience is basically not the question of the distribution of artistic production (since if artistic object is principally accessible to all people, it will not reach vulnerable groups of spectators, but of ensuring artistic creativity and presentation. This presupposes a spectator as a competent being who is able to interact with the artistic object without our interpretative explanation and who is sensible to the instance of otherness (handicap is merely a specific form of otherness. The theory of emancipation from J. Ranciere, the theory of recognition from A. Honneth, and the theory of narration from P. Ricoeur and R. Kearney, as well as our experiences with a comprehensive inductive approach and artistic experience as one of its basic educational methods offer us a theoretical framework for such a model of art inter-mediation.

  17. A Refined Teaching-Learning Based Optimization Algorithm for Dynamic Economic Dispatch of Integrated Multiple Fuel and Wind Power Plants

    Directory of Open Access Journals (Sweden)

    Umamaheswari Krishnasamy

    2014-01-01

    Full Text Available Dynamic economic dispatch problem (DEDP for a multiple fuel power plant is a nonlinear and nonsmooth optimization problem when valve-point effects, multifuel effects, and ramp-rate limits are considered. Additionally wind energy is also integrated with the DEDP to supply the load for effective utilization of the renewable energy. Since the wind power may not be predicted, a radial basis function network (RBFN is presented to forecast a one-hour-ahead wind power to plan and ensure a reliable power supply. In this paper, a refined teaching-learning based optimization (TLBO is applied to minimize the overall cost of operation of wind-thermal power system. The TLBO is refined by integrating the sequential quadratic programming (SQP method to fine-tune the better solutions whenever discovered by the former method. To demonstrate the effectiveness of the proposed hybrid TLBO-SQP method, a standard DEDP and one practical DEDP with wind power forecasted are tested based on the practical information of wind speed. Simulation results validate the proposed methodology which is reasonable by ensuring quality solution throughout the scheduling horizon for secure operation of the system.

  18. Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions.

    Science.gov (United States)

    Goswami, R; Dufort, P; Tartaglia, M C; Green, R E; Crawley, A; Tator, C H; Wennberg, R; Mikulis, D J; Keightley, M; Davis, Karen D

    2016-05-01

    The frontotemporal cortical network is associated with behaviours such as impulsivity and aggression. The health of the uncinate fasciculus (UF) that connects the orbitofrontal cortex (OFC) with the anterior temporal lobe (ATL) may be a crucial determinant of behavioural regulation. Behavioural changes can emerge after repeated concussion and thus we used MRI to examine the UF and connected gray matter as it relates to impulsivity and aggression in retired professional football players who had sustained multiple concussions. Behaviourally, athletes had faster reaction times and an increased error rate on a go/no-go task, and increased aggression and mania compared to controls. MRI revealed that the athletes had (1) cortical thinning of the ATL, (2) negative correlations of OFC thickness with aggression and task errors, indicative of impulsivity, (3) negative correlations of UF axial diffusivity with error rates and aggression, and (4) elevated resting-state functional connectivity between the ATL and OFC. Using machine learning, we found that UF diffusion imaging differentiates athletes from healthy controls with significant classifiers based on UF mean and radial diffusivity showing 79-84 % sensitivity and specificity, and 0.8 areas under the ROC curves. The spatial pattern of classifier weights revealed hot spots at the orbitofrontal and temporal ends of the UF. These data implicate the UF system in the pathological outcomes of repeated concussion as they relate to impulsive behaviour. Furthermore, a support vector machine has potential utility in the general assessment and diagnosis of brain abnormalities following concussion.

  19. Online transfer learning with extreme learning machine

    Science.gov (United States)

    Yin, Haibo; Yang, Yun-an

    2017-05-01

    In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

  20. Iatrogenic alterations in the biodistribution of radiotracers as a result of drug therapy: Reported instances

    International Nuclear Information System (INIS)

    Hladik, W.B. III; Ponto, J.A.; Lentle, B.C.; Laven, D.L.

    1987-01-01

    This chapter is a compilation of reported instances in which the biodistribution of a radiopharmaceutical has been (or could be) modified by the administration of a therapeutic nonradioactive drug or contrast agent in such a way as to potentially interfere with the interpretation of the nuclear medicine study in question. This type of phenomenon is commonly referred to as a drug-radiopharmaceutical interaction. In this chapter, interactions are arranged according to the radiopharmaceutical involved; each interaction is characterized by use of the following descriptors: 1. Interfering drug: the interfering nonradioactive drug that alters the kinetics of the radiopharmaceutical and thus changes the resulting diagnostic data obtained from the study. 2. Nuclear medicine study affected: the nuclear medicine study in which the interaction is likely to occur. 3. Effect on image: the appearance of the image (or the effect on diagnostic data) which results from the interaction. 4. Significance: the potential clinical significance of the interaction

  1. The Impact of Culture On Smart Community Technology: The Case of 13 Wikipedia Instances

    Directory of Open Access Journals (Sweden)

    Zinayida Petrushyna

    2014-11-01

    Full Text Available Smart communities provide technologies for monitoring social behaviors inside communities. The technologies that support knowledge building should consider the cultural background of community members. The studies of the influence of the culture on knowledge building is limited. Just a few works consider digital traces of individuals that they explain using cultural values and beliefs. In this work, we analyze 13 Wikipedia instances where users with different cultural background build knowledge in different ways. We compare edits of users. Using social network analysis we build and analyze co- authorship networks and watch the networks evolution. We explain the differences we have found using Hofstede dimensions and Schwartz cultural values and discuss implications for the design of smart community technologies. Our findings provide insights in requirements for technologies used for smart communities in different cultures.

  2. Comparison of learning preferences of Turkish children who had been applied cochlear implantation in Turkey and Germany according to theory of multiple intelligence.

    Science.gov (United States)

    Sahli, Sanem; Laszig, Roland; Aschendorff, Antje; Kroeger, Stefanie; Wesarg, Thomas; Belgin, Erol

    2011-12-01

    The aim of the study is to determinate the using dominant multiple intelligence types and compare the learning preferences of Turkish cochlear implanted children aged four to ten in Turkey and Germany according to Theory of multiple intelligence. The study has been conducted on a total of 80 children and four groups in Freiburg/Germany and Ankara/Turkey. The applications have been done in University of Freiburg, Cochlear Implant Center in Germany, and University of Hacettepe, ENT Department, Audiology and Speech Pathology Section in Turkey. In this study, the data have been collected by means of General Information Form and Cochlear Implant Information Form applied to parents. To determine the dominant multiple intelligence types of children, the TIMI (Teele Inventory of Multiple Intelligences) which was developed by Sue Teele have been used. The study results exposed that there was not a statistically significant difference on dominant intelligence areas and averages of scores of multiple intelligence types in control groups (p>0.05). Although, the dominant intelligence areas were different (except for first dominant intelligence) in cochlear implanted children in Turkey and Germany, there was not a statistically significant difference on averages of scores of dominant multiple intelligence types. Every hearing impaired child who started training, should be evaluated in terms of multiple intelligence areas and identified strengths and weaknesses. Multiple intelligence activities should be used in their educational programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  3. Restaurants as Learning Organizations: A Multiple-Site Case Study of U.S. Non-Chain Restaurants

    Science.gov (United States)

    Boccia, Mark

    2016-01-01

    This study investigated the construct of the learning organization in the restaurant industry. Descriptive accounts of learning were gleaned from face-to-face interviews, focus groups, observations, document analysis, and data from the Dimensions of the Learning Organization Questionnaire (DLOQ) from 52 participants employed in three US…

  4. Effects of chronic multiple stress on learning and memory and the expression of Fyn, BDNF, TrkB in the hippocampus of rats.

    Science.gov (United States)

    Li, Xiao-Heng; Liu, Neng-Bao; Zhang, Min-Hai; Zhou, Yan-Ling; Liao, Jia-Wan; Liu, Xiang-Qian; Chen, Hong-Wei

    2007-04-20

    The effect of chronic stress on cognitive functions has been one of the hot topics in neuroscience. But there has been much controversy over its mechanism. The aim of this study was to investigate the effects of chronic multiple stress on spatial learning and memory as well as the expression of Fyn, BDNF and TrkB in the hippocampus of rats. Adult rats were randomly divided into control and chronic multiple stressed groups. Rats in the multiple stressed group were irregularly and alternatively exposed to situations of vertical revolution, sleep expropriation and restraint lasting for 6 weeks, 6 hours per day with night illumination for 6 weeks. Before and after the period of chronic multiple stresses, the performance of spatial learning and memory of all rats was measured using the Morris Water Maze (MWM). The expression of Fyn, BDNF and TrkB proteins in the hippocampus was assayed by Western blotting and immunohistochemical methods. The levels of Fyn and TrkB mRNAs in the hippocampus of rats were detected by RT-PCR technique. The escape latency in the control group and the stressed group were 15.63 and 8.27 seconds respectively. The performance of spatial learning and memory of rats was increased in chronic multiple stressed group (P < 0.05). The levels of Fyn, BDNF and TrkB proteins in the stressed group were higher than those of the control group (P < 0.05). The results of immunoreactivity showed that Fyn was present in the CA3 region of the hippocampus and BDNF positive particles were distributed in the nuclei of CA1 and CA3 pyramidal cells as well as DG granular cells. Quantitative analysis indicated that level of Fyn mRNA was also upregulated in the hippocampus of the stressed group (P < 0.05). Chronic multiple stress can enhance spatial learning and memory function of rats. The expression of Fyn, BDNF and TrkB proteins and the level of Fyn mRNA are increased in the stessed rat hippocampus. These suggest that Fyn and BDNF/TrkB signal transduction pathways may

  5. Group-Based Active Learning of Classification Models.

    Science.gov (United States)

    Luo, Zhipeng; Hauskrecht, Milos

    2017-05-01

    Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

  6. Procedural learning during declarative control.

    Science.gov (United States)

    Crossley, Matthew J; Ashby, F Gregory

    2015-09-01

    There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control? Behavioral, neuroimaging, and neuroscience data are somewhat mixed with respect to these questions. Human neuroimaging and animal lesion studies suggest independent learning and are mostly agnostic with respect to control. Human behavioral studies suggest active inhibition of behavioral output but have little to say regarding learning. The results of two perceptual category-learning experiments are described that strongly suggest that procedural learning does occur while the explicit system is in control of behavior and that this learning might be just as good as if the procedural system was controlling the response. These results are consistent with the idea that declarative memory systems inhibit the ability of the procedural system to access motor output systems but do not prevent procedural learning. (c) 2015 APA, all rights reserved).

  7. Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.

    Science.gov (United States)

    Chandra, Sharat; Pandey, Jyotsana; Tamrakar, Akhilesh Kumar; Siddiqi, Mohammad Imran

    2017-01-01

    In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map.

    Directory of Open Access Journals (Sweden)

    Stephen Grossberg

    Full Text Available Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs, both increase along this axis. Slower (faster subthreshold MPOs and slower (faster EPSPs correlate with larger (smaller grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic "neural relativity" that

  9. When palliative treatment achieves more than palliation: Instances of long-term survival after palliative radiotherapy

    Directory of Open Access Journals (Sweden)

    Madhup Rastogi

    2012-01-01

    Full Text Available Context: Palliative radiotherapy aims at symptom alleviation and improvement of quality of life. It may be effective in conferring a reasonable quantum of local control, as well as possibly prolonging survival on the short term. However, there can be rare instances where long-term survival, or even cure, results from palliative radiotherapy, which mostly uses sub-therapeutic doses. Aim: To categorize and characterize the patients with long-term survival and/or cure after palliative radiotherapy. Materials and Methods: This study is a retrospective analysis of hospital records of patients treated with palliative radiotherapy from 2001 to 2006 at the Regional Cancer Centre, Shimla. Results: Of the analyzed 963 patients who received palliative radiotherapy, 2.4% (n = 23 survived at least 5 years, with a large majority of these surviving patients (73.9%, n = 17 being free of disease. Conclusions: In addition to providing valuable symptom relief, palliative radiotherapy utilizing sub-therapeutic doses may, in a small proportion of patients, bestow long-term survival, and possibly cure. Rationally, such a favorable, but rare outcome cannot be expected with supportive care alone.

  10. On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning.

    Science.gov (United States)

    Grossberg, Stephen; Markowitz, Jeffrey; Cao, Yongqiang

    2011-12-01

    Visual object recognition is an essential accomplishment of advanced brains. Object recognition needs to be tolerant, or invariant, with respect to changes in object position, size, and view. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise in stages due to processes across multiple cortical areas. These processes include the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features with different levels of vigilance. The model predicts that data from the tradeoff and image morph tasks emerge from different levels of vigilance in the animals performing them. This result illustrates how different vigilance requirements of a task may change the course of category learning, notably the critical features that are attended and incorporated into learned category prototypes. The model outlines a path for developing an animal model of how defective vigilance control can lead to symptoms of various mental disorders, such as autism and amnesia. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification

    OpenAIRE

    Zhang, Chenrui; Peng, Yuxin

    2018-01-01

    Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self-supervised learning has emerged, which explores inherent supervisory signals implied in massive data for feature learning via solving auxiliary tasks. However, existing methods in this regard suffer from two limitations when extended to video classification. First, they focus only on a single task, whereas ignoring complementarity among different task-specific feat...

  12. Cyber Situation Awareness through Instance-Based Learning: Modeling the Security Analyst in a Cyber-Attack Scenario

    Science.gov (United States)

    2012-01-01

    Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2011...program and obtain control on the machine (event 21st out of 25). During the course of this simple scenario, a security analyst is able to observe...G. A. (1989). Recognition-primed deci- sions. In Rouse, W. B. (Ed.), Advances in man- machine system research (Vol. 5, pp. 47–92). Greenwich, CT

  13. Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

    Science.gov (United States)

    Xu, Lina; Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Piraud, Marie; Buck, Andreas; Shi, Kuangyu; Menze, Bjoern H

    2018-01-01

    The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68 Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68 Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k -Nearest Neighbors ( k -NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

  14. Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

    Directory of Open Access Journals (Sweden)

    Lina Xu

    2018-01-01

    Full Text Available The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM. 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs, V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF, k-Nearest Neighbors (k-NN, and support vector machine (SVM. The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

  15. The company objects keep: Linking referents together during cross-situational word learning.

    Science.gov (United States)

    Zettersten, Martin; Wojcik, Erica; Benitez, Viridiana L; Saffran, Jenny

    2018-04-01

    Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

  16. Impairment in explicit visuomotor sequence learning is related to loss of microstructural integrity of the corpus callosum in multiple sclerosis patients with minimal disability.

    Science.gov (United States)

    Bonzano, L; Tacchino, A; Roccatagliata, L; Sormani, M P; Mancardi, G L; Bove, M

    2011-07-15

    Sequence learning can be investigated by serial reaction-time (SRT) paradigms. Explicit learning occurs when subjects have to recognize a test sequence and has been shown to activate the frontoparietal network in both contralateral and ipsilateral hemispheres. Thus, the left and right superior longitudinal fasciculi (SLF), connecting the intra-hemispheric frontoparietal circuits, could have a role in explicit unimanual visuomotor learning. Also, as both hemispheres are involved, we could hypothesize that the corpus callosum (CC) has a role in this process. Pathological damage in both SLF and CC has been detected in patients with Multiple Sclerosis (PwMS), and microstructural alterations can be quantified by Diffusion Tensor Imaging (DTI). In light of these findings, we inquired whether PwMS with minimal disability showed impairments in explicit visuomotor sequence learning and whether this could be due to loss of white matter integrity in these intra- and inter-hemispheric white matter pathways. Thus, we combined DTI analysis with a modified version of SRT task based on finger opposition movements in a group of PwMS with minimal disability. We found that the performance in explicit sequence learning was significantly reduced in these patients with respect to healthy subjects; the amount of sequence-specific learning was found to be more strongly correlated with fractional anisotropy (FA) in the CC (r=0.93) than in the left (r=0.28) and right SLF (r=0.27) (p for interaction=0.005 and 0.04 respectively). This finding suggests that an inter-hemispheric information exchange between the homologous areas is required to successfully accomplish the task and indirectly supports the role of the right (ipsilateral) hemisphere in explicit visuomotor learning. On the other hand, we found no significant correlation of the FA in the CC and in the SLFs with nonspecific learning (assessed when stimuli are randomly presented), supporting the hypothesis that inter

  17. Review Essay: The Multiple Roles and Functions of Evaluation in the Context of E-Learning Programs

    Directory of Open Access Journals (Sweden)

    Thomas Link

    2005-01-01

    Full Text Available The German initiative "New Media in Education—the Higher Education Sector" is well documented. The present volume describes the project's evaluation concepts and preliminary results. In four chapters about goals, methodology, and possible future directions of evaluation research as well as some presentations of e-learning projects, this book offers a rich overview of appropriate evaluation models from fields such as psychology, the social sciences and quality management. This compilation encompasses theoretical works on the concepts of evaluation as well as presentations of actual evaluation studies. The reader thus gains insight into the extent of the requests and expectations an evaluation team has to satisfy as well as the process of implementing e-learning in a university context. The articles in this book contain thought-provoking ideas like Sigmar-Olaf TERGAN's assertion that there is no automatic relationship between the quality of an e-learning program and students' learning outcomes. This could lead us to conclude that we have to put more emphasis on situational parameters and that we have to use methods that are capable of capturing the different perspectives of those involved. While many authors accentuate the need to triangulate data sources, methods, theories and observers, the empirical method used most often in the context of e-learning is surveys, if possible online. This difference leads to questions about the function evaluation studies fulfill for e-learning programs. Karin HAUBRICH's demand that e-learning programs must be allowed to fail seems especially important here in order to make evaluation appear less as a control instrument and more as a way to get reliable feedback and to provide a catalyst for new developments. After reading this book, one might have the impression—and one might ask why this is the case—that e-learning requires evaluation in greater depth than "traditional" forms of teaching. An argument put

  18. Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature

    Science.gov (United States)

    Ke, Junyuan; Ho, Joyce C; Ghosh, Joydeep; Wallace, Byron C

    2018-01-01

    Background Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. Objective The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. Methods PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET’s phenotype representation with PheKnow-Cloud’s by using PheKnow-Cloud’s experimental setup. In PIVET’s framework, we also introduce a statistical model trained on domain expert–verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. Results PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with

  19. Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.

    Science.gov (United States)

    Henderson, Jette; Ke, Junyuan; Ho, Joyce C; Ghosh, Joydeep; Wallace, Byron C

    2018-05-04

    Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but

  20. The Development of LinguaBytes: An Interactive Tangible Play and Learning System to Stimulate the Language Development of Toddlers with Multiple Disabilities

    Directory of Open Access Journals (Sweden)

    Bart Hengeveld

    2008-01-01

    Full Text Available Young children with multiple disabilities (e.g., both cognitive and motor disabilities are confronted with severe limitations in language development from birth and later on. Stimulating the adult-child communication can decrease these limitations. Within LinguaBytes, a three-year research program, we try to stimulate language development by developing an interactive and adaptive play and learning environment, incorporating tangible objects and multimedia content, based on interactive storytelling and anchored instruction. The development of a product for such a heterogeneous user group presents substantial challenges. We use a Research-through-Design method, that is, an iterative process of developing subsequent experiential prototypes and then testing them in real-life settings, for example, a center for rehabilitation medicine. This article gives an outline of the development of the LinguaBytes play and learning environment from the earliest studies up to the current prototype, CLICK-IT.

  1. When memorized instances compete with rules: the case of number-noun agreement in written French.

    Science.gov (United States)

    Largy, Pierre; Cousin, Marie-Paule; Bryant, Peter; Fayol, Michel

    2007-05-01

    It is claimed by Totereau, Thévenin & Fayol (1997) that French children understand the rule for spelling the plural inflection very early on. However, no evidence contradicts the alternative that they learn the spelling of a word's singular and plural forms by treating the two forms as entirely different words. We tested this by asking French first and second graders (85 six-year-old and 89 seven-year-old children, respectively) to read and write rare words, either in just the singular or in just the plural, and then testing their spelling. The children tended to attach plural inflections to words which they had encountered only as plural and to omit them from words encountered before only as singular.

  2. Guide en matière d'évaluation à l'intention des instances qui ...

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

    Les évaluations sont un élément clé de la recherche pour le développement, puisqu'elles ... Comprendre le rôle des instances qui commandent des évaluations ... Avec l'aide du CRDI, l'Instituto de la Salud, Medio Ambiente, Economia y ...

  3. Analysis of Multiple Genomic Sequence Alignments: A Web Resource, Online Tools, and Lessons Learned From Analysis of Mammalian SCL Loci

    Science.gov (United States)

    Chapman, Michael A.; Donaldson, Ian J.; Gilbert, James; Grafham, Darren; Rogers, Jane; Green, Anthony R.; Göttgens, Berthold

    2004-01-01

    Comparative analysis of genomic sequences is becoming a standard technique for studying gene regulation. However, only a limited number of tools are currently available for the analysis of multiple genomic sequences. An extensive data set for the testing and training of such tools is provided by the SCL gene locus. Here we have expanded the data set to eight vertebrate species by sequencing the dog SCL locus and by annotating the dog and rat SCL loci. To provide a resource for the bioinformatics community, all SCL sequences and functional annotations, comprising a collation of the extensive experimental evidence pertaining to SCL regulation, have been made available via a Web server. A Web interface to new tools specifically designed for the display and analysis of multiple sequence alignments was also implemented. The unique SCL data set and new sequence comparison tools allowed us to perform a rigorous examination of the true benefits of multiple sequence comparisons. We demonstrate that multiple sequence alignments are, overall, superior to pairwise alignments for identification of mammalian regulatory regions. In the search for individual transcription factor binding sites, multiple alignments markedly increase the signal-to-noise ratio compared to pairwise alignments. PMID:14718377

  4. Analysis of Peer Learning Behaviors Using Multiple Representations in Virtual Reality and Their Impacts on Geometry Problem Solving

    Science.gov (United States)

    Hwang, Wu-Yuin; Hu, Shih-Shin

    2013-01-01

    Learning geometry emphasizes the importance of exploring different representations such as virtual manipulatives, written math formulas, and verbal explanations, which help students build math concepts and develop critical thinking. Besides helping individuals construct math knowledge, peer interaction also plays a crucial role in promoting an…

  5. Scripted Collaboration in Serious Gaming for Complex Learning: Effects of Multiple Perspectives when Acquiring Water Management Skills

    Science.gov (United States)

    Hummel, Hans G. K.; van Houcke, Jasper; Nadolski, Rob J.; van der Hiele, Tony; Kurvers, Hub; Lohr, Ansje

    2011-01-01

    This paper examines how learning outcomes from playing serious games can be enhanced by including scripted collaboration in the game play. We compared the quality of advisory reports, that students in the domain of water management had to draw up for an authentic case problem, both before and after collaborating on the problem with (virtual) peer…

  6. Pre-Service English as a Foreign Language Teachers' Perceptions of the Relationship between Multiple Intelligences and Foreign Language Learning

    Science.gov (United States)

    Savas, Perihan

    2012-01-01

    The relationship between intelligence, language, and learning is a challenging field of study. One way to study how this relationship occurs and works is to investigate the perceptions of advanced language learners. Therefore, this paper reports a study that was conducted to explore 160 pre-service English language teachers' perceptions about…

  7. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

    Directory of Open Access Journals (Sweden)

    Kai Wang

    2016-01-01

    Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

  8. Microgenetic patterns of children’s multiplication learning: Confirming the overlapping waves model by latent growth modeling

    NARCIS (Netherlands)

    van der Ven, S.H.G.; Boom, J.; Kroesbergen, E.H.; Leseman, P.P.M.

    2012-01-01

    Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as "overlapping waves." In the current microgenetic study, we attempted to model

  9. Knowing, Applying, and Reasoning about Arithmetic: Roles of Domain-General and Numerical Skills in Multiple Domains of Arithmetic Learning

    Science.gov (United States)

    Zhang, Xiao; Räsänen, Pekka; Koponen, Tuire; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Nurmi, Jari-Erik

    2017-01-01

    The longitudinal relations of domain-general and numerical skills at ages 6-7 years to 3 cognitive domains of arithmetic learning, namely knowing (written computation), applying (arithmetic word problems), and reasoning (arithmetic reasoning) at age 11, were examined for a representative sample of 378 Finnish children. The results showed that…

  10. Stable chlorine isotopes in arid non-marine basins: Instances and possible fractionation mechanisms

    International Nuclear Information System (INIS)

    Eastoe, C.J.

    2016-01-01

    Stable chlorine isotopes are useful geochemical tracers in processes involving the formation and evolution of evaporitic halite. Halite and dissolved chloride in groundwater that has interacted with halite in arid non-marine basins has a δ 37 Cl range of 0 ± 3‰, far greater than the range for marine evaporites. Basins characterized by high positive (+1 to +3‰), near-0‰, and negative (−0.3 to −2.6‰) are documented. Halite in weathered crusts of sedimentary rocks has δ 37 Cl values as high as +5.6‰. Salt-excluding halophyte plants excrete salt with a δ 37 Cl range of −2.1 to −0.8‰. Differentiated rock chloride sources exist, e.g. in granitoid micas, but cannot provide sufficient chloride to account for the observed data. Single-pass application of known fractionating mechanisms, equilibrium salt-crystal interaction and disequilibrium diffusive transport, cannot account for the large ranges of δ 37 Cl. Cumulative fractionation as a result of multiple wetting-drying cycles in vadose playas that produce halite crusts can produce observed positive δ 37 Cl values in hundreds to thousands of cycles. Diffusive isotope fractionation as a result of multiple wetting-drying cycles operating at a spatial scale of 1–10 cm can produce high δ 37 Cl values in residual halite. Chloride in rainwater is subject to complex fractionation, but develops negative δ 37 Cl values in certain situations; such may explain halite deposits with bulk negative δ 37 Cl values. Future field studies will benefit from a better understanding of hydrology and rainwater chemistry, and systematic collection of data for both Cl and Br. - Highlights: • δ 37 Cl in halite from arid, non-marine sedimentary basins ranges from −3 to +5.5‰. • Cl − in vadose playas may develop large isotope fractionation through cyclic wetting and drying. • Cl − in phreatic playas undergoes no fractionation as a result of cyclic wetting and drying. • Cl − in weathered

  11. Adapting the Unique Minds Program: Exploring the Feasibility of a Multiple Family Intervention for Children with Learning Disabilities in the Context of Spain.

    Science.gov (United States)

    López-Larrosa, Silvia; González-Seijas, Rosa M; Carpenter, John S W

    2017-06-01

    The Unique Minds Program (Stern, Unique Minds Program, 1999) addresses the socio-emotional needs of children with learning disabilities (LD) and their families. Children and their parents work together in a multiple family group to learn more about LD and themselves as people with the capacity to solve problems in a collaborative way, including problems in family school relationships. This article reports the cultural adaptation of the program for use in Spain and findings from a feasibility study involving three multiple family groups and a total of 15 children and 15 mothers, using a pre-post design. This Spanish adaptation of the program is called "Mentes Únicas". Standardized outcome measures indicated an overall statistically significant decrease in children's self-rated maladjustment and relationship difficulties by the end of the program. Improvements were endorsed by most mothers, although they were not always recognized by the children's teachers. The program had a high level of acceptability: Mothers and children felt safe, understood, and helped throughout the sessions. The efficacy of the adapted intervention for the context of Spain remains to be tested in a more rigorous study. © 2016 Family Process Institute.

  12. Multiple Intelligences and ESL Teaching and Learning: An Investigation in KG II Classrooms in One Private School in Beirut, Lebanon

    Science.gov (United States)

    Ghamrawi, Norma

    2014-01-01

    This study examined teachers' use of the Multiple Intelligences Theory on vocabulary acquisition by preschoolers during English as a second language (ESL) classes in a K-12 school in Lebanon. Eighty kindergartners (KG II, aged 5 years) and eight teachers constituted the sample. The study used mixed methods, including observations of videotaped…

  13. The Development of School Autonomy and Accountability in Hong Kong: Multiple Changes in Governance, Work, Curriculum, and Learning

    Science.gov (United States)

    Ko, James; Cheng, Yin Cheong; Lee, Theodore Tai Hoi

    2016-01-01

    Purpose: The purpose of this paper is to trace the development of school autonomy and accountability and related multiple changes and impacts in key areas of school education in Hong Kong since implementing school-based management (SBM) from 1990s. Design/methodology/approach: To explore the evolution and the uniqueness of autonomy and…

  14. The Social Construction of Place Meaning: Exploring Multiple Meanings of Place as an Outdoor Teaching and Learning Environment

    OpenAIRE

    Gkoutis, Georgios

    2014-01-01

    This investigation explores the meanings primary school teachers who apply outdoor learning and teaching methods associate withthe places that encompass their teaching practices. A symbolic interactionist framework coupled with a social constructionistorientation was employed to analyze data collected from semi-structured interviews and photo elicitation techniques. The findingsillustrated that meaning ascribed to place derived from the interactional processes between the study’s respondents ...

  15. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  16. A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders

    OpenAIRE

    Nguyen, Phong; Dines, John; Krasnodebski, Jan

    2017-01-01

    Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria. However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing decisions ultimately determine the success of the recommendation systems. In this work, we address the multi-objective, multi-stakeholder, recommendation problem involving one or ...

  17. Instance Analysis for the Error of Three-pivot Pressure Transducer Static Balancing Method for Hydraulic Turbine Runner

    Science.gov (United States)

    Weng, Hanli; Li, Youping

    2017-04-01

    The working principle, process device and test procedure of runner static balancing test method by weighting with three-pivot pressure transducers are introduced in this paper. Based on an actual instance of a V hydraulic turbine runner, the error and sensitivity of the three-pivot pressure transducer static balancing method are analysed. Suggestions about improving the accuracy and the application of the method are also proposed.

  18. Correspondence of Concept Hierarchies in Semantic Web Based upon Global Instances and its Application to Facility Management Database

    Science.gov (United States)

    Takahashi, Hiroki; Nishi, Yuusuke; Gion, Tomohiro; Minami, Shinichi; Fukunaga, Tatsuya; Ogata, Jiro; Yoshie, Osamu

    Semantic Web is the technology which determines the relevance of data over the Web using meta-data and which enables advanced search of global information. It is now desired to develop and apply this technology to many situations of facility management. In facility management, vocabulary should be unified to share the database of facilities for generating optimal maintenance schedule and so on. Under such situations, ontology databases are usually used to describe composition or hierarchy of facility parts. However, these vocabularies used in databases are not unified even between factories of same company, and this situation causes communication hazard between them. Moreover, concept involved in the hierarchy cannot be corresponded each other. There are some methods to correspond concepts of different hierarchy. But these methods have some defects, because they only attend target hierarchy itself and the number of instances. We propose improved method for corresponding concepts between different concepts' hierarchies, which uses other hierarchies all over the world of Web and the distance of instances to identify their relations. Our method can work even if the sets of instances belonging to the concepts are not identical.

  19. Heads for learning, tails for memory: Reward, reinforcement and a role of dopamine in determining behavioural relevance across multiple timescales

    Directory of Open Access Journals (Sweden)

    Mathieu eBaudonnat

    2013-10-01

    Full Text Available Dopamine has long been tightly associated with aspects of reinforcement learning and motivation in simple situations where there are a limited number of stimuli to guide behaviour and constrained range of outcomes. In naturalistic situations, however, there are many potential cues and foraging strategies that could be adopted, and it is critical that animals determine what might be behaviourally relevant in such complex environments. This requires not only detecting discrepancies with what they have recently experienced, but also identifying similarities with past experiences stored in memory. Here, we review what role dopamine might play in determining how and when to learn about the world, and how to develop choice policies appropriate to the situation faced. We discuss evidence that dopamine is shaped by motivation and memory and in turn shapes reward-based memory formation. In particular, we suggest that hippocampal-striatal-dopamine networks may interact to determine how surprising the world is and to either inhibit or promote actions at time of behavioural uncertainty.

  20. Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach.

    Directory of Open Access Journals (Sweden)

    John J Wade

    Full Text Available In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a "learning signal" to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity, and the modeling strategy may be extended to coordination among remote neuron clusters.

  1. Imbalanced Class Learning in Epigenetics

    OpenAIRE

    Haque, M. Muksitul; Skinner, Michael K.; Holder, Lawrence B.

    2014-01-01

    In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the mino...

  2. Feature and Region Selection for Visual Learning.

    Science.gov (United States)

    Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

    2016-03-01

    Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

  3. Alpha test results for a Housing First eLearning strategy: the value of multiple qualitative methods for intervention design.

    Science.gov (United States)

    Ahonen, Emily Q; Watson, Dennis P; Adams, Erin L; McGuire, Alan

    2017-01-01

    Detailed descriptions of implementation strategies are lacking, and there is a corresponding dearth of information regarding methods employed in implementation strategy development. This paper describes methods and findings related to the alpha testing of eLearning modules developed as part of the Housing First Technical Assistance and Training (HFTAT) program's development. Alpha testing is an approach for improving the quality of a product prior to beta (i.e., real world) testing with potential applications for intervention development. Ten participants in two cities tested the modules. We collected data through (1) a structured log where participants were asked to record their experiences as they worked through the modules; (2) a brief online questionnaire delivered at the end of each module; and (3) focus groups. The alpha test provided useful data related to the acceptability and feasibility of eLearning as an implementation strategy, as well as identifying a number of technical issues and bugs. Each of the qualitative methods used provided unique and valuable information. In particular, logs were the most useful for identifying technical issues, and focus groups provided high quality data regarding how the intervention could best be used as an implementation strategy. Alpha testing was a valuable step in intervention development, providing us an understanding of issues that would have been more difficult to address at a later stage of the study. As a result, we were able to improve the modules prior to pilot testing of the entire HFTAT. Researchers wishing to alpha test interventions prior to piloting should balance the unique benefits of different data collection approaches with the need to minimize burdens for themselves and participants.

  4. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  5. Multi-item economic production quantity model for imperfect items with multiple production setups and rework under the effect of preservation technology and learning environment

    Directory of Open Access Journals (Sweden)

    Preeti Jawla

    2016-09-01

    Full Text Available This study aims to investigate the multi-item inventory model in a production/rework system with multiple production setups. Rework can be depicted as the transformation of production rejects, failed, or non-conforming items into re-usable products of the same or lower quality during or after inspection. Rework is very valuable and profitable, especially if materials are limited in availability and also pricey. Moreover, rework can be a good contribution to a ‘green image environment’. In this paper, we establish a multi-item inventory model to determine the optimal inventory replenishment policy for the economic production quantity (EPQ model for imperfect, deteriorating items with multiple productions and rework under inflation and learning environment. In inventory modelling, Inflation plays a very important role. In one cycle, production system produces items in n production setups and one rework setup, i.e. system follows (n, 1 policy. To reduce the deterioration of products preservation technology investment is also considered in this model. Holding cost is taken as time dependent. We develop expressions for the average profit per time unit, including procurement of input materials, costs for production, rework, deterioration cost and storage of serviceable and reworkable lots. Using those expressions, the proposed model is demonstrated numerically and the sensitivity analysis is also performed to study the behaviour of the model.

  6. Governance and management dynamics of landscape restoration at multiple scales: Learning from successful environmental managers in Sweden.

    Science.gov (United States)

    Dawson, Lucas; Elbakidze, Marine; Angelstam, Per; Gordon, Johanna

    2017-07-15

    Due to a long history of intensive land and water use, habitat networks for biodiversity conservation are generally degraded in Sweden. Landscape restoration (LR) is an important strategy for achieving representative and functional green infrastructures. However, outcomes of LR efforts are poorly studied, particularly the dynamics of LR governance and management. We apply systems thinking methods to a series of LR case studies to analyse the causal structures underlying LR governance and management in Sweden. We show that these structures appear to comprise of an interlinked system of at least three sets of drivers and four core processes. This system exhibits many characteristics of a transformative change towards an integrated, adaptive approach to governance and management. Key challenges for Swedish LR projects relate to institutional and regulatory flexibility, the timely availability of sufficient funds, and the management of learning and knowledge production processes. In response, successful project leaders develop several key strategies to manage complexity and risk, and enhance perceptions of the attractiveness of LR projects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

    Science.gov (United States)

    Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

    2016-01-01

    The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

  8. Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

    Science.gov (United States)

    Santoyo-Ramón, José Antonio

    2018-01-01

    This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA). PMID:29642638

  9. Transgenic overexpression of adenosine kinase in brain leads to multiple learning impairments and altered sensitivity to psychomimetic drugs.

    Science.gov (United States)

    Yee, Benjamin K; Singer, Philipp; Chen, Jiang-Fan; Feldon, Joram; Boison, Detlev

    2007-12-01

    The neuromodulator adenosine fulfills a unique role in the brain affecting glutamatergic neurotransmission and dopaminergic signaling via activation of adenosine A1 and A2A receptors, respectively. The adenosine system is thus ideally positioned to integrate glutamatergic and dopaminergic neurotransmission, which in turn could affect behavior and cognition. In the adult brain, adenosine levels are largely regulated by its key metabolic enzyme adenosine kinase (ADK), which may assume the role of an 'upstream regulator' of these two neurotransmitter pathways. To test this hypothesis, transgenic mice with an overexpression of ADK in brain (Adk-tg), and therefore reduced brain adenosine levels, were evaluated in a panel of behavioral and psychopharmacological assays to assess possible glutamatergic and dopaminergic dysfunction. In comparison to non-transgenic control mice, Adk-tg mice are characterized by severe learning deficits in the Morris water maze task and in Pavlovian conditioning. The Adk-tg mice also exhibited reduced locomotor reaction to systemic amphetamine, whereas their reaction to the non-competitive N-methyl-d-aspartate receptor antagonist MK-801 was enhanced. Our results confirmed that ADK overexpression could lead to functional concomitant alterations in dopaminergic and glutamatergic functions, which is in keeping with the hypothesized role of ADK in the balance and integration between glutamatergic and dopaminergic neurotransmission. The present findings are of relevance to current pathophysiological hypotheses of schizophrenia and its pharmacotherapy.

  10. Eurochemic: failure or instance

    International Nuclear Information System (INIS)

    Busekist, O. von

    1982-01-01

    In this article, the author draws up a balance sheet of twenty-three years of good and bad luck of the European venture Eurochemic. It turns out that an important number of errors of judgement are the source of the present difficult situation of the European market for the nuclear reprocessing. (AF)

  11. Theory, For Instance

    DEFF Research Database (Denmark)

    Balle, Søren Hattesen

    This paper takes its starting point in a short poem by Wallace Stevens from 1917, which incidentally bears the title “Theory”. The poem can be read as a parable of theory, i.e., as something literally ’thrown beside’ theory (cf. OED: “...“). In the philosophical tradition this is also how the style of theory has been figured, that is to say: as something that is incidental to it or just happens to be around as so much paraphernalia. In my reading of Stevens’ poem I shall argue that this is exactly the position from which Stevens takes off when he assumes...... the task of writing a personified portrait of theory. Theory emerges as always beside(s) itself in what constitutes its style, but the poem also suggests that theory’s style is what gives theory both its power and its contingency. Figured as a duchess Theoria is only capable of retaining her power...

  12. Herding: a new phenomenon affecting medical decision-making in multiple sclerosis care? Lessons learned from DIScUTIR MS

    Directory of Open Access Journals (Sweden)

    Saposnik G

    2017-01-01

    Full Text Available Gustavo Saposnik,1–3 Jorge Maurino,4 Angel P Sempere,5 Christian C Ruff,2 Philippe N Tobler2 1Division of Neurology, Department of Medicine, St Michael’s Hospital, University of Toronto, Toronto, ON, Canada; 2Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland; 3Li Ka Shing Knowledge Institute, St Michael’s Hospital, University of Toronto, Toronto, ON, Canada; 4Neuroscience Area, Medical Department, Roche Farma, Madrid, 5Department of Neurology, Hospital General Universitario de Alicante, Alicante, Spain Purpose: Herding is a phenomenon by which individuals follow the behavior of others rather than deciding independently on the basis of their own private information. A herding-like phenomenon can occur in multiple sclerosis (MS when a neurologist follows a therapeutic recommendation by a colleague even though it is not supported by best practice clinical guidelines. Limited information is currently available on the role of herding in medical care. The objective of this study was to determine the prevalence (and its associated factors of herding in the management of MS.Methods: We conducted a study among neurologists with expertise in MS care throughout Spain. Participants answered questions regarding the management of 20 case scenarios commonly encountered in clinical practice and completed 3 surveys and 4 experimental paradigms based on behavioral economics. The herding experiment consisted of a case scenario of a 40-year-old woman who has been stable for 3 years on subcutaneous interferon and developed a self-limited neurological event. There were no new magnetic resonance imaging (MRI lesions. Her neurological examination and disability scores were unchanged. She was advised by an MS neurologist to switch from interferon to fingolimod against best practice guidelines. Multivariable logistic regression analysis was conducted to evaluate factors associated with herding

  13. Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution.

    Science.gov (United States)

    Schrum, Jacob; Miikkulainen, Risto

    2016-01-01

    Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

  14. Speech and language therapists' approaches to communication intervention with children and adults with profound and multiple learning disability.

    Science.gov (United States)

    Goldbart, Juliet; Chadwick, Darren; Buell, Susan

    2014-11-01

    People with profound intellectual and multiple disabilities (PMLD) have communication impairments as one defining characteristic. To explore speech and language therapists' (SLTs) decision making in communication interventions for people with PMLD, in terms of the intervention approaches used, the factors informing the decisions to use specific interventions and the extent to which the rationales underpinning these decisions related to the components of evidence based practice (EBP), namely empirical evidence, clinical experience and client/carer views and values. A questionnaire on communication assessment and intervention for people with PMLD was sent to SLTs in the UK to elicit information on: the communication intervention approaches they used; their rationales for their intervention choices; their use of published evidence to inform decision making. Intensive interaction and objects of reference were the communication interventions most often used with people with PMLD, with some differences between children and adults evident. Rationales provided conformed somewhat to the EBP framework though extension of the existing framework and addition of practical and organizational considerations led to a revised typology of rationale for decision making. Rationales most frequently related to the empowerment, development and behavioural preferences of the person with PMLD. Empirical research evidence was seldom mentioned by SLTs as informing intervention decision making leading to very diverse practice. There is a need for further research on the effectiveness of commonly used but under-evaluated interventions. There is also a need to alert SLTs to the evidence base supporting other approaches, particularly switch-based, cause and effect approaches. © 2014 Royal College of Speech and Language Therapists.

  15. Herding: a new phenomenon affecting medical decision-making in multiple sclerosis care? Lessons learned from DIScUTIR MS.

    Science.gov (United States)

    Saposnik, Gustavo; Maurino, Jorge; Sempere, Angel P; Ruff, Christian C; Tobler, Philippe N

    2017-01-01

    Herding is a phenomenon by which individuals follow the behavior of others rather than deciding independently on the basis of their own private information. A herding-like phenomenon can occur in multiple sclerosis (MS) when a neurologist follows a therapeutic recommendation by a colleague even though it is not supported by best practice clinical guidelines. Limited information is currently available on the role of herding in medical care. The objective of this study was to determine the prevalence (and its associated factors) of herding in the management of MS. We conducted a study among neurologists with expertise in MS care throughout Spain. Participants answered questions regarding the management of 20 case scenarios commonly encountered in clinical practice and completed 3 surveys and 4 experimental paradigms based on behavioral economics. The herding experiment consisted of a case scenario of a 40-year-old woman who has been stable for 3 years on subcutaneous interferon and developed a self-limited neurological event. There were no new magnetic resonance imaging (MRI) lesions. Her neurological examination and disability scores were unchanged. She was advised by an MS neurologist to switch from interferon to fingolimod against best practice guidelines. Multivariable logistic regression analysis was conducted to evaluate factors associated with herding. Out of 161 neurologists who were invited to participate, 96 completed the study (response rate: 60%). Herding was present in 75 (78.1%), having a similar prevalence in MS experts and general neurologists (68.8% vs 82.8%; P =0.12). In multivariate analyses, the number of MS patients seen per week was positively associated with herding (odds ratio [OR] 1.08, 95% CI 1.01-1.14). Conversely, physician's age, gender, years of practice, setting of practice, or risk preferences were not associated with herding. Herding was a common phenomenon affecting nearly 8 out of 10 neurologists caring for MS patients. Herding may

  16. Applying Multiple Intelligences

    Science.gov (United States)

    Christodoulou, Joanna A.

    2009-01-01

    The ideas of multiple intelligences introduced by Howard Gardner of Harvard University more than 25 years ago have taken form in many ways, both in schools and in other sometimes-surprising settings. The silver anniversary of Gardner's learning theory provides an opportunity to reflect on the ways multiple intelligences theory has taken form and…

  17. Assessing Children's Multiplicative Thinking

    Science.gov (United States)

    Hurst, Chris; Hurrell, Derek

    2016-01-01

    Multiplicative thinking is a "big idea" of mathematics that underpins much of the mathematics learned beyond the early primary school years. This paper reports on a current study that utilises an interview tool and a written quiz to gather data about children's multiplicative thinking. The development of the tools and some of the…

  18. Estrategias de aprendizaje-enseñanza e inteligencias múltiples: ¿Aprendemos todos igual? Teaching-learning strategies and multiple intelligence: Do all of us learn in the same way?

    Directory of Open Access Journals (Sweden)

    Magaly Hernández Más

    2006-04-01

    Full Text Available La Pedagogía se enfrenta a un gran reto en estos momentos y éste es dirigir el proceso enseñanza aprendizaje de forma tal que el educando desarrolle un pensamiento reflexivo, crítico, que pueda aplicar, desde el punto de vista cognoscitivo, estrategias para aprender por sí mismo. El presente artículo aborda las estrategias de aprendizaje-enseñanza en una taxonomía general que permite tanto al profesor como al estudiante tener una comprensión cabal de este fenómeno dentro del proceso de enseñanza aprendizaje. Además se valora el papel de las inteligencias múltiples en este proceso. Ambos aspectos juegan un rol decisivo en el entorno didáctico y pedagógico de hoy cuando la universalización de la enseñanza se convierte en una realidad en la educación cubana actual y muy en especial en los centros de educación superior del paísThe teaching process has a new challenge nowadays: that of conducting the process in such a way that the students develop critical thinking abilities that allow them to learn for themselves. This paper presents a general taxonomy of learning strategies that will permit both students and teachers to have a more comprehensive background of this pedagogical issue. It also illustrates the role played by multiple intelligences in the teaching and learning process. These two aspects are now of key importance in the light of the most recent advances of education in Cuba, especially at higher education centers.

  19. Two new Bent-toed Geckos of the Cyrtodactylus pulchellus complex from Peninsular Malaysia and multiple instances of convergent adaptation to limestone forest ecosystems.

    Science.gov (United States)

    Grismer, L Lee; Wood, Perry L; Anuar, Shahrul; Grismer, Marta S; Quah, Evan S H; Murdoch, Matthew L; Muin, Mohd Abdul; Davis, Hayden R; Aguilar, César; Klabacka, Randy; Cobos, Anthony J; Aowphol, Anchalee; Sites, Jack W

    2016-04-25

    A new species of limestone cave-adapted gecko of the Cyrtodactylus pulchellus complex, C. hidupselamanya sp. nov., is described from an isolated karst formation at Felda Chiku 7, Kelantan, Peninsular Malaysia. This formation is scheduled to be completely quarried for its mineral content. From what we know about the life history of C. hidupselamanya sp. nov., this will result in its extinction. A new limestone forest-adapted species, C. lenggongensis sp. nov., from the Lenggong Valley, Perak was previously considered to be conspecific with C. bintangrendah but a re-evaluation of morphological, color pattern, molecular, and habitat preference indicates that it too is a unique lineage worthy of specific recognition. Fortunately C. lenggongensis sp. nov. is not facing extinction because its habitat is protected by the UNESCO Archaeological Heritage of the Lenggong Valley due to the archaeological significance of that region. Both new species can be distinguished from all other species of Cyrtodactylus based on molecular evidence from the mitochondrial gene ND2 and its flanking tRNAs as well as having unique combinations of morphological and color pattern characteristics. Using a time-calibrated BEAST analysis we inferred that the evolution of a limestone habitat preference and its apparently attendant morphological and color pattern adaptations evolved independently at least four times in the C. pulchellus complex between 26.1 and 0.78 mya.

  20. Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-11-01

    Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.

  1. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  2. Learning of Alignment Rules between Concept Hierarchies

    Science.gov (United States)

    Ichise, Ryutaro; Takeda, Hideaki; Honiden, Shinichi

    With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

  3. Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development.

    Science.gov (United States)

    Morse, Anthony F; Cangelosi, Angelo

    2017-02-01

    Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. Copyright © 2016 Cognitive Science Society, Inc.

  4. Leveraging multiple datasets for deep leaf counting

    OpenAIRE

    Dobrescu, Andrei; Giuffrida, Mario Valerio; Tsaftaris, Sotirios A

    2017-01-01

    The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method tre...

  5. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  6. An Investigation into the Factors Affecting the Design of Nature-Compatible Recreational-Residential Complexes- Instance Analysis

    Directory of Open Access Journals (Sweden)

    Seyyedeh Fatemeh Safavi Mirmahalleh

    2017-02-01

    Full Text Available Explaining tourism concepts, standards of recreational-residential complexes and the methods for accurate treating with nature, this paper tries to study the suitable instances of recreational-residential complexes and to adopt their positive aspects as a design strategy. SWOT matrix was built based on the weaknesses and strengths of the project site and design principles were derived by observing the extracted influential factors. Considering land topography, for example, different areas of buildings were shifted and combined with the nature. - Residential and public zones gained a nice view towards the nature by keeping their orientation and extending them in east-west direction - Service section connected more appropriately with residential and public areas - Sections which do not need extra light such as W.Cs, storages and installation rooms, were considered in a side of the building which was adjacent to soil - In residential section, rhythm was implemented in ceilings and windows - Golden values and proportions were used to design the plane and façade of the complex

  7. Impact of extracorporeal shock waves on the human skin with cellulite: A case study of an unique instance

    Science.gov (United States)

    Kuhn, Christoph; Angehrn, Fiorenzo; Sonnabend, Ortrud; Voss, Axel

    2008-01-01

    In this case study of an unique instance, effects of medium-energy, high-focused extracorporeal generated shock waves (ESW) onto the skin and the underlying fat tissue of a cellulite afflicted, 50-year-old woman were investigated. The treatment consisted of four ESW applications within 21 days. Diagnostic high-resolution ultrasound (Collagenoson) was performed before and after treatment. Directly after the last ESW application, skin samples were taken for histopathological analysis from the treated and from the contra-lateral untreated area of skin with cellulite. No damage to the treated skin tissue, in particular no mechanical destruction to the subcutaneous fat, could be demonstrated by histopathological analysis. However an astounding induction of neocollageno- and neoelastino-genesis within the scaffolding fabric of the dermis and subcutis was observed. The dermis increased in thickness as well as the scaffolding within the subcutaneous fat-tissue. Optimization of critical application parameters may turn ESW into a noninvasive cellulite therapy. PMID:18488890

  8. TYPOLOGY OF THE REALITY STATUS CATEGORY IN SELECTED LANGUAGES. IS THE HABITUAL IN POLISH AN INSTANCE OF (IRREALIS OR MODALITY?

    Directory of Open Access Journals (Sweden)

    Paulina Pietras

    2017-10-01

    Full Text Available The present article is aimed at examining the category of the reality status by discussing the dichotomy “realis / irrealis” in the context of the categories of modality, habituality and futurity. Prototype analysis is juxtaposed with scope analysis, and the category of the habitual is discussed from the typological perspective as well as from the perspective of its connection with the category of futurity. The paper presents aspect diversity of habituals (perfective and imperfective aspect and its contextual implications as well as the differentiation between the habitual and modality. A special focus is on the prototype analysis and its application instances in Polish, English and Hebrew. The primary objective of the paper is to show that, although it is possible to treat irrealis as notional category, the habituals in Polish and many other Slavic languages (e.g. Czech should be identified with the modality domain rather than irrealis category. The paper is also an attempt to provide an insight into the distinction between (irrealis and encoding systems of modalities as the habitual aspect displays modal category features in many languages (including Polish.

  9. Representation of architectural artifacts: definition of an approach combining the complexity of the 3d digital instance with the intelligibility of the theoretical model.

    Directory of Open Access Journals (Sweden)

    David Lo Buglio

    2012-12-01

    Full Text Available EnWith the arrival of digital technologies in the field of architectural documentation, many tools and methods for data acquisition have been considerably developed. However, these developments are primarily used for recording colorimetric and dimensional properties of the objects processed. The actors, of the disciplines concerned by 3D digitization of architectural heritage, are facing with a large number of data, leaving the survey far from its cognitive dimension. In this context, it seems necessary to provide innovative solutions in order to increase the informational value of the representations produced by strengthen relations between "multiplicity" of data and "intelligibility" of the theoretical model. With the purpose of answering to the lack of methodology we perceived, this article therefore offers an approach to the creation of representation systems that articulate the digital instance with the geometric/semantic model.ItGrazie all’introduzione delle tecnologie digitali nel campo della documentazione architettonica, molti strumenti e metodi di acquisizione hanno avuto un notevole sviluppo. Tuttavia, questi sviluppi si sono principalmente concentrati sulla registrazione e sulla restituzione delle proprietà geometriche e colorimetriche degli oggetti di studio. Le discipline interessate alla digitalizzazione 3D del patrimonio architettonico hanno pertanto la possibilità di produrre delle grandi quantità di dati attraverso un’evoluzione delle pratiche di documentazione che potrebbero progressivamente far scomparire la dimensione cognitiva del rilievo. In questo contesto, appare necessario fornire soluzioni innovative per aumentare il valore informativo delle rappresentazioni digitali tramite l’identificazione delle relazioni potenziali che è possibile costruire fra le nozioni di "molteplicità" ed "intelligibilità". Per rispondere a questo deficit metodologico, questo articolo presenta le basi di un approccio per la

  10. System administrator's manual (SAM) for the enhanced logistics intratheater support tool (ELIST) database instance segment version 8.1.0.0 for solaris 7.; TOPICAL

    International Nuclear Information System (INIS)

    Dritz, K.

    2002-01-01

    This document is the System Administrator's Manual (SAM) for the Enhanced Logistics Intratheater Support Tool (ELIST) Database Instance Segment. It covers errors that can arise during the segment's installation and deinstallation, and it outlines appropriate recovery actions. It also tells how to change the password for the SYSTEM account of the database instance after the instance is created, and it discusses the creation of a suitable database instance for ELIST by means other than the installation of the segment. The latter subject is covered in more depth than its introductory discussion in the Installation Procedures (IP) for the Enhanced Logistics Intratheater Support Tool (ELIST) Global Data Segment, Database Instance Segment, Database Fill Segment, Database Segment, Database Utility Segment, Software Segment, and Reference Data Segment (referred to in portions of this document as the ELIST IP). The information in this document is expected to be of use only rarely. Other than errors arising from the failure to follow instructions, difficulties are not expected to be encountered during the installation or deinstallation of the segment. By the same token, the need to create a database instance for ELIST by means other than the installation of the segment is expected to be the exception, rather than the rule. Most administrators will only need to be aware of the help that is provided in this document and will probably not actually need to read and make use of it

  11. Diverse Expected Gradient Active Learning for Relative Attributes.

    Science.gov (United States)

    You, Xinge; Wang, Ruxin; Tao, Dacheng

    2014-06-02

    The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

  12. Exploring counter-theoretical instances of graduate learners’ self-regulatory processes when using an online repository

    Directory of Open Access Journals (Sweden)

    Kamran Shaikh

    2012-01-01

    Full Text Available Academic self-regulation theories have proposed that learning involves a complex set of cognitive and metacognitive mechanisms that are enacted in phases. These phases include task understanding, strategy adoption, monitoring, and reflection. Whereas classical approaches to self-regulation contend that these phases work together to influence academic performance, the empirical research reported herein reveals that, for essay writing in an online learning environment, improved self-regulation is not necessarily associated with improved learning outcomes. We begin by reviewing frameworks for academic self-regulation, specifically in the context of learners’ experiences in online repositories equipped with Topic Maps (ISO 13250 indexes. We then offer explanations for counter-theoretical interactions found between task understanding (a frontline phase of self-regulation and academic performance in 38 graduate learners who used Topic Maps to tackle ill-structured essay tasks. Our investigation sheds light not only on how learners’ perceptions of feedback facilitate task understanding, but also on the complex relationship between task understanding and monitoring proficiencies.

  13. CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

    2017-07-14

    We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

  14. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Science.gov (United States)

    Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

    2017-01-01

    Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness

  15. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Directory of Open Access Journals (Sweden)

    Gerald Forkuor

    Full Text Available Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat, terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC, soil organic carbon (SOC and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR, random forest regression (RFR, support vector machine (SVM, stochastic gradient boosting (SGB-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices

  16. ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

    KAUST Repository

    Wu, Baoyuan

    2015-12-07

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.

  17. Application of Human Factors Methods to Design Healthcare Work Systems: Instance of the prevention of Adverse Drug Events.

    Science.gov (United States)

    Marcilly, Romaric; Beuscart-Zephir, Marie-Catherine

    2015-01-01

    Human Factors (HF) methods are increasingly needed to support the design of new technologies in order to avoid that introducing those technologies into healthcare work systems induces use errors with potentially catastrophic consequences for the patients. This chapter illustrates the application of HF methods in developing two health technologies aiming at securing the hospital medication management process. Lessons learned from this project highlight the importance of (i) analyzing the work system in which the technology is intended to be implemented, (ii) involving end users in the design process and (iii) the intermediation role of HF between end users and scientific/technical experts.

  18. Multiple Perspectives / Multiple Readings

    Directory of Open Access Journals (Sweden)

    Simon Biggs

    2005-01-01

    Full Text Available People experience things from their own physical point of view. What they see is usually a function of where they are and what physical attitude they adopt relative to the subject. With augmented vision (periscopes, mirrors, remote cameras, etc we are able to see things from places where we are not present. With time-shifting technologies, such as the video recorder, we can also see things from the past; a time and a place we may never have visited.In recent artistic work I have been exploring the implications of digital technology, interactivity and internet connectivity that allow people to not so much space/time-shift their visual experience of things but rather see what happens when everybody is simultaneously able to see what everybody else can see. This is extrapolated through the remote networking of sites that are actual installation spaces; where the physical movements of viewers in the space generate multiple perspectives, linked to other similar sites at remote locations or to other viewers entering the shared data-space through a web based version of the work.This text explores the processes involved in such a practice and reflects on related questions regarding the non-singularity of being and the sense of self as linked to time and place.

  19. Learning the association between a context and a target location in infancy

    NARCIS (Netherlands)

    Bertels, Julie; San Anton, Estibaliz; Gebuis, Titia; Destrebecqz, Arnaud

    2017-01-01

    Extracting the statistical regularities present in the environment is a central learning mechanism in infancy. For instance, infants are able to learn the associations between simultaneously or successively presented visual objects (Fiser & Aslin,; Kirkham, Slemmer & Johnson,). The present study

  20. The Effect of Dynamic and Interactive Mathematics Learning Environments (DIMLE), Supporting Multiple Representations, on Perceptions of Elementary Mathematics Pre-Service Teachers in Problem Solving Process

    Science.gov (United States)

    Ozdemir, S.; Reis, Z. Ayvaz

    2013-01-01

    Mathematics is an important discipline, providing crucial tools, such as problem solving, to improve our cognitive abilities. In order to solve a problem, it is better to envision and represent through multiple means. Multiple representations can help a person to redefine a problem with his/her own words in that envisioning process. Dynamic and…

  1. Effects of Goal Relations on Self-Regulated Learning in Multiple Goal Pursuits: Performance, the Self-Regulatory Process, and Task Enjoyment

    Science.gov (United States)

    Lee, Hyunjoo

    2012-01-01

    The purpose of this study was to investigate the effects of goal relations on self-regulation in the pursuit of multiple goals, focusing on self-regulated performance, the self-regulatory process, and task enjoyment. The effect of multiple goal relations on self-regulation was explored in a set of three studies. Goal relations were divided into…

  2. Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

    DEFF Research Database (Denmark)

    Friis-Nielsen, Jens; Kjartansdóttir, Kristín Rós; Mollerup, Sarah

    2016-01-01

    have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32...

  3. Generalized query-based active learning to identify differentially methylated regions in DNA.

    Science.gov (United States)

    Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

    2013-01-01

    Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

  4. Co-Labeling for Multi-View Weakly Labeled Learning.

    Science.gov (United States)

    Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

    2016-06-01

    It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi

  5. Supervised Learning for Dynamical System Learning.

    Science.gov (United States)

    Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

    2015-01-01

    Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

  6. Multiple sclerosis

    Science.gov (United States)

    ... indwelling catheter Osteoporosis or thinning of the bones Pressure sores Side effects of medicines used to treat the ... Daily bowel care program Multiple sclerosis - discharge Preventing pressure ulcers Swallowing problems Images Multiple sclerosis MRI of the ...

  7. Multi-locus phylogeny reveals instances of mitochondrial introgression and unrecognized diversity in Kenyan barbs (Cyprininae: Smiliogastrini).

    Science.gov (United States)

    Schmidt, Ray C; Bart, Henry L; Nyingi, Wanja Dorothy

    2017-06-01

    The phylogenetics and taxonomic status of small African barbs (Cyprininae: Smiliogastrini) remains unresolved despite the recent decision to elevate the genus name Enteromius for the group. The main barrier to understanding the origin of African small barbs and evolutionary relationships within the group is the poor resolution of phylogenies published to date. These phylogenies usually rely on mitochondrial markers and have limited taxon sampling. Here we investigate the phylogenetic relationships of small barbs of Kenya utilizing cytochrome b, Growth Hormone (GH) intron 2, and RAG1 markers from multiple populations of many species in the region. This multi-locus study produced well-supported phylogenies and revealed additional issues that complicate understanding the relationships among East African barbs. We observed widespread mtDNA introgression within the Kenyan barbs, highlighting the need to include nuclear markers in phylogenetic studies of the group. The GH intron 2 resolved heterospecific individuals and aided in inferring the species level phylogeny. The study reveals unrecognized diversity within the group, including within species reported to occur throughout East Africa, and it provides the groundwork for future taxonomic work in the region and across Africa. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. MULTIPLE OBJECTS

    Directory of Open Access Journals (Sweden)

    A. A. Bosov

    2015-04-01

    Full Text Available Purpose. The development of complicated techniques of production and management processes, information systems, computer science, applied objects of systems theory and others requires improvement of mathematical methods, new approaches for researches of application systems. And the variety and diversity of subject systems makes necessary the development of a model that generalizes the classical sets and their development – sets of sets. Multiple objects unlike sets are constructed by multiple structures and represented by the structure and content. The aim of the work is the analysis of multiple structures, generating multiple objects, the further development of operations on these objects in application systems. Methodology. To achieve the objectives of the researches, the structure of multiple objects represents as constructive trio, consisting of media, signatures and axiomatic. Multiple object is determined by the structure and content, as well as represented by hybrid superposition, composed of sets, multi-sets, ordered sets (lists and heterogeneous sets (sequences, corteges. Findings. In this paper we study the properties and characteristics of the components of hybrid multiple objects of complex systems, proposed assessments of their complexity, shown the rules of internal and external operations on objects of implementation. We introduce the relation of arbitrary order over multiple objects, we define the description of functions and display on objects of multiple structures. Originality.In this paper we consider the development of multiple structures, generating multiple objects.Practical value. The transition from the abstract to the subject of multiple structures requires the transformation of the system and multiple objects. Transformation involves three successive stages: specification (binding to the domain, interpretation (multiple sites and particularization (goals. The proposed describe systems approach based on hybrid sets

  9. 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

  10. Ensinando e aprendendo com portadores de Esclerose Múltipla: relato de experiência Enseñando y aprendiendo com los portatodes de Esclerósis Multiple: relato de experiencia Teaching and learning with Multiple Sclerosis patients: experience report

    Directory of Open Access Journals (Sweden)

    Lúcia Helena Rios Barbosa de Almeida

    2007-08-01

    Full Text Available Relatode experiência realizado para descrever a vivência de um grupo de estudantes de Enfermagem da Universidade Federal do Piauí, diante da realidade do ser portador de Esclerose Múltipla. A experiência evidencia a necessidade de uma equipe integrada para obter conhecimentos fundamentais e oferecer uma assistência holística ao portador. Enfatiza-se o impacto das ações desenvolvidas pelos estudantes, na Associação Piauiense de Esclerose Múltipla, possibilitando-os a conviver melhor com as dificuldades advindas com a doença. Além disso, a comunidade em geral é favorecida através dos conhecimentos repassados mediante atividades desenvolvidas pelo grupo. Concluímos que um conhecimento mais amplo acerca de uma patologia ainda tão cheia de mistérios só é possível quando se alia teoria à realidade do paciente.Este es un relato de una experiência de trabajo realizado por un grupo de estudiantes de Enfermería de la Universidad Federal de Piauí, sobre la siutación de un portador de Esclerosis Multiple. La experiencia demuestra la necesidad de un equipo integrado para obtener conocimientos fundamentales y ofrecer una asistencia integral al portador. Aquí se enfatiza las acciones desarrolladas por los estudiantes, dentro de la APPEM (Asociación Piauiense de Esclerósis Multiple, posible a coexistir mejor él las dificultades que se sucedierón con la enfermedad. Favorecen la comunidad en general con el conocimiento repasado por medio de las actividades desarrolladas para el grupo. Un conocimiento más amplio acerca de una patología llena de misterios, solo es posible, cuando se junta la teoria a la realidad del paciente.This report describes the experience of a group of nursing students from Universidad Federal do Piauí, in face of the reality of multiple sclerosis patient. The experience highlight the necessity of an integrated team to achieve fundamental knowledge and to offer a holistic assistance to the patient. The

  11. 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

  12. Learning by observing: the effect of multiple sessions of action-observation training on the spontaneous movement tempo and motor resonance.

    Science.gov (United States)

    Lagravinese, Giovanna; Bisio, Ambra; Ruggeri, Piero; Bove, Marco; Avanzino, Laura

    2017-02-01

    The present study was designed to explore the changes in motor performance and motor resonance after multiple sessions of action observation (AO) training. Subjects were exposed to the observation of a video showing finger tapping movements executed at 3Hz, a frequency higher than the spontaneous one (2Hz) for four consecutive days. Motor performance and motor resonance were tested before the AO training on the first day, and on the last day. Results showed that multiple sessions of AO training induced a shift of the speed of execution of finger tapping movements toward the observed one and a change in motor resonance. Before the 3Hz-AO training cortical excitability was highest during the observation of the 2Hz video. This motor resonance effect was lost after one single session of 3Hz-AO training whereas after multiple sessions of 3Hz-AO training cortical excitability was highest during the observation of the 3Hz video. Our study shows for the first time that multiple sessions of AO training are able not only to induce performance gains but also to change the way by which the observer's motor system recognizes a certain movement as belonging to the individual motor repertoire. These results may encourage the development of novel rehabilitative protocols based on multiple sessions of action observation aimed to regain a correct movement when its spontaneous speed is modified by pathologies or to modify the innate temporal properties of certain movements. Copyright © 2017. Published by Elsevier Ltd.

  13. Teacher behaviour in learning contexts for gifted and talented students : A multiple case study focusing on basic needs satisfaction and motivational differentiation

    NARCIS (Netherlands)

    de Boer, Greet C.; Opdenakker, Marie-Christine J.L.; Minnaert, Alexander E.M.G.

    2015-01-01

    In the international comparative study on reading, mathematics, and science of 2009 (OECD, 2010) especially Dutch gifted and talented (G&T) students underachieved compared to 17 countries. These students require a differentiated, adaptive curriculum embedded within an optimal learning environment

  14. The Importance of Multiple Representations of Mathematical Problems: Evidence from Chinese Preservice Elementary Teachers' Analysis of a Learning Goal

    Science.gov (United States)

    Kang, Rui; Liu, Di

    2018-01-01

    This article describes a study of how Chinese preservice teachers unpacked a learning goal pertaining to adding fractions and understanding the concepts underlying the operation. Based on work in the USA by Morris, Hiebert, and Spizter ("Journal for Research in Mathematics Education," 40(5), 491-529, 2009), 50 Chinese preservice teachers…

  15. A Parent's Guide to Playground Safety, [and] The Multiage Classroom: A Guide for Parents, [and] Multiple Intelligences: Different Ways of Learning. ACEI Speaks Series.

    Science.gov (United States)

    Frost, Joe L.; And Others

    Three brochures for parents are presented. The first lists potential playground hazards and suggestions for improving playgrounds. The second describes benefits of the multiage classroom, comparing such a classroom with a traditional, single-grade class. The third brochure describes verbal, logical, visual, musical, and physical learning styles…

  16. Effects of phentermine and pentobarbital on choice processes during multiple probability learning (MPL) and decision processes manipulated by pay-off conditions

    NARCIS (Netherlands)

    Volkerts, ER; VanLaar, MW; Verbaten, MN; Mulder, G; Maes, RAA

    1997-01-01

    The primary research question in this investigation concerned whether arousal manipulation by a stimulant (phentermine 20 mg) and a depressant (pentobarbital 100 mg) will oppositely affect choice behaviour in a probability learning task and decision processes manipulated by pay-off. A 3-source

  17. Making Connections among Multiple Visual Representations: How Do Sense-Making Skills and Perceptual Fluency Relate to Learning of Chemistry Knowledge?

    Science.gov (United States)

    Rau, Martina A.

    2018-01-01

    To learn content knowledge in science, technology, engineering, and math domains, students need to make connections among visual representations. This article considers two kinds of connection-making skills: (1) "sense-making skills" that allow students to verbally explain mappings among representations and (2) "perceptual…

  18. Multiple sexual partners and condom use among 10 - 19 year-olds in four districts in Tanzania: What do we learn?

    Directory of Open Access Journals (Sweden)

    Kweka Khadija

    2011-06-01

    Full Text Available Abstract Background Although some studies in Tanzania have addressed the question of sexuality and STIs among adolescents, mostly those aged 15 - 19 years, evidence on how multiple sexual partners influence condom use among 10 - 19 year-olds is limited. This study attempts to bridge this gap by testing a hypothesis that sexual relationships with multiple partners in the age group 10 - 19 years spurs condom use during sex in four districts in Tanzania. Methods Secondary analysis was performed using data from the Adolescents Module of the cross-sectional household survey on Maternal, Newborn and Child Health (MNCH that was done in Kigoma, Kilombero, Rufiji and Ulanga districts, Tanzania in 2008. A total of 612 adolescents resulting from a random sample of 1200 households participated in this study. Pearson Chi-Square was used as a test of association between multiple sexual partners and condom use. Multivariate logistic regression model was fitted to the data to assess the effect of multiple sexual partners on condom use, having adjusted for potential confounding variables. STATA (10 statistical software was used to carry out this process at 5% two-sided significance level. Results Of the 612 adolescents interviewed, 23.4% reported being sexually active and 42.0% of these reported having had multiple (> 1 sexual partners in the last 12 months. The overall prevalence of condom use among them was 39.2%. The proportion using a condom at the last sexual intercourse was higher among those who knew that they can get a condom if they want than those who did not. No evidence of association was found between multiple sexual partners and condom use (OR = 0.77, 95% CI = 0.35 - 1.67, P = 0.504. With younger adolescents (10 - 14 years being a reference, condom use was associated with age group (15 - 19: OR = 3.69, 95% CI = 1.21 - 11.25, P = 0.022 and district of residence (Kigoma: OR = 7.45, 95% CI = 1.79 - 31.06, P = 0.006; Kilombero: OR = 8.89, 95% CI = 2

  19. Perceptual learning and human expertise.

    Science.gov (United States)

    Kellman, Philip J; Garrigan, Patrick

    2009-06-01

    We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual

  20. Perceptual learning and human expertise

    Science.gov (United States)

    Kellman, Philip J.; Garrigan, Patrick

    2009-06-01

    We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual