Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

ERIC Educational Resources Information Center

Zhang, Zhidong

2016-01-01

This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

Family Learning Research in Museums: An Emerging Disciplinary Matrix?

ERIC Educational Resources Information Center

Ellenbogen, Kirsten M.; Luke, Jessica J.; Dierking, Lynn D.

2004-01-01

Thomas Kuhn's notion of a disciplinary matrix provides a useful framework for investigating the growth of research on family learning in and from museums over the last decade. To track the emergence of this disciplinary matrix we consider three issues. First are shifting theoretical perspectives that result in new shared language, beliefs, values,…

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

2017-01-01

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Improving patient-level costing in the English and the German 'DRG' system.

PubMed

Vogl, Matthias

2013-03-01

The purpose of this paper is to develop ways to improve patient-level cost apportioning (PLCA) in the English and German inpatient 'DRG' cost accounting systems, to support regulators in improving costing schemes, and to give clinicians and hospital management sophisticated tools to measure and link their management. The paper analyzes and evaluates the PLCA step in the cost accounting schemes of both countries according to the impact on the key aspects of DRG introduction: transparency and efficiency. The goal is to generate a best available PLCA standard with enhanced accuracy and managerial relevance, the main requirements of cost accounting. A best available PLCA standard in 'DRG' cost accounting uses: (1) the cost-matrix from the German system; (2) a third axis in this matrix, representing service-lines or clinical pathways; (3) a scoring system for key cost drivers with the long-term objective of time-driven activity-based costing and (4) a point of delivery separation. Both systems have elements that the other system can learn from. By combining their strengths, regulators are supported in enhancing PLCA systems, improving the accuracy of national reimbursement and the managerial relevance of inpatient cost accounting systems, in order to reduce costs in health care. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

PubMed

Xiao, Shijie; Xu, Dong; Wu, Jianxin

2015-10-01

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.

Extracellular matrix stiffness causes systematic variations in proliferation and chemosensitivity in myeloid leukemias.

PubMed

Shin, Jae-Won; Mooney, David J

2016-10-25

Extracellular matrix stiffness influences biological functions of some tumors. However, it remains unclear how cancer subtypes with different oncogenic mutations respond to matrix stiffness. In addition, the relevance of matrix stiffness to in vivo tumor growth kinetics and drug efficacy remains elusive. Here, we designed 3D hydrogels with physical parameters relevant to hematopoietic tissues and adapted them to a quantitative high-throughput screening format to facilitate mechanistic investigations into the role of matrix stiffness on myeloid leukemias. Matrix stiffness regulates proliferation of some acute myeloid leukemia types, including MLL-AF9 + MOLM-14 cells, in a biphasic manner by autocrine regulation, whereas it decreases that of chronic myeloid leukemia BCR-ABL + K-562 cells. Although Arg-Gly-Asp (RGD) integrin ligand and matrix softening confer resistance to a number of drugs, cells become sensitive to drugs against protein kinase B (PKB or AKT) and rapidly accelerated fibrosarcoma (RAF) proteins regardless of matrix stiffness when MLL-AF9 and BCR-ABL are overexpressed in K-562 and MOLM-14 cells, respectively. By adapting the same hydrogels to a xenograft model of extramedullary leukemias, we confirm the pathological relevance of matrix stiffness in growth kinetics and drug sensitivity against standard chemotherapy in vivo. The results thus demonstrate the importance of incorporating 3D mechanical cues into screening for anticancer drugs.

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

A NEW RELEVANCE ESTIMATOR FOR THE COMPILATION AND VISUALIZATION OF DISEASE PATTERNS AND POTENTIAL DRUG TARGETS.

PubMed

VON Korff, Modest; Fink, Tobias; Sander, Thomas

2017-01-01

A new computational method is presented to extract disease patterns from heterogeneous and text-based data. For this study, 22 million PubMed records were mined for co-occurrences of gene name synonyms and disease MeSH terms. The resulting publication counts were transferred into a matrix Mdata. In this matrix, a disease was represented by a row and a gene by a column. Each field in the matrix represented the publication count for a co-occurring disease-gene pair. A second matrix with identical dimensions Mrelevance was derived from Mdata. To create Mrelevance the values from Mdata were normalized. The normalized values were multiplied by the column-wise calculated Gini coefficient. This multiplication resulted in a relevance estimator for every gene in relation to a disease. From Mrelevance the similarities between all row vectors were calculated. The resulting similarity matrix Srelevance related 5,000 diseases by the relevance estimators calculated for 15,000 genes. Three diseases were analyzed in detail for the validation of the disease patterns and the relevant genes. Cytoscape was used to visualize and to analyze Mrelevance and Srelevance together with the genes and diseases. Summarizing the results, it can be stated that the relevance estimator introduced here was able to detect valid disease patterns and to identify genes that encoded key proteins and potential targets for drug discovery projects.

Systematic Correlation Matrix Evaluation (SCoMaE) - a bottom-up, science-led approach to identifying indicators

NASA Astrophysics Data System (ADS)

Mengis, Nadine; Keller, David P.; Oschlies, Andreas

2018-01-01

This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom-up approach which combines expert judgment and statistical information to systematically select transparent, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (4) testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, (5) enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate-high, as well as a business-as-usual, climate change scenario simulation. This necessity arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Constrained low-rank matrix estimation: phase transitions, approximate message passing and applications

NASA Astrophysics Data System (ADS)

Lesieur, Thibault; Krzakala, Florent; Zdeborová, Lenka

2017-07-01

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on Information Theory Proc. pp 1635-9 and 2015 53rd Annual Allerton Conf. on Communication, Control and Computing (IEEE) pp 680-7) on low-rank matrix estimation in the presence of constraints on the factors into which the matrix is factorized. Low-rank matrix factorization is one of the basic methods used in data analysis for unsupervised learning of relevant features and other types of dimensionality reduction. We present a framework to study the constrained low-rank matrix estimation for a general prior on the factors, and a general output channel through which the matrix is observed. We draw a parallel with the study of vector-spin glass models—presenting a unifying way to study a number of problems considered previously in separate statistical physics works. We present a number of applications for the problem in data analysis. We derive in detail a general form of the low-rank approximate message passing (Low-RAMP) algorithm, that is known in statistical physics as the TAP equations. We thus unify the derivation of the TAP equations for models as different as the Sherrington-Kirkpatrick model, the restricted Boltzmann machine, the Hopfield model or vector (xy, Heisenberg and other) spin glasses. The state evolution of the Low-RAMP algorithm is also derived, and is equivalent to the replica symmetric solution for the large class of vector-spin glass models. In the section devoted to result we study in detail phase diagrams and phase transitions for the Bayes-optimal inference in low-rank matrix estimation. We present a typology of phase transitions and their relation to performance of algorithms such as the Low-RAMP or commonly used spectral methods.

Family learning research in museums: An emerging disciplinary matrix?

NASA Astrophysics Data System (ADS)

Ellenbogen, Kirsten M.; Luke, Jessica J.; Dierking, Lynn D.

2004-07-01

Thomas Kuhn's notion of a disciplinary matrix provides a useful framework for investigating the growth of research on family learning in and from museums over the last decade. To track the emergence of this disciplinary matrix we consider three issues. First are shifting theoretical perspectives that result in new shared language, beliefs, values, understandings, and assumptions about what counts as family learning. Second are realigning methodologies, driven by underlying disciplinary assumptions about how research in this arena is best conducted, what questions should be addressed, and criteria for valid and reliable evidence. Third is resituating the focus of our research to make the family central to what we study, reflecting a more holistic understanding of the family as an educational institution within larger learning infrastructure. We discuss research that exemplifies these three issues and demonstrates the ways in which shifting theoretical perspectives, realigning methodologies, and resituating research foci signal the existence of a nascent disciplinary matrix.

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

ERIC Educational Resources Information Center

Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

2015-01-01

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

Faculty Teaching Development: Using the Multidimensional Matrix of Teaching Development to Guide Teaching Improvement Activities

ERIC Educational Resources Information Center

Murphy, Cheryl A.; Jensen, Thomas D.

2016-01-01

Higher education faculty learn how to teach through courses, seminars, or workshops during and after their doctoral program. Perhaps the more prevalent way faculty learn to teach is through observational and self-directed learning. In order to assist with self-directed teaching improvements we developed the Multidimensional Matrix of Teaching…

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

PubMed Central

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods. PMID:26880879

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

PubMed

Wang, Bingkun; Huang, Yongfeng; Li, Xing

2016-01-01

E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.

PubMed

Lam, Van K; Nguyen, Thanh C; Chung, Byung M; Nehmetallah, George; Raub, Christopher B

2018-03-01

The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two-dimensional surfaces in vitro, MDA-MB-231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA-MB-231 cell line were cultured on glass, tissue-culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F-actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F-actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA-MB-231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

PubMed

Bueno, C; Sousa, C O M; Freitas, S R

2015-11-01

We believe that in tropics we need a community approach to evaluate road impacts on wildlife, and thus, suggest mitigation measures for groups of species instead a focal-species approach. Understanding which landscape characteristics indicate road-kill events may also provide models that can be applied in other regions. We intend to evaluate if habitat or matrix is more relevant to predict road-kill events for a group of species. Our hypothesis is: more permeable matrix is the most relevant factor to explain road-kill events. To test this hypothesis, we chose vertebrates as the studied assemblage and a highway crossing in an Atlantic Forest region in southeastern Brazil as the study site. Logistic regression models were designed using presence/absence of road-kill events as dependent variables and landscape characteristics as independent variables, which were selected by Akaike's Information Criterion. We considered a set of candidate models containing four types of simple regression models: Habitat effect model; Matrix types effect models; Highway effect model; and, Reference models (intercept and buffer distance). Almost three hundred road-kills and 70 species were recorded. River proximity and herbaceous vegetation cover, both matrix effect models, were associated to most road-killed vertebrate groups. Matrix was more relevant than habitat to predict road-kill of vertebrates. The association between river proximity and road-kill indicates that rivers may be a preferential route for most species. We discuss multi-species mitigation measures and implications to movement ecology and conservation strategies.

Matrix Treatment of Ray Optics.

ERIC Educational Resources Information Center

Quon, W. Steve

1996-01-01

Describes a method to combine two learning experiences--optical physics and matrix mathematics--in a straightforward laboratory experiment that allows engineering/physics students to integrate a variety of learning insights and technical skills, including using lasers, studying refraction through thin lenses, applying concepts of matrix…

Learning Circulant Sensing Kernels

DTIC Science & Technology

2014-03-01

Furthermore, we test learning the circulant sensing matrix/operator and the nonparametric dictionary altogether and obtain even better performance. We...scale. Furthermore, we test learning the circulant sensing matrix/operator and the nonparametric dictionary altogether and obtain even better performance...matrices, Tropp et al.[28] de - scribes a random filter for acquiring a signal x̄; Haupt et al.[12] describes a channel estimation problem to identify a

Enhanced Resource Descriptions Help Learning Matrix Users.

ERIC Educational Resources Information Center

Roempler, Kimberly S.

2003-01-01

Describes the Learning Matrix digital library which focuses on improving the preparation of math and science teachers by supporting faculty who teach introductory math and science courses in two- and four-year colleges. Suggests it is a valuable resource for school library media specialists to support new science and math teachers. (LRW)

Accurate interatomic force fields via machine learning with covariant kernels

NASA Astrophysics Data System (ADS)

Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

2017-06-01

We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

Nuclear physics from Lattice QCD

NASA Astrophysics Data System (ADS)

Shanahan, Phiala

2017-09-01

I will discuss the current state and future scope of numerical Lattice Quantum Chromodynamics (LQCD) calculations of nuclear matrix elements. The goal of the program is to provide direct QCD calculations of nuclear observables relevant to experimental programs, including double-beta decay matrix elements, nuclear corrections to axial matrix elements relevant to long-baseline neutrino experiments and nuclear sigma terms needed for theory predictions of dark matter cross-sections at underground detectors. I will discuss the progress and challenges on these fronts, and also address recent work constraining a gluonic analogue of the EMC effect, which will be measurable at a future electron-ion collider.

Stellarator Saddle Coils

NASA Astrophysics Data System (ADS)

Boozer, Allen H.

1999-11-01

Modern stellarators are designed using J. Nuehrenberg’s method of varying Fourier coefficients in the shape of the plasma boundary to maximize a target function. The matrix of second derivatives of the target function at the optimum determines a quality matrix. This matrix gives the degradation in the quality of the configuration as the normal magnetic field is varied on a control surface, which lies on or outside the plasma surface. The task is finding saddle coils that produce the desired configuration in the presence of a given toroidal field. An eigenvector of the quality matrix can be important for two reasons: (1) the normal field that must be produced by the saddles is large or (2) the eigenvalue is large (an island-causing resonant perturbation). The rank of the important part of the quality matrix is the number of important eigenvectors. The current in each saddle coil produces a normal field on the control surface, which can be described by an inductance matrix. The relevant part of the inductance matrix has large eigenvalues. The coils can produce the configuration if the rank of the important part of the quality matrix and its product with the relevant part of the inductance matrix are the same. Existing coil design codes, pioneered by P. Merkel, approximate the quality matrix by the unit matrix. Stellarator flexibility could be enhanced by using a more realistic quality matrix and by using trim coils to balance large eigenvalues.

The Strategy Selection Matrix--A Guide for Individualizing Instruction.

ERIC Educational Resources Information Center

Bell, Steven

The Strategy Selection Matrix (SSM) is offered as a means for matching teaching technique to the individual special needs student. Three steps in the SSM are described: development of an intra-individual learning style profile based on 14 learning components; review of the individualizing teaching strategies (such as tutoring, continuous progress…

Development of a Problem-Based Learning Matrix for Data Collection

ERIC Educational Resources Information Center

Sipes, Shannon M.

2017-01-01

Few of the papers published in journals and conference proceedings on problem-based learning (PBL) are empirical studies, and most of these use self-report as the measure of PBL (Beddoes, Jesiek, & Borrego, 2010). The current study provides a theoretically derived matrix for coding and classifying PBL that was objectively applied to official…

Modeling the Dynamics of Gel Electrophorresis in the High School Classroom

NASA Astrophysics Data System (ADS)

Saucedo, Skyler R.

2013-01-01

Gel electrophoresis, used by geneticists and forensic experts alike, is an immensely popular technique that utilizes an electric field to separate molecules and proteins by size and charge. At the microscopic level, a dye or complex protein like DNA is passed through agarose, a gelatinous three-dimensional matrix of pores and nano-sized tunnels. When forced through a maze of holes, the molecule unravels, forming a long chain, slithering through the field of pores in a process colloquially coined "reputation." As a result, the smaller molecules travel farther through the gel when compared to molecules of larger molecular weight. This highly effective "molecular sieve" provides consistent data and allows scientists to compare similar sequences of DNA base pairs in a routine fashion.2 When performed at the high school level, gel electrophoresis provides students the opportunity to learn about a contemporary lab technique of great scientific relevance. Doing real science certainly excites students and motivates them to learn more.

Group sparse multiview patch alignment framework with view consistency for image classification.

PubMed

Gui, Jie; Tao, Dacheng; Sun, Zhenan; Luo, Yong; You, Xinge; Tang, Yuan Yan

2014-07-01

No single feature can satisfactorily characterize the semantic concepts of an image. Multiview learning aims to unify different kinds of features to produce a consensual and efficient representation. This paper redefines part optimization in the patch alignment framework (PAF) and develops a group sparse multiview patch alignment framework (GSM-PAF). The new part optimization considers not only the complementary properties of different views, but also view consistency. In particular, view consistency models the correlations between all possible combinations of any two kinds of view. In contrast to conventional dimensionality reduction algorithms that perform feature extraction and feature selection independently, GSM-PAF enjoys joint feature extraction and feature selection by exploiting l(2,1)-norm on the projection matrix to achieve row sparsity, which leads to the simultaneous selection of relevant features and learning transformation, and thus makes the algorithm more discriminative. Experiments on two real-world image data sets demonstrate the effectiveness of GSM-PAF for image classification.

Features: Real-Time Adaptive Feature and Document Learning for Web Search.

ERIC Educational Resources Information Center

Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

2001-01-01

Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion.

PubMed

Fierimonte, Roberto; Scardapane, Simone; Uncini, Aurelio; Panella, Massimo

2016-08-26

Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.

PubMed

Yu, Hwanjo; Kim, Taehoon; Oh, Jinoh; Ko, Ilhwan; Kim, Sungchul; Han, Wook-Shin

2010-04-16

Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

PubMed Central

2010-01-01

Background Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user’s feedback and efficiently processes the function to return relevant articles in real time. PMID:20406504

Land Operations in the Year 2020 (LO2020) (Operations terrestres a l’horizon 2020 (LO2020)).

DTIC Science & Technology

1999-03-01

CAPABILITIES Technologies [  ] □ [500-700] n[>70°] 186 APPENDIX 4 to ANNEX V SHORT LISTED TECHNOLOGIES CARACTERISED REGARDING CC 1. top... CARACTERISATION MATRIX techno Legend: no relevance weak relevance good relevance strong relevance 189 KEY TECHNOLOGIES CARACTERISED REGARDING COST (34

Creative Learning Identities

ERIC Educational Resources Information Center

Jeffrey, Bob

2008-01-01

Making learning relevant involves many aspects of teaching such as attention to levels of maturity, individual inclinations, emotional, physical, aesthetic and cognitive activity and group dynamics. However, making learning relevant is not only a teacher led activity, for learners make activities relevant by the identification of connections with…

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

NASA Astrophysics Data System (ADS)

Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

2014-06-01

Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

High Performance Composites. "Designed" Materials for the New Millennium. 2nd Module in a Series on Advanced Materials. Resources in Technology.

ERIC Educational Resources Information Center

Jacobs, James A.

1994-01-01

This learning module on composites such as polymer matrix, metal matrix, ceramic matrix, particulate, and laminar includes a design brief giving context, objectives, evaluation, student outcomes, and quiz. (SK)

Neuroanatomy-based matrix-guided trimming protocol for the rat brain.

PubMed

Defazio, Rossella; Criado, Ana; Zantedeschi, Valentina; Scanziani, Eugenio

2015-02-01

Brain trimming through defined neuroanatomical landmarks is recommended to obtain consistent sections in rat toxicity studies. In this article, we describe a matrix-guided trimming protocol that uses channels to reproduce coronal levels of anatomical landmarks. Both setup phase and validation study were performed on Han Wistar male rats (Crl:WI(Han)), 10-week-old, with bodyweight of 298 ± 29 (SD) g, using a matrix (ASI-Instruments(®), Houston, TX) fitted for brains of rats with 200 to 400 g bodyweight. In the setup phase, we identified eight channels, that is, 6, 8, 10, 12, 14, 16, 19, and 21, matching the recommended landmarks midway to the optic chiasm, frontal pole, optic chiasm, infundibulum, mamillary bodies, midbrain, middle cerebellum, and posterior cerebellum, respectively. In the validation study, we trimmed the immersion-fixed brains of 60 rats using the selected channels to determine how consistently the channels reproduced anatomical landmarks. Percentage of success (i.e., presence of expected targets for each level) ranged from 89 to 100%. Where 100% success was not achieved, it was noted that the shift in brain trimming was toward the caudal pole. In conclusion, we developed and validated a trimming protocol for the rat brain that allow comparable extensiveness, homology, and relevance of coronal sections as the landmark-guided trimming with the advantage of being quickly learned by technicians. © 2014 by The Author(s).

Unpacking and Communicating the Multidimensional Mission of Educational Development: A Mission Matrix Tool for Centers of Teaching and Learning

ERIC Educational Resources Information Center

Schroeder, Connie

2015-01-01

In recent decades, the work of educational developers in Centers of Teaching and Learning (CTLs) is complex and diverse. The wide range of services and programs makes it difficult understand the mission and purpose of CTLs and communicate this effectively. The Center Mission Matrix Tool enables analysis and articulation of all facets of the…

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

Adaptive Inverse Control for Rotorcraft Vibration Reduction

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.

1985-01-01

This thesis extends the Least Mean Square (LMS) algorithm to solve the mult!ple-input, multiple-output problem of alleviating N/Rev (revolutions per minute by number of blades) helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the higher harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. For higher order transfer matrices, a rough estimate of the inverse is needed to start the algorithm efficiently. The simulation results indicate that the factor which most influences LMS adaptive inverse control is the product of the control relaxation and the the stability gain matrix. A small stability gain matrix makes the controller less sensitive to relaxation selection, and permits faster and more stable vibration reduction, than by choosing the stability gain matrix large and the control relaxation term small. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification. In the simulation studies, the LMS adaptive inverse control algorithm is shown to be capable of adapting the inverse (controller) matrix to track changes in the flight conditions. The algorithm converges quickly for moderate disturbances, while taking longer for larger disturbances. Perfect knowledge of the inverse matrix is not required for good control of the N/Rev vibration. However it is shown that measurement noise will prevent the LMS adaptive inverse control technique from controlling the vibration, unless the signal averaging method presented is incorporated into the algorithm.

Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation.

PubMed

Xu, Yong; Fang, Xiaozhao; Wu, Jian; Li, Xuelong; Zhang, David

2016-02-01

In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.

Novel entries in a fungal biofilm matrix encyclopedia.

PubMed

Zarnowski, Robert; Westler, William M; Lacmbouh, Ghislain Ade; Marita, Jane M; Bothe, Jameson R; Bernhardt, Jörg; Lounes-Hadj Sahraoui, Anissa; Fontaine, Joël; Sanchez, Hiram; Hatfield, Ronald D; Ntambi, James M; Nett, Jeniel E; Mitchell, Aaron P; Andes, David R

2014-08-05

Virulence of Candida is linked with its ability to form biofilms. Once established, biofilm infections are nearly impossible to eradicate. Biofilm cells live immersed in a self-produced matrix, a blend of extracellular biopolymers, many of which are uncharacterized. In this study, we provide a comprehensive analysis of the matrix manufactured by Candida albicans both in vitro and in a clinical niche animal model. We further explore the function of matrix components, including the impact on drug resistance. We uncovered components from each of the macromolecular classes (55% protein, 25% carbohydrate, 15% lipid, and 5% nucleic acid) in the C. albicans biofilm matrix. Three individual polysaccharides were identified and were suggested to interact physically. Surprisingly, a previously identified polysaccharide of functional importance, β-1,3-glucan, comprised only a small portion of the total matrix carbohydrate. Newly described, more abundant polysaccharides included α-1,2 branched α-1,6-mannans (87%) associated with unbranched β-1,6-glucans (13%) in an apparent mannan-glucan complex (MGCx). Functional matrix proteomic analysis revealed 458 distinct activities. The matrix lipids consisted of neutral glycerolipids (89.1%), polar glycerolipids (10.4%), and sphingolipids (0.5%). Examination of matrix nucleic acid identified DNA, primarily noncoding sequences. Several of the in vitro matrix components, including proteins and each of the polysaccharides, were also present in the matrix of a clinically relevant in vivo biofilm. Nuclear magnetic resonance (NMR) analysis demonstrated interaction of aggregate matrix with the antifungal fluconazole, consistent with a role in drug impedance and contribution of multiple matrix components. Importance: This report is the first to decipher the complex and unique macromolecular composition of the Candida biofilm matrix, demonstrate the clinical relevance of matrix components, and show that multiple matrix components are needed for protection from antifungal drugs. The availability of these biochemical analyses provides a unique resource for further functional investigation of the biofilm matrix, a defining trait of this lifestyle. Copyright © 2014 Zarnowski et al.

Re-Interpreting Relevant Learning: An Evaluative Framework for Secondary Education in a Global Language

ERIC Educational Resources Information Center

Barrett, Angeline M.; Bainton, David

2016-01-01

The 2030 education goal privileges "relevant learning outcomes" as the evaluative space for quality improvement. Whilst the goal was designed for global level monitoring, its influence cuts across different scales. Implementation of the goal involves reinterpreting "relevant learning" at the local level. One way that small…

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects.

PubMed

Lötsch, Jörn; Thrun, Michael; Lerch, Florian; Brunkhorst, Robert; Schiffmann, Susanne; Thomas, Dominique; Tegder, Irmgard; Geisslinger, Gerd; Ultsch, Alfred

2017-06-07

Lipid metabolism has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid research. We used unsupervised machine-learning to analyze lipid marker serum concentrations, pursuing the hypothesis that for the most relevant markers the emerging data structures will coincide with the diagnosis of MS. Machine learning was implemented as emergent self-organizing feature maps (ESOM) combined with the U*-matrix visualization technique. The data space consisted of serum concentrations of three main classes of lipid markers comprising eicosanoids ( d = 11 markers), ceramides ( d = 10), and lyosophosphatidic acids ( d = 6). They were analyzed in cohorts of MS patients ( n = 102) and healthy subjects ( n = 301). Clear data structures in the high-dimensional data space were observed in eicosanoid and ceramides serum concentrations whereas no clear structure could be found in lysophosphatidic acid concentrations. With ceramide concentrations, the structures that had emerged from unsupervised machine-learning almost completely overlapped with the known grouping of MS patients versus healthy subjects. This was only partly provided by eicosanoid serum concentrations. Thus, unsupervised machine-learning identified distinct data structures of bioactive lipid serum concentrations. These structures could be superimposed with the known grouping of MS patients versus healthy subjects, which was almost completely possible with ceramides. Therefore, based on the present analysis, ceramides are first-line candidates for further exploration as drug-gable targets or biomarkers in MS.

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent.

PubMed

Hu, En-Liang; Kwok, James T

2015-09-01

Nonparametric kernel learning (NPKL) is a flexible approach to learn the kernel matrix directly without assuming any parametric form. It can be naturally formulated as a semidefinite program (SDP), which, however, is not very scalable. To address this problem, we propose the combined use of low-rank approximation and block coordinate descent (BCD). Low-rank approximation avoids the expensive positive semidefinite constraint in the SDP by replacing the kernel matrix variable with V(T)V, where V is a low-rank matrix. The resultant nonlinear optimization problem is then solved by BCD, which optimizes each column of V sequentially. It can be shown that the proposed algorithm has nice convergence properties and low computational complexities. Experiments on a number of real-world data sets show that the proposed algorithm outperforms state-of-the-art NPKL solvers.

Data-Driven Learning of Q-Matrix

ERIC Educational Resources Information Center

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

2006-01-01

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

A general framework to learn surrogate relevance criterion for atlas based image segmentation

NASA Astrophysics Data System (ADS)

Zhao, Tingting; Ruan, Dan

2016-09-01

Multi-atlas based image segmentation sees great opportunities in the big data era but also faces unprecedented challenges in identifying positive contributors from extensive heterogeneous data. To assess data relevance, image similarity criteria based on various image features widely serve as surrogates for the inaccessible geometric agreement criteria. This paper proposes a general framework to learn image based surrogate relevance criteria to better mimic the behaviors of segmentation based oracle geometric relevance. The validity of its general rationale is verified in the specific context of fusion set selection for image segmentation. More specifically, we first present a unified formulation for surrogate relevance criteria and model the neighborhood relationship among atlases based on the oracle relevance knowledge. Surrogates are then trained to be small for geometrically relevant neighbors and large for irrelevant remotes to the given targets. The proposed surrogate learning framework is verified in corpus callosum segmentation. The learned surrogates demonstrate superiority in inferring the underlying oracle value and selecting relevant fusion set, compared to benchmark surrogates.

The Bioactivity of Cartilage Extracellular Matrix in Articular Cartilage Regeneration

PubMed Central

Sutherland, Amanda J.; Converse, Gabriel L.; Hopkins, Richard A.; Detamore, Michael S.

2014-01-01

Cartilage matrix is a particularly promising acellular material for cartilage regeneration given the evidence supporting its chondroinductive character. The ‘raw materials’ of cartilage matrix can serve as building blocks and signals for enhanced tissue regeneration. These matrices can be created by chemical or physical methods: physical methods disrupt cellular membranes and nuclei but may not fully remove all cell components and DNA, whereas chemical methods when combined with physical methods are particularly effective in fully decellularizing such materials. Critical endpoints include no detectable residual DNA or immunogenic antigens. It is important to first delineate between the sources of the cartilage matrix, i.e., derived from matrix produced by cells in vitro or from native tissue, and then to further characterize the cartilage matrix based on the processing method, i.e., decellularization or devitalization. With these distinctions, four types of cartilage matrices exist: decellularized native cartilage (DCC), devitalized native cartilage (DVC), decellularized cell derived matrix (DCCM), and devitalized cell derived matrix (DVCM). Delivery of cartilage matrix may be a straightforward approach without the need for additional cells or growth factors. Without additional biological additives, cartilage matrix may be attractive from a regulatory and commercialization standpoint. Source and delivery method are important considerations for clinical translation. Only one currently marketed cartilage matrix medical device is decellularized, although trends in filed patents suggest additional decellularized products may be available in the future. To choose the most relevant source and processing for cartilage matrix, qualifying testing needs to include targeting the desired application, optimizing delivery of the material, identify relevant FDA regulations, assess availability of raw materials, and immunogenic properties of the product. PMID:25044502

Learning More from Educational Intervention Studies: Estimating Complier Average Causal Effects in a Relevance Intervention

ERIC Educational Resources Information Center

Nagengast, Benjamin; Brisson, Brigitte M.; Hulleman, Chris S.; Gaspard, Hanna; Häfner, Isabelle; Trautwein, Ulrich

2018-01-01

An emerging literature demonstrates that relevance interventions, which ask students to produce written reflections on how what they are learning relates to their lives, improve student learning outcomes. As part of a randomized evaluation of a relevance intervention (N = 1,978 students from 82 ninth-grade classes), we used Complier Average Causal…

Systematic review on the use of matrix-bound sealants in pancreatic resection.

PubMed

Smits, F Jasmijn; van Santvoort, Hjalmar C; Besselink, Marc G H; Borel Rinkes, Inne H M; Molenaar, I Quintus

2015-11-01

Pancreatic fistula is a potentially life-threatening complication after a pancreatic resection. The aim of this systematic review was to evaluate the role of matrix-bound sealants after a pancreatic resection in terms of preventing or ameliorating the course of a post-operative pancreatic fistula. A systematic search was performed in the literature from May 2005 to April 2015. Included were clinical studies using matrix-bound sealants after a pancreatic resection, reporting a post-operative pancreatic fistula (POPF) according to the International Study Group on Pancreatic Fistula classification, in which grade B and C fistulae were considered clinically relevant. Two were studies on patients undergoing pancreatoduodenectomy (sealants n = 67, controls n = 27) and four studies on a distal pancreatectomy (sealants n = 258, controls n = 178). After a pancreatoduodenectomy, 13% of patients treated with sealants versus 11% of patients without sealants developed a POPF (P = 0.76), of which 4% versus 4% were clinically relevant (P = 0.87). After a distal pancreatectomy, 42% of patients treated with sealants versus 52% of patients without sealants developed a POPF (P = 0.03). Of these, 9% versus 12% were clinically relevant (P = 0.19). The present data do not support the routine use of matrix-bound sealants after a pancreatic resection, as there was no effect on clinically relevant POPF. Larger, well-designed studies are needed to determine the efficacy of sealants in preventing POPF after a pancreatoduodenectomy. © 2015 International Hepato-Pancreato-Biliary Association.

Not all choices are created equal: Task-relevant choices enhance motor learning compared to task-irrelevant choices.

PubMed

Carter, Michael J; Ste-Marie, Diane M

2017-12-01

Lewthwaite et al. (2015) reported that the learning benefits of exercising choice (i.e., their self-controlled condition) are not restricted to task-relevant features (e.g., feedback). They found that choosing one's golf ball color (Exp. 1) or choosing which of two tasks to perform at a later time plus which of two artworks to hang (Exp. 2) resulted in better retention than did being denied these same choices (i.e., yoked condition). The researchers concluded that the learning benefits derived from choice, whether irrelevant or relevant to the to-be-learned task, are predominantly motivational because choice is intrinsically rewarding and satisfies basic psychological needs. However, the absence of a group that made task-relevant choices and the lack of psychological measures significantly weakened their conclusions. Here, we investigated how task-relevant and task-irrelevant choices affect motor-skill learning. Participants practiced a spatiotemporal motor task in either a task-relevant group (choice over feedback schedule), a task-irrelevant group (choice over the color of an arm-wrap plus game selection), or a no-choice group. The results showed significantly greater learning in the task-relevant group than in both the task-irrelevant and no-choice groups, who did not differ significantly. Critically, these learning differences were not attributed to differences in perceptions of competence or autonomy, but instead to superior error-estimation abilities. These results challenge the perspective that motivational influences are the root cause of self-controlled learning advantages. Instead, the findings add to the growing evidence highlighting that the informational value gained from task-relevant choices makes a greater relative contribution to these advantages than motivational influences do.

Connecting the Curriculum through National Science and Mathematics Standards: A Matrix Approach.

ERIC Educational Resources Information Center

Francis, Raymond

This paper provides instructions for linking conceptual understandings using the Connections Matrix. The Connections Matrix and the process of connecting the curriculum works equally well with state-level learning objectives or outcomes. The intent of this process is to help educators see the overlap and connections between what teachers say they…

Active subspace: toward scalable low-rank learning.

PubMed

Liu, Guangcan; Yan, Shuicheng

2012-12-01

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.

PubMed

Ying, Shihui; Wen, Zhijie; Shi, Jun; Peng, Yaxin; Peng, Jigen; Qiao, Hong

2017-05-18

In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.

Improving Recall Using Database Management Systems: A Learning Strategy.

ERIC Educational Resources Information Center

Jonassen, David H.

1986-01-01

Describes the use of microcomputer database management systems to facilitate the instructional uses of learning strategies relating to information processing skills, especially recall. Two learning strategies, cross-classification matrixing and node acquisition and integration, are highlighted. (Author/LRW)

Student Involvement in Learning and School Achievement.

ERIC Educational Resources Information Center

Anderson, Lorin W.

The purpose of the study was to investigate the relationship between selected student characteristics, student involvement in learning, and achievement. Both naturalistic (n = 28, 27) and experimental studies were conducted. In the experimental study, two classes (n = 29, 26) learned a sequence of matrix arithmetic by mastery learning strategies.…

The cost of selective attention in category learning: Developmental differences between adults and infants

PubMed Central

Best, Catherine A.; Yim, Hyungwook; Sloutsky, Vladimir M.

2013-01-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6–8 months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. PMID:23773914

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Maintenance of neural progenitor cell stemness in 3D hydrogels requires matrix remodelling

NASA Astrophysics Data System (ADS)

Madl, Christopher M.; Lesavage, Bauer L.; Dewi, Ruby E.; Dinh, Cong B.; Stowers, Ryan S.; Khariton, Margarita; Lampe, Kyle J.; Nguyen, Duong; Chaudhuri, Ovijit; Enejder, Annika; Heilshorn, Sarah C.

2017-12-01

Neural progenitor cell (NPC) culture within three-dimensional (3D) hydrogels is an attractive strategy for expanding a therapeutically relevant number of stem cells. However, relatively little is known about how 3D material properties such as stiffness and degradability affect the maintenance of NPC stemness in the absence of differentiation factors. Over a physiologically relevant range of stiffness from ~0.5 to 50 kPa, stemness maintenance did not correlate with initial hydrogel stiffness. In contrast, hydrogel degradation was both correlated with, and necessary for, maintenance of NPC stemness. This requirement for degradation was independent of cytoskeletal tension generation and presentation of engineered adhesive ligands, instead relying on matrix remodelling to facilitate cadherin-mediated cell-cell contact and promote β-catenin signalling. In two additional hydrogel systems, permitting NPC-mediated matrix remodelling proved to be a generalizable strategy for stemness maintenance in 3D. Our findings have identified matrix remodelling, in the absence of cytoskeletal tension generation, as a previously unknown strategy to maintain stemness in 3D.

Maintenance of Neural Progenitor Cell Stemness in 3D Hydrogels Requires Matrix Remodeling

PubMed Central

Madl, Christopher M.; LeSavage, Bauer L.; Dewi, Ruby E.; Dinh, Cong B.; Stowers, Ryan S.; Khariton, Margarita; Lampe, Kyle J.; Nguyen, Duong; Chaudhuri, Ovijit; Enejder, Annika; Heilshorn, Sarah C.

2017-01-01

Neural progenitor cell (NPC) culture within 3D hydrogels is an attractive strategy for expanding a therapeutically-relevant number of stem cells. However, relatively little is known about how 3D material properties such as stiffness and degradability affect the maintenance of NPC stemness in the absence of differentiation factors. Over a physiologically-relevant range of stiffness from ~0.5–50 kPa, stemness maintenance did not correlate with initial hydrogel stiffness. In contrast, hydrogel degradation was both correlated with, and necessary for, maintenance of NPC stemness. This requirement for degradation was independent of cytoskeletal tension generation and presentation of engineered adhesive ligands, instead relying on matrix remodeling to facilitate cadherin-mediated cell-cell contact and promote β-catenin signaling. In two additional hydrogel systems, permitting NPC-mediated matrix remodeling proved to be a generalizable strategy for stemness maintenance in 3D. Our findings have identified matrix remodeling, in the absence of cytoskeletal tension generation, as a previously unknown strategy to maintain stemness in 3D. PMID:29115291

Fostering Personal Meaning and Self-Relevance: A Self-Determination Theory Perspective on Internalization

ERIC Educational Resources Information Center

Vansteenkiste, Maarten; Aelterman, Nathalie; De Muynck, Gert-Jan; Haerens, Leen; Patall, Erika; Reeve, Johnmarshall

2018-01-01

Central to self-determination theory (SDT) is the notion that autonomously motivated learning relates to greater learning benefits. While learners' intrinsic motivation has received substantial attention, learners also display volitional learning when they come to endorse the personal meaning or self-relevance of the learning task. In Part I of…

A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine.

PubMed

Duan, Mingxing; Li, Kenli; Liao, Xiangke; Li, Keqin

2018-06-01

As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix decomposition algorithm, and matrix decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an speedup on a cluster with ten nodes, and reaches a speedup with 15 nodes, an speedup with 20 nodes, a speedup with 25 nodes, a speedup with 30 nodes, and a speedup with 35 nodes.

Interfacial and capillary phenomena in solidification processing of metal-matrix composites

NASA Technical Reports Server (NTRS)

Asthana, R.; Tewari, S. N.

1993-01-01

Chemical and hydrodynamic aspects of wetting and interfacial phenomena during the solidification processing of metal-matrix composites are reviewed. Significant experimental results on fiber-matrix interactions and wetting under equilibrium and non-equilibrium conditions in composites of engineering interest have been compiled, based on a survey of the recent literature. Finally, certain aspects of wetting relevant to stir-casting and infiltration processing of composites are discussed.

Systematic review on the use of matrix-bound sealants in pancreatic resection

PubMed Central

Smits, F Jasmijn; van Santvoort, Hjalmar C; Besselink, Marc G H; Borel Rinkes, Inne H M; Molenaar, I Quintus

2015-01-01

Background Pancreatic fistula is a potentially life-threatening complication after a pancreatic resection. The aim of this systematic review was to evaluate the role of matrix-bound sealants after a pancreatic resection in terms of preventing or ameliorating the course of a post-operative pancreatic fistula. Methods A systematic search was performed in the literature from May 2005 to April 2015. Included were clinical studies using matrix-bound sealants after a pancreatic resection, reporting a post-operative pancreatic fistula (POPF) according to the International Study Group on Pancreatic Fistula classification, in which grade B and C fistulae were considered clinically relevant. Results Two were studies on patients undergoing pancreatoduodenectomy (sealants n = 67, controls n = 27) and four studies on a distal pancreatectomy (sealants n = 258, controls n = 178). After a pancreatoduodenectomy, 13% of patients treated with sealants versus 11% of patients without sealants developed a POPF (P = 0.76), of which 4% versus 4% were clinically relevant (P = 0.87). After a distal pancreatectomy, 42% of patients treated with sealants versus 52% of patients without sealants developed a POPF (P = 0.03). Of these, 9% versus 12% were clinically relevant (P = 0.19). Conclusions The present data do not support the routine use of matrix-bound sealants after a pancreatic resection, as there was no effect on clinically relevant POPF. Larger, well-designed studies are needed to determine the efficacy of sealants in preventing POPF after a pancreatoduodenectomy. PMID:26292846

A Coordinated Approach to Curricular Review and Development in Undergraduate Geoscience Programs: Using a Matrix to Identify and Track Skills and Skill Development

NASA Astrophysics Data System (ADS)

MacDonald, R.; Savina, M. E.

2003-12-01

One approach to curriculum review and development is to construct a matrix of the desired skills versus courses in the departmental curriculum. The matrix approach requires faculty to articulate their goals, identify specific skills, and assess where in the curriculum students will learn and practice these skills and where there are major skills gaps. Faculty members in the Geology Department at Carleton College developed a matrix of skills covered in geology courses with the following objectives: 1) Geology majors should begin their "senior integrative exercise" having practiced multiple times all of the formal steps in the research process (recognizing problems, writing proposals, carrying out a project, reporting a project in several ways); 2) Geology majors should learn and practice a variety of professional and life skills life (e.g. computer skills, field skills, lab skills, and interpretive skills).The matrix was used to identify where in the curriculum various research methods and skills were addressed and to map potential student experiences to the objectives. In Carleton's non-hierarchical curriculum, the matrix was used to verify that students have many opportunities to practice research and life skills regardless of the path they take to completion of the major. In William and Mary's more structured curriculum, the matrix was used to ensure that skills build upon each other from course to course. Faculty members in the Geology Department at the College of William and Mary first used this approach to focus on teaching quantitative skills across the geology curriculum, and later used it in terms of teaching research, communication, and information literacy skills. After articulating goals and skills, faculty members in both departments developed more specific skill lists within each category of skills, then described the current assignments and activities in each course relative to the specific components of the matrix and discussed whether to add assignment or activities. We have found that much conversation among faculty and change within courses happens simply as a result of compiling the matrix. One effect of the use of the matrix is that faculty in the department know fairly specifically what skills students are learning and practicing in their other geology courses. Moreover, some faculty members are better suited by background or inclination to teach certain sets of skills. This coordinated approach avoids unnecessary duplication and allows faculty to build on skills and topics developed in previous courses. The matrix can also be used as a planning tool to identify gaps in the curriculum. In our experience, the skills matrix is a powerful organizational and communication tool. The skills matrix is a representation of what the department believes actually happens in the curriculum. Thus, development of a skills matrix provides a basis for departmental discussions of student learning goals and objectives as well as for describing the existing curriculum. The matrix is also a graphic representation, to college administrators and outside evaluators, of the "intentionality" of an entire curriculum, going beyond single courses and their syllabi. It can be used effectively to engage administration in discussions of departmental planning and needs analysis.

Local matrix learning in clustering and applications for manifold visualization.

PubMed

Arnonkijpanich, Banchar; Hasenfuss, Alexander; Hammer, Barbara

2010-05-01

Electronic data sets are increasing rapidly with respect to both, size of the data sets and data resolution, i.e. dimensionality, such that adequate data inspection and data visualization have become central issues of data mining. In this article, we present an extension of classical clustering schemes by local matrix adaptation, which allows a better representation of data by means of clusters with an arbitrary spherical shape. Unlike previous proposals, the method is derived from a global cost function. The focus of this article is to demonstrate the applicability of this matrix clustering scheme to low-dimensional data embedding for data inspection. The proposed method is based on matrix learning for neural gas and manifold charting. This provides an explicit mapping of a given high-dimensional data space to low dimensionality. We demonstrate the usefulness of this method for data inspection and manifold visualization. 2009 Elsevier Ltd. All rights reserved.

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Learned predictiveness training modulates biases towards using boundary or landmark cues during navigation

PubMed Central

Buckley, Matthew G.; Smith, Alastair D.; Haselgrove, Mark

2015-01-01

A number of navigational theories state that learning about landmark information should not interfere with learning about shape information provided by the boundary walls of an environment. A common test of such theories has been to assess whether landmark information will overshadow, or restrict, learning about shape information. Whilst a number of studies have shown that landmarks are not able to overshadow learning about shape information, some have shown that landmarks can, in fact, overshadow learning about shape information. Given the continued importance of theories that grant the shape information that is provided by the boundary of an environment a special status during learning, the experiments presented here were designed to assess whether the relative salience of shape and landmark information could account for the discrepant results of overshadowing studies. In Experiment 1, participants were first trained that either the landmarks within an arena (landmark-relevant), or the shape information provided by the boundary walls of an arena (shape-relevant), were relevant to finding a hidden goal. In a subsequent stage, when novel landmark and shape information were made relevant to finding the hidden goal, landmarks dominated behaviour for those given landmark-relevant training, whereas shape information dominated behaviour for those given shape-relevant training. Experiment 2, which was conducted without prior relevance training, revealed that the landmark cues, unconditionally, dominated behaviour in our task. The results of the present experiments, and the conflicting results from previous overshadowing experiments, are explained in terms of associative models that incorporate an attention variant. PMID:25409751

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

PubMed Central

Rombouts, Jaldert O.; Bohte, Sander M.; Roelfsema, Pieter R.

2015-01-01

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions. PMID:25742003

Machine-Learned Data Structures of Lipid Marker Serum Concentrations in Multiple Sclerosis Patients Differ from Those in Healthy Subjects

PubMed Central

Lötsch, Jörn; Thrun, Michael; Lerch, Florian; Brunkhorst, Robert; Schiffmann, Susanne; Thomas, Dominique; Tegder, Irmgard; Geisslinger, Gerd; Ultsch, Alfred

2017-01-01

Lipid signaling has been suggested to be a major pathophysiological mechanism of multiple sclerosis (MS). With the increasing knowledge about lipid signaling, acquired data become increasingly complex making bioinformatics necessary in lipid research. We used unsupervised machine-learning to analyze lipid marker serum concentrations, pursuing the hypothesis that for the most relevant markers the emerging data structures will coincide with the diagnosis of MS. Machine learning was implemented as emergent self-organizing feature maps (ESOM) combined with the U*-matrix visualization technique. The data space consisted of serum concentrations of three main classes of lipid markers comprising eicosanoids (d = 11 markers), ceramides (d = 10), and lyosophosphatidic acids (d = 6). They were analyzed in cohorts of MS patients (n = 102) and healthy subjects (n = 301). Clear data structures in the high-dimensional data space were observed in eicosanoid and ceramides serum concentrations whereas no clear structure could be found in lysophosphatidic acid concentrations. With ceramide concentrations, the structures that had emerged from unsupervised machine-learning almost completely overlapped with the known grouping of MS patients versus healthy subjects. This was only partly provided by eicosanoid serum concentrations. Thus, unsupervised machine-learning identified distinct data structures of bioactive lipid serum concentrations. These structures could be superimposed with the known grouping of MS patients versus healthy subjects, which was almost completely possible with ceramides. Therefore, based on the present analysis, ceramides are first-line candidates for further exploration as drug-gable targets or biomarkers in MS. PMID:28590455

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Learning object correspondences with the observed transport shape measure.

PubMed

Pitiot, Alain; Delingette, Hervé; Toga, Arthur W; Thompson, Paul M

2003-07-01

We propose a learning method which introduces explicit knowledge to the object correspondence problem. Our approach uses an a priori learning set to compute a dense correspondence field between two objects, where the characteristics of the field bear close resemblance to those in the learning set. We introduce a new local shape measure we call the "observed transport measure", whose properties make it particularly amenable to the matching problem. From the values of our measure obtained at every point of the objects to be matched, we compute a distance matrix which embeds the correspondence problem in a highly expressive and redundant construct and facilitates its manipulation. We present two learning strategies that rely on the distance matrix and discuss their applications to the matching of a variety of 1-D, 2-D and 3-D objects, including the corpus callosum and ventricular surfaces.

An Interactive System of Computer Generated Graphic Displays for Motivating Meaningful Learning of Matrix Operations and Concepts of Matrix Algebra

DTIC Science & Technology

1990-09-01

community’s search for a workable set of standards for school mathematics . In 1989 the National Council of Teachers of Mathematics ( NCTM ) established the...made by the Commission on Standards for School Mathematics to the National Council of Teachers of Mathematics ( NCTM ). Of the 40 students who...Abstract This -s-y evaluated students’ responses to a teaching method designed to involve students and teachers of mathematics in a meaningful learning

Accurate Monitoring Leads to Effective Control and Greater Learning of Patient Education Materials

ERIC Educational Resources Information Center

Rawson, Katherine A.; O'Neil, Rochelle; Dunlosky, John

2011-01-01

Effective management of chronic diseases (e.g., diabetes) can depend on the extent to which patients can learn and remember disease-relevant information. In two experiments, we explored a technique motivated by theories of self-regulated learning for improving people's learning of information relevant to managing a chronic disease. Materials were…

Necessary Contributions of Human Frontal Lobe Subregions to Reward Learning in a Dynamic, Multidimensional Environment.

PubMed

Vaidya, Avinash R; Fellows, Lesley K

2016-09-21

Real-world decisions are typically made between options that vary along multiple dimensions, requiring prioritization of the important dimensions to support optimal choice. Learning in this setting depends on attributing decision outcomes to the dimensions with predictive relevance rather than to dimensions that are irrelevant and nonpredictive. This attribution problem is computationally challenging, and likely requires an interplay between selective attention and reward learning. Both these processes have been separately linked to the prefrontal cortex, but little is known about how they combine to support learning the reward value of multidimensional stimuli. Here, we examined the necessary contributions of frontal lobe subregions in attributing feedback to relevant and irrelevant dimensions on a trial-by-trial basis in humans. Patients with focal frontal lobe damage completed a demanding reward learning task where options varied on three dimensions, only one of which predicted reward. Participants with left lateral frontal lobe damage attributed rewards to irrelevant dimensions, rather than the relevant dimension. Damage to the ventromedial frontal lobe also impaired learning about the relevant dimension, but did not increase reward attribution to irrelevant dimensions. The results argue for distinct roles for these two regions in learning the value of multidimensional decision options under dynamic conditions, with the lateral frontal lobe required for selecting the relevant dimension to associate with reward, and the ventromedial frontal lobe required to learn the reward association itself. The real world is complex and multidimensional; how do we attribute rewards to predictive features when surrounded by competing cues? Here, we tested the critical involvement of human frontal lobe subregions in a probabilistic, multidimensional learning environment, asking whether focal lesions affected trial-by-trial attribution of feedback to relevant and irrelevant dimensions. The left lateral frontal lobe was required for filtering option dimensions to allow appropriate feedback attribution, while the ventromedial frontal lobe was necessary for learning the value of features in the relevant dimension. These findings argue that selective attention and associative learning processes mediated by anatomically distinct frontal lobe subregions are both critical for adaptive choice in more complex, ecologically valid settings. Copyright © 2016 the authors 0270-6474/16/369843-16$15.00/0.

The cost of selective attention in category learning: developmental differences between adults and infants.

PubMed

Best, Catherine A; Yim, Hyungwook; Sloutsky, Vladimir M

2013-10-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6-8months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. Copyright © 2013 Elsevier Inc. All rights reserved.

Application of Fuzzy Logic to Matrix FMECA

NASA Astrophysics Data System (ADS)

Shankar, N. Ravi; Prabhu, B. S.

2001-04-01

A methodology combining the benefits of Fuzzy Logic and Matrix FMEA is presented in this paper. The presented methodology extends the risk prioritization beyond the conventional Risk Priority Number (RPN) method. Fuzzy logic is used to calculate the criticality rank. Also the matrix approach is improved further to develop a pictorial representation retaining all relevant qualitative and quantitative information of several FMEA elements relationships. The methodology presented is demonstrated by application to an illustrative example.

Comprehensive Thematic T-matrix Reference Database: a 2013-2014 Update

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Zakharova, Nadezhda T.; Khlebtsov, Nikolai G.; Wriedt, Thomas; Videen, Gorden

2014-01-01

This paper is the sixth update to the comprehensive thematic database of peer-reviewedT-matrix publications initiated by us in 2004 and includes relevant publications that have appeared since 2013. It also lists several earlier publications not incorporated in the original database and previous updates.

Introducing Matrix Management within a Children's Services Setting--Personal Reflections

ERIC Educational Resources Information Center

Brooks, Michael; Kakabadse, Nada K.

2014-01-01

This article reflects on the introduction of "matrix management" arrangements for an Educational Psychology Service (EPS) within a Children's Service Directorate of a Local Authority (LA). It seeks to demonstrate critical self-awareness, consider relevant literature with a view to bringing insights to processes and outcomes, and offers…

Promising ethical arguments for product differentiation in the organic food sector. A mixed methods research approach.

PubMed

Zander, Katrin; Stolz, Hanna; Hamm, Ulrich

2013-03-01

Ethical consumerism is a growing trend worldwide. Ethical consumers' expectations are increasing and neither the Fairtrade nor the organic farming concept covers all the ethical concerns of consumers. Against this background the aim of this research is to elicit consumers' preferences regarding organic food with additional ethical attributes and their relevance at the market place. A mixed methods research approach was applied by combining an Information Display Matrix, Focus Group Discussions and Choice Experiments in five European countries. According to the results of the Information Display Matrix, 'higher animal welfare', 'local production' and 'fair producer prices' were preferred in all countries. These three attributes were discussed with Focus Groups in depth, using rather emotive ways of labelling. While the ranking of the attributes was the same, the emotive way of communicating these attributes was, for the most part, disliked by participants. The same attributes were then used in Choice Experiments, but with completely revised communication arguments. According to the results of the Focus Groups, the arguments were presented in a factual manner, using short and concise statements. In this research step, consumers in all countries except Austria gave priority to 'local production'. 'Higher animal welfare' and 'fair producer prices' turned out to be relevant for buying decisions only in Germany and Switzerland. According to our results, there is substantial potential for product differentiation in the organic sector through making use of production standards that exceed existing minimum regulations. The combination of different research methods in a mixed methods approach proved to be very helpful. The results of earlier research steps provided the basis from which to learn - findings could be applied in subsequent steps, and used to adjust and deepen the research design. Copyright © 2012 Elsevier Ltd. All rights reserved.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

Face verification with balanced thresholds.

PubMed

Yan, Shuicheng; Xu, Dong; Tang, Xiaoou

2007-01-01

The process of face verification is guided by a pre-learned global threshold, which, however, is often inconsistent with class-specific optimal thresholds. It is, hence, beneficial to pursue a balance of the class-specific thresholds in the model-learning stage. In this paper, we present a new dimensionality reduction algorithm tailored to the verification task that ensures threshold balance. This is achieved by the following aspects. First, feasibility is guaranteed by employing an affine transformation matrix, instead of the conventional projection matrix, for dimensionality reduction, and, hence, we call the proposed algorithm threshold balanced transformation (TBT). Then, the affine transformation matrix, constrained as the product of an orthogonal matrix and a diagonal matrix, is optimized to improve the threshold balance and classification capability in an iterative manner. Unlike most algorithms for face verification which are directly transplanted from face identification literature, TBT is specifically designed for face verification and clarifies the intrinsic distinction between these two tasks. Experiments on three benchmark face databases demonstrate that TBT significantly outperforms the state-of-the-art subspace techniques for face verification.

Cascaded VLSI Chips Help Neural Network To Learn

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher; Thakoor, Anilkumar P.

1993-01-01

Cascading provides 12-bit resolution needed for learning. Using conventional silicon chip fabrication technology of VLSI, fully connected architecture consisting of 32 wide-range, variable gain, sigmoidal neurons along one diagonal and 7-bit resolution, electrically programmable, synaptic 32 x 31 weight matrix implemented on neuron-synapse chip. To increase weight nominally from 7 to 13 bits, synapses on chip individually cascaded with respective synapses on another 32 x 32 matrix chip with 7-bit resolution synapses only (without neurons). Cascade correlation algorithm varies number of layers effectively connected into network; adds hidden layers one at a time during learning process in such way as to optimize overall number of neurons and complexity and configuration of network.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Frames of Reference for the Assessment of Learning Disabilities: New Views on Measurement Issues.

ERIC Educational Resources Information Center

Lyon, G. Reid, Ed.

This book offers 27 papers addressing critical issues in the assessment of students with all kinds of learning disabilities. Papers have the following titles and authors: "Critical Issues in the Measurement of Learning Disabilities" (G. Reid Lyon); "A Matrix of Decision Points in the Measurement of Learning Disabilities" (Barbara K. Keogh);…

Tensor Decompositions for Learning Latent Variable Models

DTIC Science & Technology

2012-12-08

and eigenvectors of tensors is generally significantly more complicated than their matrix counterpart (both algebraically [Qi05, CS11, Lim05] and...The reduction First, let W ∈ Rd×k be a linear transformation such that M2(W,W ) = W M2W = I where I is the k × k identity matrix (i.e., W whitens ...approximate the whitening matrix W ∈ Rd×k from second-moment matrix M2 ∈ Rd×d. To do this, one first multiplies M2 by a random matrix R ∈ Rd×k′ for some k′ ≥ k

Developing a Learning Classroom: Moving beyond Management through Relationships, Relevance, and Rigor

ERIC Educational Resources Information Center

Cooper, Nic; Garner, Betty K.

2012-01-01

All too often, managing a classroom means gaining control, dictating guidelines, and implementing rules. Designed for any teacher struggling with student behavior, motivation, and engagement, "Developing a Learning Classroom" explores how to create a thriving, learning-centered classroom through three critical concepts: relationships, relevance,…

International Service Learning: Analytical Review of Published Research Literature

ERIC Educational Resources Information Center

Dixon, Brett

2015-01-01

International service learning (ISL) is an emerging area of international education. This paper summarizes academic journal articles on ISL programs and organizes the relevant publications by academic disciplines, service learning project areas, and other topics. The basis for this review is relevant literature from full-text scholarly peer…

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

PubMed

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.

Matrix Metalloproteinase (MMP) 9 Transcription in Mouse Brain Induced by Fear Learning*

PubMed Central

Ganguly, Krishnendu; Rejmak, Emilia; Mikosz, Marta; Nikolaev, Evgeni; Knapska, Ewelina; Kaczmarek, Leszek

2013-01-01

Memory formation requires learning-based molecular and structural changes in neurons, whereas matrix metalloproteinase (MMP) 9 is involved in the synaptic plasticity by cleaving extracellular matrix proteins and, thus, is associated with learning processes in the mammalian brain. Because the mechanisms of MMP-9 transcription in the brain are poorly understood, this study aimed to elucidate regulation of MMP-9 gene expression in the mouse brain after fear learning. We show here that contextual fear conditioning markedly increases MMP-9 transcription, followed by enhanced enzymatic levels in the three major brain structures implicated in fear learning, i.e. the amygdala, hippocampus, and prefrontal cortex. To reveal the role of AP-1 transcription factor in MMP-9 gene expression, we have used reporter gene constructs with specifically mutated AP-1 gene promoter sites. The constructs were introduced into the medial prefrontal cortex of neonatal mouse pups by electroporation, and the regulation of MMP-9 transcription was studied after contextual fear conditioning in the adult animals. Specifically, −42/-50- and −478/-486-bp AP-1 binding motifs of the mouse MMP-9 promoter sequence have been found to play a major role in MMP-9 gene activation. Furthermore, increases in MMP-9 gene promoter binding by the AP-1 transcription factor proteins c-Fos and c-Jun have been demonstrated in all three brain structures under investigation. Hence, our results suggest that AP-1 acts as a positive regulator of MMP-9 transcription in the brain following fear learning. PMID:23720741

Matrix metalloproteinase (MMP) 9 transcription in mouse brain induced by fear learning.

PubMed

Ganguly, Krishnendu; Rejmak, Emilia; Mikosz, Marta; Nikolaev, Evgeni; Knapska, Ewelina; Kaczmarek, Leszek

2013-07-19

Memory formation requires learning-based molecular and structural changes in neurons, whereas matrix metalloproteinase (MMP) 9 is involved in the synaptic plasticity by cleaving extracellular matrix proteins and, thus, is associated with learning processes in the mammalian brain. Because the mechanisms of MMP-9 transcription in the brain are poorly understood, this study aimed to elucidate regulation of MMP-9 gene expression in the mouse brain after fear learning. We show here that contextual fear conditioning markedly increases MMP-9 transcription, followed by enhanced enzymatic levels in the three major brain structures implicated in fear learning, i.e. the amygdala, hippocampus, and prefrontal cortex. To reveal the role of AP-1 transcription factor in MMP-9 gene expression, we have used reporter gene constructs with specifically mutated AP-1 gene promoter sites. The constructs were introduced into the medial prefrontal cortex of neonatal mouse pups by electroporation, and the regulation of MMP-9 transcription was studied after contextual fear conditioning in the adult animals. Specifically, -42/-50- and -478/-486-bp AP-1 binding motifs of the mouse MMP-9 promoter sequence have been found to play a major role in MMP-9 gene activation. Furthermore, increases in MMP-9 gene promoter binding by the AP-1 transcription factor proteins c-Fos and c-Jun have been demonstrated in all three brain structures under investigation. Hence, our results suggest that AP-1 acts as a positive regulator of MMP-9 transcription in the brain following fear learning.

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

PubMed Central

Jaeger, Sébastien; Thieffry, Denis

2017-01-01

Abstract Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines. PMID:28591841

Fast calculation of the `ILC norm' in iterative learning control

NASA Astrophysics Data System (ADS)

Rice, Justin K.; van Wingerden, Jan-Willem

2013-06-01

In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

"Sankofa" Teaching and Learning: Evaluating Relevance for Today's African-American Student

ERIC Educational Resources Information Center

Talpade, Medha; Talpade, Salil

2014-01-01

The intent of this project was to identify and relate the values and perceptions of today's African American students to culturally relevant teaching and learning practices. The reason for relating student culture with teaching practices is to improve pedagogical processes for African American students. Culturally relevant pedagogy, according to…

Dynamic Textures Modeling via Joint Video Dictionary Learning.

PubMed

Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng

2017-04-06

Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

A robot conditioned reflex system modeled after the cerebellum.

NASA Technical Reports Server (NTRS)

Albus, J. S.

1972-01-01

Reduction of a theory of cerebellar function to computer software for the control of a mechanical manipulator. This reduction is achieved by considering the cerebellum, along with the higher-level brain centers which control it, as a type of finite-state machine with input entering the cerebellum via mossy fibers from the periphery and output from the cerebellum occurring via Purkinje cells. It is hypothesized that the cerebellum learns by an error-correction system similar to Perceptron training algorithms. An electromechanical model of the cerebellum is then developed for the control of a mechanical arm. The problem of modeling the granular layer which selects the set of parallel fibers which are active at any instant of time is considered, and a relevance matrix is constructed to model the relative degree of influence which mossy fibers from the various joints have on the sets of granule cells unique to each joint.

Fibroblast-matrix interplay: Nintedanib and pirfenidone modulate the effect of IPF fibroblast-conditioned matrix on normal fibroblast phenotype.

PubMed

Epstein Shochet, Gali; Wollin, Lutz; Shitrit, David

2018-03-12

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis. Activated fibroblasts are the key effector cells in fibrosis, producing excessive amounts of collagen and extracellular matrix (ECM) proteins. Whether the ECM conditioned by IPF fibroblasts determines the phenotype of naïve fibroblasts is difficult to explore. IPF-derived primary fibroblasts were cultured on Matrigel and then cleared using ammonium hydroxide, creating an IPF-conditioned matrix (CM). Normal fibroblast CM served as control. Normal fibroblasts were cultured on both types of CM, and cell count, cell distribution and markers of myofibroblast differentiation; transforming growth factor beta (TGFβ) signalling; and ECM expression were assessed. The effects of the anti-fibrotic drugs nintedanib and pirfenidone at physiologically relevant concentrations were also explored. Normal fibroblasts cultured on IPF-CM arranged in large aggregates as a result of increased proliferation and migration. Moreover, increased levels of pSmad3, pSTAT3 (phospho signal transducer and activator of transcription 3), alpha smooth muscle actin (αSMA) and Collagen1a were found, suggesting a differentiation towards a myofibroblast-like phenotype. SB505124 (10 μmol/L) partially reversed these alterations, suggesting a TGFβ contribution. Furthermore, nintedanib at 100 nmol/L and, to a lesser extent, pirfenidone at 100 μmol/L prevented the IPF-CM-induced fibroblast phenotype alterations, suggesting an attenuation of the ECM-fibroblast interplay. IPF fibroblasts alter the ECM, thus creating a CM that further propagates an IPF-like phenotype in normal fibroblasts. This assay demonstrated differences in drug activities for approved IPF drugs at clinically relevant concentrations. Thus, the matrix-fibroblast phenotype interplay might be a relevant assay to explore drug candidates for IPF treatment. © 2018 Asian Pacific Society of Respirology.

The Relevance of Workplace Learning in Guiding Student and Curriculum Development

ERIC Educational Resources Information Center

Nduna, N. J.

2012-01-01

In an attempt to demonstrate the relevance of workplace learning (previously known as "cooperative education") in guiding student and curriculum development, this article presents findings from a research project on the current practice of workplace learning, drawn from an analysis of evaluation reports in a university of technology.…

Towards Increased Relevance: Context-Adapted Models of the Learning Organization

ERIC Educational Resources Information Center

Örtenblad, Anders

2015-01-01

Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

Science Spots AR: A Platform for Science Learning Games with Augmented Reality

ERIC Educational Resources Information Center

Laine, Teemu H.; Nygren, Eeva; Dirin, Amir; Suk, Hae-Jung

2016-01-01

Lack of motivation and of real-world relevance have been identified as reasons for low interest in science among children. Game-based learning and storytelling are prominent methods for generating intrinsic motivation in learning. Real-world relevance requires connecting abstract scientific concepts with the real world. This can be done by…

Some New Theoretical Issues in Systems Thinking Relevant for Modelling Corporate Learning

ERIC Educational Resources Information Center

Minati, Gianfranco

2007-01-01

Purpose: The purpose of this paper is to describe fundamental concepts and theoretical challenges with regard to systems, and to build on these in proposing new theoretical frameworks relevant to learning, for example in so-called learning organizations. Design/methodology/approach: The paper focuses on some crucial fundamental aspects introduced…

A Matrix for Processing Descriptive and Judgmental Data for Use with the Learning Area Sections of the "Evaluative Criteria," Sixth Edition.

ERIC Educational Resources Information Center

Pace, Vernon D.; Buser, Robert L.

1990-01-01

Presents a matrix that can be used by accreditation team members to gather, organize, analyze, and report descriptive and evaluative information about instructional support components, teacher observations/interviews, and curriculum evaluation. (DMM)

Exploring Deep Learning and Sparse Matrix Format Selection

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

Zhao, Y.; Liao, C.; Shen, X.

We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat;more » in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.« less

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Implementation of biological tissue Mueller matrix for polarization-sensitive optical coherence tomography based on LabVIEW

NASA Astrophysics Data System (ADS)

Lin, Yongping; Zhang, Xiyang; He, Youwu; Cai, Jianyong; Li, Hui

2018-02-01

The Jones matrix and the Mueller matrix are main tools to study polarization devices. The Mueller matrix can also be used for biological tissue research to get complete tissue properties, while the commercial optical coherence tomography system does not give relevant analysis function. Based on the LabVIEW, a near real time display method of Mueller matrix image of biological tissue is developed and it gives the corresponding phase retardant image simultaneously. A quarter-wave plate was placed at 45 in the sample arm. Experimental results of the two orthogonal channels show that the phase retardance based on incident light vector fixed mode and the Mueller matrix based on incident light vector dynamic mode can provide an effective analysis method of the existing system.

Matrix thermalization

NASA Astrophysics Data System (ADS)

Craps, Ben; Evnin, Oleg; Nguyen, Kévin

2017-02-01

Matrix quantum mechanics offers an attractive environment for discussing gravitational holography, in which both sides of the holographic duality are well-defined. Similarly to higher-dimensional implementations of holography, collapsing shell solutions in the gravitational bulk correspond in this setting to thermalization processes in the dual quantum mechanical theory. We construct an explicit, fully nonlinear supergravity solution describing a generic collapsing dilaton shell, specify the holographic renormalization prescriptions necessary for computing the relevant boundary observables, and apply them to evaluating thermalizing two-point correlation functions in the dual matrix theory.

Analytical Solution for Transport with Bimolecular Reactions in Fracture-Matrix Systems with Application to In-Situ Chemical Oxidation

NASA Astrophysics Data System (ADS)

Rajaram, H.; Arshadi, M.

2016-12-01

In-situ chemical oxidation (ISCO) is an effective strategy for remediation of DNAPL contamination in fractured rock. During ISCO, an oxidant (e.g. permanganate) is typically injected through fractures and is consumed by bimolecular reactions with DNAPLs such as TCE and natural organic matter in the fracture and the adjacent rock matrix. Under these conditions, moving reaction fronts form and propagate along the fracture and into the rock matrix. The propagation of these reaction fronts is strongly influenced by the heterogeneity/discontinuity across the fracture-matrix interface (advective transport dominates in the fractures, while diffusive transport dominates in the rock matrix). We present analytical solutions for the concentrations of the oxidant, TCE and natural organic matter; and the propagation of the reaction fronts in a fracture-matrix system. Our approximate analytical solutions assume advection and reaction dominate over diffusion/dispersion in the fracture and neglect the latter. Diffusion and reaction with both TCE and immobile natural organic matter in the rock matrix are considered. The behavior of the reaction-diffusion equations in the rock matrix is posed as a Stefan problem where the diffusing oxidant reacts with both diffusing (TCE) and immobile (natural organic matter) reductants. Our analytical solutions establish that the reaction fronts propagate diffusively (i.e. as the square root of time) in both the matrix and the fracture. Our analytical solutions agree very well with numerical simulations for the case of uniform advection in the fracture. We also present extensions of our analytical solutions to non-uniform flows in the fracture by invoking a travel-time transformation. The non-uniform flow solutions are relevant to field applications of ISCO. The approximate analytical solutions are relevant to a broad class of reactive transport problems in fracture-matrix systems where moving reaction fronts occur.

The Unconscious Allocation of Cognitive Resources to Task-Relevant and Task-Irrelevant Thoughts

ERIC Educational Resources Information Center

Kuldas, Seffetullah; Hashim, Shahabuddin; Ismail, Hairul Nizam; Samsudin, Mohd Ali; Bakar, Zainudin Abu

2014-01-01

Conscious allocation of cognitive resources to task-relevant thoughts is necessary for learning. However, task-irrelevant thoughts often associated with fear of failure can enter the mind and interfere with learning. Effects like this prompt the question of whether or not learners consciously shift their cognitive resources from task-relevant to…

Bringing Relevance to Elearning--A Gender Perspective

ERIC Educational Resources Information Center

Wallace, Ann; Panteli, Niki

2018-01-01

In this paper, we discuss the importance of relevance in the provision of eLearning for the pursuit of higher education (HE). In particular, we argue how the extant literature focuses on quality and security in the design of eLearning platforms, but pays scant attention to how relevant the platform and the programme contents are to the needs of…

"FAILING GIRLS": Understanding Connections among Identity Negotiation, Personal Relevance, and Engagement in Science Learning from Underachieving Girls

NASA Astrophysics Data System (ADS)

Thompson, Jessica J.; Windschitl, Mark

Contemporary critiques of science education have noted that girls often fail to engage in science learning because the activities lack relevance for them, and they cannot "see themselves" in the work of science. Despite the empirical support for these claims, theory around the important connections between relevance, emerging self-identity, and engagement for girls remains underdeveloped. This qualitative, exploratory investigation examines engagement in science learning among five underachieving high school girls. Data sources include in-depth interviews, classroom observations, and teacher surveys. The girls were asked to describe engagement within three learning contexts: science class, a favorite class, and an extracurricular activity. From the girls' voices emerge three themes reflecting the centrality of self: "who I am," "who I am becoming," and "the importance of relationships." It is important that these themes of self and of identity negotiation are integrated with the ways these girls find learning personally relevant. One pattern of extracurricular engagement and two patterns of science engagement (integrated and situational) are described. This study attempts to expand the dialogue around the relationships between identity, relevance, and engagement among underachieving girls and suggests ways in which curriculum can be grounded in students' lives and developing identities.

Transformative Sustainability Learning: Cultivating a Tree-Planting Ethos in Western Kenya

ERIC Educational Resources Information Center

Bull, Marijoan

2013-01-01

Given the fundamental objective of ESD--perspective change--it is increasingly being aligned with the theoretical foundation of Mezirow's Transformative Learning. In 2008, Sipos et al. built upon this connection by proposing a matrix of learning objectives to assess ESD in formal settings. These objectives, grouped under the title of…

[Continuum, the continuing education platform based on a competency matrix].

PubMed

Ochoa Sangrador, C; Villaizán Pérez, C; González de Dios, J; Hijano Bandera, F; Málaga Guerrero, S

2016-04-01

Competency-Based Education is a learning method that has changed the traditional teaching-based focus to a learning-based one. Students are the centre of the process, in which they must learn to learn, solve problems, and adapt to changes in their environment. The goal is to provide learning based on knowledge, skills (know-how), attitude and behaviour. These sets of knowledge are called competencies. It is essential to have a reference of the required competencies in order to identify the need for them. Their acquisition is approached through teaching modules, in which one or more skills can be acquired. This teaching strategy has been adopted by Continuum, the distance learning platform of the Spanish Paediatric Association, which has developed a competency matrix based on the Global Paediatric Education Consortium training program. In this article, a review will be presented on the basics of Competency-Based Education and how it is applied in Continuum. Copyright © 2015 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Another Face of the Hero: "The Matrix" as Modern Hero-Quest.

ERIC Educational Resources Information Center

Stroud, Scott R.

This paper analyzes the interesting narrative structure of the hero-quest myth contained within the 1999 film, "The Matrix," and explicates the implications of this message upon the audience. Initially, the relevance of myth to movies and the format of Joseph Campbell's hero-quest is illustrated. This format is then applied to "The…

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

"The Learning Sticks": Reflections on a Case Study of Role-Playing for Sustainability

ERIC Educational Resources Information Center

Gordon, Sue; Thomas, Ian

2018-01-01

Use of role-plays to develop deep student-learning has many advocates. Role-play is a powerful approach for learning that develops relevant skills in a range of disciplines and situations. In Higher Education, sustainability programmes role-play pedagogy appears to have great relevance for developing the competencies that these graduates will…

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.

PubMed

Castro-Mondragon, Jaime Abraham; Jaeger, Sébastien; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2017-07-27

Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning.

PubMed

Grimm, Lisa R; Maddox, W Todd

2013-11-01

Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning. © 2013.

Teaching the extracellular matrix and introducing online databases within a multidisciplinary course with i-cell-MATRIX: A student-centered approach.

PubMed

Sousa, João Carlos; Costa, Manuel João; Palha, Joana Almeida

2010-03-01

The biochemistry and molecular biology of the extracellular matrix (ECM) is difficult to convey to students in a classroom setting in ways that capture their interest. The understanding of the matrix's roles in physiological and pathological conditions study will presumably be hampered by insufficient knowledge of its molecular structure. Internet-available resources can bridge the division between the molecular details and ECM's biological properties and associated processes. This article presents an approach to teach the ECM developed for first year medical undergraduates who, working in teams: (i) Explore a specific molecular component of the matrix, (ii) identify a disease in which the component is implicated, (iii) investigate how the component's structure/function contributes to ECM' supramolecular organization in physiological and in pathological conditions, and (iv) share their findings with colleagues. The approach-designated i-cell-MATRIX-is focused on the contribution of individual components to the overall organization and biological functions of the ECM. i-cell-MATRIX is student centered and uses 5 hours of class time. Summary of results and take home message: A "1-minute paper" has been used to gather student feedback on the impact of i-cell-MATRIX. Qualitative analysis of student feedback gathered in three consecutive years revealed that students appreciate the approach's reliance on self-directed learning, the interactivity embedded and the demand for deeper insights on the ECM. Learning how to use internet biomedical resources is another positive outcome. Ninety percent of students recommend the activity for subsequent years. i-cell-MATRIX is adaptable by other medical schools which may be looking for an approach that achieves higher student engagement with the ECM. Copyright © 2010 International Union of Biochemistry and Molecular Biology, Inc.

Feature saliency and feedback information interactively impact visual category learning

PubMed Central

Hammer, Rubi; Sloutsky, Vladimir; Grill-Spector, Kalanit

2015-01-01

Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously. PMID:25745404

The Role of Specificity, Targeted Learning Activities, and Prior Knowledge for the Effects of Relevance Instructions

ERIC Educational Resources Information Center

Roelle, Julian; Lehmkuhl, Nina; Beyer, Martin-Uwe; Berthold, Kirsten

2015-01-01

In 2 experiments we examined the role of (a) specificity, (b) the type of targeted learning activities, and (c) learners' prior knowledge for the effects of relevance instructions on learning from instructional explanations. In Experiment 1, we recruited novices regarding the topic of atomic structure (N = 80) and found that "specific"…

A Draft Conceptual Framework of Relevant Theories to Inform Future Rigorous Research on Student Service-Learning Outcomes

ERIC Educational Resources Information Center

Whitley, Meredith A.

2014-01-01

While the quality and quantity of research on service-learning has increased considerably over the past 20 years, researchers as well as governmental and funding agencies have called for more rigor in service-learning research. One key variable in improving rigor is using relevant existing theories to improve the research. The purpose of this…

Motor-visual neurons and action recognition in social interactions.

PubMed

de la Rosa, Stephan; Bülthoff, Heinrich H

2014-04-01

Cook et al. suggest that motor-visual neurons originate from associative learning. This suggestion has interesting implications for the processing of socially relevant visual information in social interactions. Here, we discuss two aspects of the associative learning account that seem to have particular relevance for visual recognition of social information in social interactions - namely, context-specific and contingency based learning.

Defense Acquisition Review Journal. Volume 16, Number 3, Issue 52

DTIC Science & Technology

2009-10-01

This is also theorized to lead to increased ability for a student to transfer that learning experience into their everyday workplace experiences. In...hands-on, apprenticeship -type learning environment, increased motivation, and increased relevance for students through interactivity and...vation to learn and increasing perceived relevance of the instruction . This article covers the use of games and simulations in three different

Varying irrelevant phonetic features hinders learning of the feature being trained.

PubMed

Antoniou, Mark; Wong, Patrick C M

2016-01-01

Learning to distinguish nonnative words that differ in a critical phonetic feature can be difficult. Speech training studies typically employ methods that explicitly direct the learner's attention to the relevant nonnative feature to be learned. However, studies on vision have demonstrated that perceptual learning may occur implicitly, by exposing learners to stimulus features, even if they are irrelevant to the task, and it has recently been suggested that this task-irrelevant perceptual learning framework also applies to speech. In this study, subjects took part in a seven-day training regimen to learn to distinguish one of two nonnative features, namely, voice onset time or lexical tone, using explicit training methods consistent with most speech training studies. Critically, half of the subjects were exposed to stimuli that varied not only in the relevant feature, but in the irrelevant feature as well. The results showed that subjects who were trained with stimuli that varied in the relevant feature and held the irrelevant feature constant achieved the best learning outcomes. Varying both features hindered learning and generalization to new stimuli.

Scaramouche Goes to Preschool: The Complex Matrix of Young Children's Everyday Music

ERIC Educational Resources Information Center

Ilari, Beatriz

2018-01-01

This article examines everyday musical practices and their connections to young children's learning and development, in and through music. It begins with a discussion of music learning in early childhood as a form of participation and levels of intention in learning. Next, conceptions of child that have dominated early childhood music education…

The Development of a Proposed Global Work-Integrated Learning Framework

ERIC Educational Resources Information Center

McRae, Norah; Johnston, Nancy

2016-01-01

Building on the work completed in BC that resulted in the development of a WIL Matrix for comparing and contrasting various forms of WIL with the Canadian co-op model, this paper proposes a Global Work-Integrated Learning Framework that allows for the comparison of a variety of models of work-integrated learning found in the international…

Conditions Enhancing Self-Directed Learning in the Workplace. A Report to the Participants.

ERIC Educational Resources Information Center

1992

The appreciative inquiry process was used to identify conditions enhancing self-directed learning. Participants in the project did the following: (1) used the five-step process to identify factors/conditions/forces that seemed to cause self-directed learning to occur; (2) created a matrix by combining the factors/conditions/forces with six…

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

PubMed Central

Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

2013-01-01

Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

Modeling Single Well Injection-Withdrawal (SWIW) Tests for Characterization of Complex Fracture-Matrix Systems

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

Cotte, F.P.; Doughty, C.; Birkholzer, J.

2010-11-01

The ability to reliably predict flow and transport in fractured porous rock is an essential condition for performance evaluation of geologic (underground) nuclear waste repositories. In this report, a suite of programs (TRIPOLY code) for calculating and analyzing flow and transport in two-dimensional fracture-matrix systems is used to model single-well injection-withdrawal (SWIW) tracer tests. The SWIW test, a tracer test using one well, is proposed as a useful means of collecting data for site characterization, as well as estimating parameters relevant to tracer diffusion and sorption. After some specific code adaptations, we numerically generated a complex fracture-matrix system for computationmore » of steady-state flow and tracer advection and dispersion in the fracture network, along with solute exchange processes between the fractures and the porous matrix. We then conducted simulations for a hypothetical but workable SWIW test design and completed parameter sensitivity studies on three physical parameters of the rock matrix - namely porosity, diffusion coefficient, and retardation coefficient - in order to investigate their impact on the fracture-matrix solute exchange process. Hydraulic fracturing, or hydrofracking, is also modeled in this study, in two different ways: (1) by increasing the hydraulic aperture for flow in existing fractures and (2) by adding a new set of fractures to the field. The results of all these different tests are analyzed by studying the population of matrix blocks, the tracer spatial distribution, and the breakthrough curves (BTCs) obtained, while performing mass-balance checks and being careful to avoid some numerical mistakes that could occur. This study clearly demonstrates the importance of matrix effects in the solute transport process, with the sensitivity studies illustrating the increased importance of the matrix in providing a retardation mechanism for radionuclides as matrix porosity, diffusion coefficient, or retardation coefficient increase. Interestingly, model results before and after hydrofracking are insensitive to adding more fractures, while slightly more sensitive to aperture increase, making SWIW tests a possible means of discriminating between these two potential hydrofracking effects. Finally, we investigate the possibility of inferring relevant information regarding the fracture-matrix system physical parameters from the BTCs obtained during SWIW testing.« less

Data-Driven Learning of Q-Matrix

PubMed Central

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2013-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which specifies the item–attribute relationships. This article proposes a data-driven approach to identification of the Q-matrix and estimation of related model parameters. A key ingredient is a flexible T-matrix that relates the Q-matrix to response patterns. The flexibility of the T-matrix allows the construction of a natural criterion function as well as a computationally amenable algorithm. Simulations results are presented to demonstrate usefulness and applicability of the proposed method. Extension to handling of the Q-matrix with partial information is presented. The proposed method also provides a platform on which important statistical issues, such as hypothesis testing and model selection, may be formally addressed. PMID:23926363

Critical Domains of Culturally Relevant Leadership Learning: A Call to Transform Leadership Programs.

PubMed

Jones, Tamara Bertrand; Guthrie, Kathy L; Osteen, Laura

2016-12-01

This chapter introduces the critical domains of culturally relevant leadership learning. The model explores how capacity, identity, and efficacy of student leaders interact with dimensions of campus climate. © 2016 Wiley Periodicals, Inc., A Wiley Company.

Effect of tDCS on task relevant and irrelevant perceptual learning of complex objects.

PubMed

Van Meel, Chayenne; Daniels, Nicky; de Beeck, Hans Op; Baeck, Annelies

2016-01-01

During perceptual learning the visual representations in the brain are altered, but these changes' causal role has not yet been fully characterized. We used transcranial direct current stimulation (tDCS) to investigate the role of higher visual regions in lateral occipital cortex (LO) in perceptual learning with complex objects. We also investigated whether object learning is dependent on the relevance of the objects for the learning task. Participants were trained in two tasks: object recognition using a backward masking paradigm and an orientation judgment task. During both tasks, an object with a red line on top of it were presented in each trial. The crucial difference between both tasks was the relevance of the object: the object was relevant for the object recognition task, but not for the orientation judgment task. During training, half of the participants received anodal tDCS stimulation targeted at the lateral occipital cortex (LO). Afterwards, participants were tested on how well they recognized the trained objects, the irrelevant objects presented during the orientation judgment task and a set of completely new objects. Participants stimulated with tDCS during training showed larger improvements of performance compared to participants in the sham condition. No learning effect was found for the objects presented during the orientation judgment task. To conclude, this study suggests a causal role of LO in relevant object learning, but given the rather low spatial resolution of tDCS, more research on the specificity of this effect is needed. Further, mere exposure is not sufficient to train object recognition in our paradigm.

Ask Not Only "What Can Problem-Based Learning Do for Psychology?" but "What Can Psychology Do for Problem-Based Learning?" A Review of the Relevance of Problem-Based Learning for Psychology Teaching and Research

ERIC Educational Resources Information Center

Wiggins, Sally; Chiriac, Eva Hammar; Abbad, Gunvor Larsson; Pauli, Regina; Worrell, Marcia

2016-01-01

Problem-based learning (PBL) is an internationally recognised pedagogical approach that is implemented within a number of disciplines. The relevance and uptake of PBL in psychology has to date, however, received very limited attention. The aim of this paper is therefore to review published accounts of how PBL is being used to deliver psychology…

Mean deformation metrics for quantifying 3D cell–matrix interactions without requiring information about matrix material properties

PubMed Central

Stout, David A.; Bar-Kochba, Eyal; Estrada, Jonathan B.; Toyjanova, Jennet; Kesari, Haneesh; Reichner, Jonathan S.; Franck, Christian

2016-01-01

Mechanobiology relates cellular processes to mechanical signals, such as determining the effect of variations in matrix stiffness with cell tractions. Cell traction recorded via traction force microscopy (TFM) commonly takes place on materials such as polyacrylamide- and polyethylene glycol-based gels. Such experiments remain limited in physiological relevance because cells natively migrate within complex tissue microenvironments that are spatially heterogeneous and hierarchical. Yet, TFM requires determination of the matrix constitutive law (stress–strain relationship), which is not always readily available. In addition, the currently achievable displacement resolution limits the accuracy of TFM for relatively small cells. To overcome these limitations, and increase the physiological relevance of in vitro experimental design, we present a new approach and a set of associated biomechanical signatures that are based purely on measurements of the matrix's displacements without requiring any knowledge of its constitutive laws. We show that our mean deformation metrics (MDM) approach can provide significant biophysical information without the need to explicitly determine cell tractions. In the process of demonstrating the use of our MDM approach, we succeeded in expanding the capability of our displacement measurement technique such that it can now measure the 3D deformations around relatively small cells (∼10 micrometers), such as neutrophils. Furthermore, we also report previously unseen deformation patterns generated by motile neutrophils in 3D collagen gels. PMID:26929377

Bioengineering Human Myocardium on Native Extracellular Matrix

PubMed Central

Guyette, Jacques P.; Charest, Jonathan M; Mills, Robert W; Jank, Bernhard J.; Moser, Philipp T.; Gilpin, Sarah E.; Gershlak, Joshua R.; Okamoto, Tatsuya; Gonzalez, Gabriel; Milan, David J.; Gaudette, Glenn R.; Ott, Harald C.

2015-01-01

Rationale More than 25 million individuals suffer from heart failure worldwide, with nearly 4,000 patients currently awaiting heart transplantation in the United States. Donor organ shortage and allograft rejection remain major limitations with only about 2,500 hearts transplanted each year. As a theoretical alternative to allotransplantation, patient-derived bioartificial myocardium could provide functional support and ultimately impact the treatment of heart failure. Objective The objective of this study is to translate previous work to human scale and clinically relevant cells, for the bioengineering of functional myocardial tissue based on the combination of human cardiac matrix and human iPS-derived cardiac myocytes. Methods and Results To provide a clinically relevant tissue scaffold, we translated perfusion-decellularization to human scale and obtained biocompatible human acellular cardiac scaffolds with preserved extracellular matrix composition, architecture, and perfusable coronary vasculature. We then repopulated this native human cardiac matrix with cardiac myocytes derived from non-transgenic human induced pluripotent stem cells (iPSCs) and generated tissues of increasing three-dimensional complexity. We maintained such cardiac tissue constructs in culture for 120 days to demonstrate definitive sarcomeric structure, cell and matrix deformation, contractile force, and electrical conduction. To show that functional myocardial tissue of human scale can be built on this platform, we then partially recellularized human whole heart scaffolds with human iPSC-derived cardiac myocytes. Under biomimetic culture, the seeded constructs developed force-generating human myocardial tissue, showed electrical conductivity, left ventricular pressure development, and metabolic function. Conclusions Native cardiac extracellular matrix scaffolds maintain matrix components and structure to support the seeding and engraftment of human iPS-derived cardiac myocytes, and enable the bioengineering of functional human myocardial-like tissue of multiple complexities. PMID:26503464

Prepared stimuli enhance aversive learning without weakening the impact of verbal instructions

PubMed Central

2018-01-01

Fear-relevant stimuli such as snakes and spiders are thought to capture attention due to evolutionary significance. Classical conditioning experiments indicate that these stimuli accelerate learning, while instructed extinction experiments suggest they may be less responsive to instructions. We manipulated stimulus type during instructed aversive reversal learning and used quantitative modeling to simultaneously test both hypotheses. Skin conductance reversed immediately upon instruction in both groups. However, fear-relevant stimuli enhanced dynamic learning, as measured by higher learning rates in participants conditioned with images of snakes and spiders. Results are consistent with findings that dissociable neural pathways underlie feedback-driven and instructed aversive learning. PMID:29339561

Fear conditioning to subliminal fear relevant and non fear relevant stimuli.

PubMed

Lipp, Ottmar V; Kempnich, Clare; Jee, Sang Hoon; Arnold, Derek H

2014-01-01

A growing body of evidence suggests that conscious visual awareness is not a prerequisite for human fear learning. For instance, humans can learn to be fearful of subliminal fear relevant images--images depicting stimuli thought to have been fear relevant in our evolutionary context, such as snakes, spiders, and angry human faces. Such stimuli could have a privileged status in relation to manipulations used to suppress usually salient images from awareness, possibly due to the existence of a designated sub-cortical 'fear module'. Here we assess this proposition, and find it wanting. We use binocular masking to suppress awareness of images of snakes and wallabies (particularly cute, non-threatening marsupials). We find that subliminal presentations of both classes of image can induce differential fear conditioning. These data show that learning, as indexed by fear conditioning, is neither contingent on conscious visual awareness nor on subliminal conditional stimuli being fear relevant.

Perceptual learning: toward a comprehensive theory.

PubMed

Watanabe, Takeo; Sasaki, Yuka

2015-01-03

Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. At least two serious problems exist. First, there is the controversy over which stage of information processing is changed in association with task-relevant VPL. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model in which feature-based plasticity is a change in a representation of the learned feature, and task-based plasticity is a change in processing of the trained task. Although the two types of plasticity underlie task-relevant VPL, only feature-based plasticity underlies task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

Pre-Service Teachers' Mental Constructions of Concepts in Matrix Algebra

ERIC Educational Resources Information Center

Ndlovu, Zanele; Brijlall, Deonarain

2015-01-01

This study is part of ongoing research in undergraduate mathematics education. The study was guided by the belief that understanding the mental constructions the pre-service teachers make when learning matrix algebra concepts leads to improved instructional methods. In this preliminary study the data was collected from 85 pre-service teachers…

Developing defined substrates for stem cell culture and differentiation.

PubMed

Hagbard, Louise; Cameron, Katherine; August, Paul; Penton, Christopher; Parmar, Malin; Hay, David C; Kallur, Therése

2018-07-05

Over the past few decades, a variety of different reagents for stem cell maintenance and differentiation have been commercialized. These reagents share a common goal in facilitating the manufacture of products suitable for cell therapy while reducing the amount of non-defined components. Lessons from developmental biology have identified signalling molecules that can guide the differentiation process in vitro , but less attention has been paid to the extracellular matrix used. With the introduction of more biologically relevant and defined matrices, that better mimic specific cell niches, researchers now have powerful resources to fine-tune their in vitro differentiation systems, which may allow the manufacture of therapeutically relevant cell types. In this review article, we revisit the basics of the extracellular matrix, and explore the important role of the cell-matrix interaction. We focus on laminin proteins because they help to maintain pluripotency and drive cell fate specification.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'. © 2018 The Authors.

Integration of Andragogy into Preceptorship

ERIC Educational Resources Information Center

Leigh, Katherine; Whitted, Kelli; Hamilton, Bernita

2015-01-01

Return of registered nurses to school dictates that mobility programs integrate principles and design elements of adult learning theory. The Decisional Matrix for Preceptorship Experiences (DMPE) was designed to support mutual needs assessment and identification of individualized clinical learning activities. Using the Andragogy in Practice…

The pro children intervention: applying the intervention mapping protocol to develop a school-based fruit and vegetable promotion programme.

PubMed

Pérez-Rodrigo, Carmen; Wind, Marianne; Hildonen, Christina; Bjelland, Mona; Aranceta, Javier; Klepp, Knut-Inge; Brug, Johannes

2005-01-01

The importance of careful theory-based intervention planning is recognized for fruit and vegetable promotion. This paper describes the application of the Intervention Mapping (IM) protocol to develop the Pro Children intervention to promote consumption of fruit and vegetable among 10- to 13-year-old schoolchildren. Based on a needs assessment, promotion of intake of fruit and vegetable was split into performance objectives and related personal, social and environmental determinants. Crossing the performance objectives with related important and changeable determinants resulted in a matrix of learning and change objectives for which appropriate educational strategies were identified. Theoretically similar but culturally relevant interventions were designed, implemented and evaluated in Norway, the Netherlands and Spain during 2 school years. Programme activities included provision of fruits and vegetables in the schools, guided classroom activities, computer-tailored feedback and advice for children, and activities to be completed at home with the family. Additionally, optional intervention components for community reinforcement included incorporation of mass media, school health services or grocery stores. School project committees were supported. The Pro Children intervention was carefully developed based on the IM protocol that resulted in a comprehensive school-based fruit and vegetable promotion programme, but culturally sensible and locally relevant. (c) 2005 S. Karger AG, Basel

Which Features Make Illustrations in Multimedia Learning Interesting?

ERIC Educational Resources Information Center

Magner, Ulrike Irmgard Elisabeth; Glogger, Inga; Renkl, Alexander

2016-01-01

How can illustrations motivate learners in multimedia learning? Which features make illustrations interesting? Beside the theoretical relevance of addressing these questions, these issues are practically relevant when instructional designers are to decide which features of illustrations can trigger situational interest irrespective of individual…

Assessment of Relevant Learning Processes.

ERIC Educational Resources Information Center

Kim, JinGyu

Criteria for relevant classroom assessments are discussed, and a biofunctional model of learning assessment is presented. In classroom assessment, the following criteria must be considered: (1) assessment approach (process-oriented and outcome-oriented); (2) assessment context (knowledge and higher-order thinking skills); (3) assessment method…

Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.

PubMed

Khan, Adil G; Poort, Jasper; Chadwick, Angus; Blot, Antonin; Sahani, Maneesh; Mrsic-Flogel, Thomas D; Hofer, Sonja B

2018-06-01

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

PubMed

Zhang, Xiang; Guan, Naiyang; Jia, Zhilong; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

The information architecture of behavior change websites.

PubMed

Danaher, Brian G; McKay, H Garth; Seeley, John R

2005-05-18

The extraordinary growth in Internet use offers researchers important new opportunities to identify and test new ways to deliver effective behavior change programs. The information architecture (IA)-the structure of website information--is an important but often overlooked factor to consider when adapting behavioral strategies developed in office-based settings for Web delivery. Using examples and relevant perspectives from multiple disciplines, we describe a continuum of website IA designs ranging from a matrix design to the tunnel design. The free-form matrix IA design allows users free rein to use multiple hyperlinks to explore available content according to their idiosyncratic interests. The more directive tunnel IA design (commonly used in e-learning courses) guides users step-by-step through a series of Web pages that are arranged in a particular order to improve the chances of achieving a goal that is measurable and consistent. Other IA designs are also discussed, including hierarchical IA and hybrid IA designs. In the hierarchical IA design, program content is arranged in a top-down manner, which helps the user find content of interest. The more complex hybrid IA design incorporates some combination of components that use matrix, tunnel, and/or hierarchical IA designs. Each of these IA designs is discussed in terms of usability, participant engagement, and program tailoring, as well as how they might best be matched with different behavior change goals (using Web-based smoking cessation interventions as examples). Our presentation underscores the role of considering and clearly reporting the use of IA designs when creating effective Web-based interventions. We also encourage the adoption of a multidisciplinary perspective as we move towards a more mature view of Internet intervention research.

Impact of a novel teaching method based on feedback, activity, individuality and relevance on students' learning.

PubMed

Edafe, Ovie; Brooks, William S; Laskar, Simone N; Benjamin, Miles W; Chan, Philip

2016-03-20

This study examines the perceived impact of a novel clinical teaching method based on FAIR principles (feedback, activity, individuality and relevance) on students' learning on clinical placement. This was a qualitative research study. Participants were third year and final year medical students attached to one UK vascular firm over a four-year period (N=108). Students were asked to write a reflective essay on how FAIRness approach differs from previous clinical placement, and its advantages and disadvantages. Essays were thematically analysed and globally rated (positive, negative or neutral) by two independent researchers. Over 90% of essays reported positive experiences of feedback, activity, individuality and relevance model. The model provided multifaceted feedback; active participation; longitudinal improvement; relevance to stage of learning and future goals; structured teaching; professional development; safe learning environment; consultant involvement in teaching. Students perceived preparation for tutorials to be time intensive for tutors/students; a lack of teaching on medical sciences and direct observation of performance; more than once weekly sessions would be beneficial; some issues with peer and public feedback, relevance to upcoming exam and large group sizes. Students described negative experiences of "standard" clinical teaching. Progressive teaching programmes based on the FAIRness principles, feedback, activity, individuality and relevance, could be used as a model to improve current undergraduate clinical teaching.

Stimulus fear relevance and the speed, magnitude, and robustness of vicariously learned fear.

PubMed

Dunne, Güler; Reynolds, Gemma; Askew, Chris

2017-08-01

Superior learning for fear-relevant stimuli is typically indicated in the laboratory by faster acquisition of fear responses, greater learned fear, and enhanced resistance to extinction. Three experiments investigated the speed, magnitude, and robustness of UK children's (6-10 years; N = 290; 122 boys, 168 girls) vicariously learned fear responses for three types of stimuli. In two experiments, children were presented with pictures of novel animals (Australian marsupials) and flowers (fear-irrelevant stimuli) alone (control) or together with faces expressing fear or happiness. To determine learning speed the number of stimulus-face pairings seen by children was varied (1, 10, or 30 trials). Robustness of learning was examined via repeated extinction procedures over 3 weeks. A third experiment compared the magnitude and robustness of vicarious fear learning for snakes and marsupials. Significant increases in fear responses were found for snakes, marsupials and flowers. There was no indication that vicarious learning for marsupials was faster than for flowers. Moreover, vicariously learned fear was neither greater nor more robust for snakes compared to marsupials, or for marsupials compared to flowers. These findings suggest that for this age group stimulus fear relevance may have little influence on vicarious fear learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Matrix of Key Federal Statutes and Federal and State Court Decisions Reflecting the Core Concepts of Disability Policy

ERIC Educational Resources Information Center

Turnbull, H. Rutherford, III; Stowe, Matt; Klein, Samara; Riffel, Brandon

2012-01-01

This matrix displays the decisions of the United States Supreme Court and the federal statutes most relevant to individuals with disabilities and their families. It is organized according to the core concepts of disability policy as identified by Rud Turnbull and his colleagues at the Beach Center on Disability, the University of Kansas, Lawrence,…

Treatment of gun-shot defect of the foot with bovine collagen matrix application.

PubMed

Coban, Yusuf Kenan; Kalender, Ali Murat

2009-12-01

Nonoperative therapy might be chosen for patients with small wounds or defects around the foot and ankle region. Lyophilized bovine collagen matrix is one of ideal biological dressings used in wound treatment. We present an example of type 1 bovine collagen (Gelfix, Euroresearch, Inc., Milano, Italy) usage in a complex gun-shot wound of the foot and relevant literature is discussed.

The Effect of Aqueous Alteration in Antarctic Carbonaceous Chondrites from Comparative ICP-MS Bulk Chemistry

NASA Technical Reports Server (NTRS)

Alonso-Azcarate, J.; Trigo-Rodriguez, J. M.; Moyano-Cambero, C. E.; Zolensky, M.

2014-01-01

Terrestrial ages of Antarctic carbonaceous chondrites (CC) indicate that these meteorites have been preserved in or on ice for, at least, tens of thousands of years. Due to the porous structure of these chondrites formed by the aggregation of silicate-rich chondrules, refractory inclusions, metal grains, and fine-grained matrix materials, the effect of pervasive terrestrial water is relevant. Our community defends that pristine CC matrices are representing samples of scarcely processed protoplanetary disk materials as they contain stellar grains, but they might also trace parent body processes. It is important to study the effects of terrestrial aqueous alteration in promoting bulk chemistry changes, and creating distinctive alteration minerals. Particularly because it is thought that aqueous alteration has particularly played a key role in some CC groups in modifying primordial bulk chemistry, and homogenizing the isotopic content of fine-grained matrix materials. Fortunately, the mineralogy produced by parent-body and terrestrial aqueous alteration processes is distinctive. With the goal to learn more about terrestrial alteration in Antarctica we are obtaining reflectance spectra of CCs, but also performing ICP-MS bulk chemistry of the different CC groups. A direct comparison with the mean bulk elemental composition of recovered falls might inform us on the effects of terrestrial alteration in finds. With such a goal, in the current work we have analyzed some members representative of CO and CM chondrite groups.

Motivation in Second Language Learning: A Historical Overview and Its Relevance in a Public High School in Pasto, Colombia

ERIC Educational Resources Information Center

Guerrero, Mario

2015-01-01

Motivation has a significant role in the process of language learning. It is important to understand its theoretical evolution in this field to be able to consider its relevance in the learning and teaching of a foreign language. Motivation is a term that is commonly used among language teachers and language learners but perhaps many are not aware…

Research on Infancy of Special Relevance for Mental Health. Matrix No. 11A.

ERIC Educational Resources Information Center

Provence, Sally

Research relevant to planning and practice in the area of infant mental health is discussed in this paper. First, three examples of research approaches that reflect current attitudes are given. The first example represents those studies in which there is an effort to closely coordinate physiological and behavioral studies. The second example…

Relevance and Rigor in International Business Teaching: Using the CSA-FSA Matrix

ERIC Educational Resources Information Center

Collinson, Simon C.; Rugman, Alan M.

2011-01-01

We advance three propositions in this paper. First, teaching international business (IB) at any level needs to be theoretically driven, using mainstream frameworks to organize thinking. Second, these frameworks need to be made relevant to the experiences of the students; for example, by using them in case studies. Third, these parameters of rigor…

Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling.

PubMed

Khan, Zulfiqar Hasan; Gu, Irene Yu-Hua

2013-12-01

This paper proposes a novel Bayesian online learning and tracking scheme for video objects on Grassmann manifolds. Although manifold visual object tracking is promising, large and fast nonplanar (or out-of-plane) pose changes and long-term partial occlusions of deformable objects in video remain a challenge that limits the tracking performance. The proposed method tackles these problems with the main novelties on: 1) online estimation of object appearances on Grassmann manifolds; 2) optimal criterion-based occlusion handling for online updating of object appearances; 3) a nonlinear dynamic model for both the appearance basis matrix and its velocity; and 4) Bayesian formulations, separately for the tracking process and the online learning process, that are realized by employing two particle filters: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in an alternating fashion to mitigate the tracking drift. Experiments using the proposed tracker on videos captured by a single dynamic/static camera have shown robust tracking performance, particularly for scenarios when target objects contain significant nonplanar pose changes and long-term partial occlusions. Comparisons with eight existing state-of-the-art/most relevant manifold/nonmanifold trackers with evaluations have provided further support to the proposed scheme.

Medical teachers' perception of professional roles in the framework of the German National Competence-Based Learning Objectives for Undergraduate Medical Education (NKLM)-A multicenter study.

PubMed

Griewatz, Jan; Wiechers, Steffen; Ben-Karacobanim, Hadiye; Lammerding-Koeppel, Maria

2016-11-01

Based on CanMEDS and others, the German National Competence-Based Learning Objectives for Undergraduate Medical Education (NKLM) were recently consented. International studies recommend integrating national and cultural context when transferring a professional roles framework in different countries. Teachers' misconceptions may establish barriers in role understanding and implementation. The aim is to analyze medical teachers' rating and perception of NKLM roles in order to reveal differences to official definitions. A two-step sequential mixed methods design was used including a survey and focus groups with N = 80 medical teachers from four German universities. Most of the teachers highly valued the importance of the role "Medical Expert" and understood comprehensively. The Communicator and the Collaborator were rated fairly and perceived to a large extent. Other intrinsic roles like Health Advocate and Scholar showed more deficits in perception and less importance by the participants. This was seen generally problematic and should be considered carefully. Manager and professional showed one-sided weaknesses either in importance or perception. Medical teachers considered NKLM roles relevant for medical practice, although their role perception differed considerably. The value and risk matrix visualizes the specific role profile and offers strategic implications for NKLM communication and handling, thus supporting change management.

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys

NASA Astrophysics Data System (ADS)

Pham, Tien-Lam; Nguyen, Nguyen-Duong; Nguyen, Van-Doan; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

2018-05-01

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

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

PubMed Central

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

2014-01-01

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

Inter-individual differences in audio-motor learning of piano melodies and white matter fiber tract architecture.

PubMed

Engel, Annerose; Hijmans, Brenda S; Cerliani, Leonardo; Bangert, Marc; Nanetti, Luca; Keller, Peter E; Keysers, Christian

2014-05-01

Humans vary substantially in their ability to learn new motor skills. Here, we examined inter-individual differences in learning to play the piano, with the goal of identifying relations to structural properties of white matter fiber tracts relevant to audio-motor learning. Non-musicians (n = 18) learned to perform three short melodies on a piano keyboard in a pure audio-motor training condition (vision of their own fingers was occluded). Initial learning times ranged from 17 to 120 min (mean ± SD: 62 ± 29 min). Diffusion-weighted magnetic resonance imaging was used to derive the fractional anisotropy (FA), an index of white matter microstructural arrangement. A correlation analysis revealed that higher FA values were associated with faster learning of piano melodies. These effects were observed in the bilateral corticospinal tracts, bundles of axons relevant for the execution of voluntary movements, and the right superior longitudinal fasciculus, a tract important for audio-motor transformations. These results suggest that the speed with which novel complex audio-motor skills can be acquired may be determined by variability in structural properties of white matter fiber tracts connecting brain areas functionally relevant for audio-motor learning. Copyright © 2013 Wiley Periodicals, Inc.

Aiming to Complete the Matrix: Eye-Movement Analysis of Processing Strategies in Children's Relational Thinking

ERIC Educational Resources Information Center

Chen, Zhe; Honomichl, Ryan; Kennedy, Diane; Tan, Enda

2016-01-01

The present study examines 5- to 8-year-old children's relation reasoning in solving matrix completion tasks. This study incorporates a componential analysis, an eye-tracking method, and a microgenetic approach, which together allow an investigation of the cognitive processing strategies involved in the development and learning of children's…

The Extracellular Protease Matrix Metalloproteinase-9 Is Activated by Inhibitory Avoidance Learning and Required for Long-Term Memory

ERIC Educational Resources Information Center

Nagy, Vanja; Bozdagi, Ozlem; Huntley, George W.

2007-01-01

Matrix metalloproteinases (MMPs) are a family of extracellularly acting proteolytic enzymes with well-recognized roles in plasticity and remodeling of synaptic circuits during brain development and following brain injury. However, it is now becoming increasingly apparent that MMPs also function in normal, nonpathological synaptic plasticity of the…

Rhetoric and the Social Matrix: Reflections from the Asian Classics.

ERIC Educational Resources Information Center

Oliver, Robert T.

As humanity seeks a new social matrix which is suited to the global conditions that have ended the isolation of communities, we must learn to understand the various rhetorics of different cultures. This paper explores at length some of the richness of rhetorical theory within the classics of the East, including the "Upanishads," and in the ideas…

An Implementation Model for Integrated Learning Systems.

ERIC Educational Resources Information Center

Mills, Steven C.; Ragan, Tillman R.

This paper describes the development, validation, and research application of the Computer-Delivered Instruction Configuration Matrix (CDICM), an instrument for evaluating the implementation of Integrated Learning Systems (ILS). The CDICM consists of a 15-item checklist, describing the major components of implementation of ILS technology, to be…

Effects of Online Note Taking Formats and Self-Monitoring Prompts on Learning from Online Text: Using Technology to Enhance Self-Regulated Learning

ERIC Educational Resources Information Center

Kauffman, Douglas F.; Zhao, Ruomeng; Yang, Ya-Shu

2011-01-01

This study explored conditions under which note taking methods and self-monitoring prompts are most effective for facilitating information collection and achievement in an online learning environment. In experiment 1 30 students collected notes from a website using an online conventional, outline, or matrix note taking tool. In experiment 2 119…

Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B–induced local skin hypersensitization in healthy subjects: a machine-learned analysis

PubMed Central

Lötsch, Jörn; Geisslinger, Gerd; Heinemann, Sarah; Lerch, Florian; Oertel, Bruno G.; Ultsch, Alfred

2018-01-01

Abstract The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models. PMID:28700537

Trend of E-Learning: The Service Mashup

ERIC Educational Resources Information Center

Yen, Neil Y.; Shih, Timothy K.; Jin, Qun; Hsu, Hui-Huang; Chao, Louis R.

2010-01-01

With the improvement of internet technologies and multimedia resources, traditional learning has been replaced by distance learning, web-based learning or others' e-learning learning styles. According to distance learning, there are many research organizations and companies who make efforts in developing the relevant systems. But they lack…

Comprehensive Thematic T-Matrix Reference Database: A 2014-2015 Update

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Zakharova, Nadezhda; Khlebtsov, Nikolai G.; Videen, Gorden; Wriedt, Thomas

2015-01-01

The T-matrix method is one of the most versatile and efficient direct computer solvers of the macroscopic Maxwell equations and is widely used for the computation of electromagnetic scattering by single and composite particles, discrete random media, and particles in the vicinity of an interface separating two half-spaces with different refractive indices. This paper is the seventh update to the comprehensive thematic database of peer-reviewed T-matrix publications initiated by us in 2004 and includes relevant publications that have appeared since 2013. It also lists a number of earlier publications overlooked previously.

The Role of Reader Characteristics in Processing and Learning from Informational Text

ERIC Educational Resources Information Center

Fox, Emily

2009-01-01

This article considers the role of reader characteristics in processing and learning from informational text, as revealed in think-aloud research. A theoretical framework for relevant aspects of readers' processing and products was developed. These relevant aspects included three attentional foci for processing (comprehension, monitoring, and…

Integration of Culturally Relevant Pedagogy into the Science Learning Progression Framework

ERIC Educational Resources Information Center

Bernardo, Cyntra

2017-01-01

This study integrated elements of culturally relevant pedagogy into a science learning progression framework, with the goal of enhancing teachers' cultural knowledge and thereby creating better teaching practices in an urban public high school science classroom. The study was conducted using teachers, an administrator, a science coach, and…

Creating Culturally Relevant Instructional Materials: A Swaziland Case Study

ERIC Educational Resources Information Center

Titone, Connie; Plummer, Emily C.; Kielar, Melissa A.

2012-01-01

In the field of English language learning, research proves that culturally relevant reading materials improve students' language acquisition, learning motivation, self-esteem, and identity formation. Since English is the language of instruction in many distant countries, such as Swaziland, even when English is not the native language of those…

Creativity and Learning: What Research Says to the Teacher.

ERIC Educational Resources Information Center

Hennessey, Beth A.; Amabile, Teresa M.

The pamphlet reviews research on creativity and applies it to the learning process. After discussing the definition and measurement of creativity, the components of creative performance are outlined, including domain-relevant skills, creativity-relevant skills, and intrinsic task motivation. Factors which destroy students' creativity are noted,…

Double-β decay matrix elements from lattice quantum chromodynamics

NASA Astrophysics Data System (ADS)

Tiburzi, Brian C.; Wagman, Michael L.; Winter, Frank; Chang, Emmanuel; Davoudi, Zohreh; Detmold, William; Orginos, Kostas; Savage, Martin J.; Shanahan, Phiala E.; Nplqcd Collaboration

2017-09-01

A lattice quantum chromodynamics (LQCD) calculation of the nuclear matrix element relevant to the n n →p p e e ν¯eν¯e transition is described in detail, expanding on the results presented in Ref. [P. E. Shanahan et al., Phys. Rev. Lett. 119, 062003 (2017), 10.1103/PhysRevLett.119.062003]. This matrix element, which involves two insertions of the weak axial current, is an important input for phenomenological determinations of double-β decay rates of nuclei. From this exploratory study, performed using unphysical values of the quark masses, the long-distance deuteron-pole contribution to the matrix element is separated from shorter-distance hadronic contributions. This polarizability, which is only accessible in double-weak processes, cannot be constrained from single-β decay of nuclei, and is found to be smaller than the long-distance contributions in this calculation, but non-negligible. In this work, technical aspects of the LQCD calculations, and of the relevant formalism in the pionless effective field theory, are described. Further calculations of the isotensor axial polarizability, in particular near and at the physical values of the light-quark masses, are required for precise determinations of both two-neutrino and neutrinoless double-β decay rates in heavy nuclei.

Trace detection of organic compounds in complex sample matrixes by single photon ionization ion trap mass spectrometry: real-time detection of security-relevant compounds and online analysis of the coffee-roasting process.

PubMed

Schramm, Elisabeth; Kürten, Andreas; Hölzer, Jasper; Mitschke, Stefan; Mühlberger, Fabian; Sklorz, Martin; Wieser, Jochen; Ulrich, Andreas; Pütz, Michael; Schulte-Ladbeck, Rasmus; Schultze, Rainer; Curtius, Joachim; Borrmann, Stephan; Zimmermann, Ralf

2009-06-01

An in-house-built ion trap mass spectrometer combined with a soft ionization source has been set up and tested. As ionization source, an electron beam pumped vacuum UV (VUV) excimer lamp (EBEL) was used for single-photon ionization. It was shown that soft ionization allows the reduction of fragmentation of the target analytes and the suppression of most matrix components. Therefore, the combination of photon ionization with the tandem mass spectrometry (MS/MS) capability of an ion trap yields a powerful tool for molecular ion peak detection and identification of organic trace compounds in complex matrixes. This setup was successfully tested for two different applications. The first one is the detection of security-relevant substances like explosives, narcotics, and chemical warfare agents. One test substance from each of these groups was chosen and detected successfully with single photon ionization ion trap mass spectrometry (SPI-ITMS) MS/MS measurements. Additionally, first tests were performed, demonstrating that this method is not influenced by matrix compounds. The second field of application is the detection of process gases. Here, exhaust gas from coffee roasting was analyzed in real time, and some of its compounds were identified using MS/MS studies.

Comparison of the perceived relevance of oral biology reported by students and interns of a Pakistani dental college.

PubMed

Farooq, I; Ali, S

2014-11-01

The purpose of this study was to analyse and compare the perceived relevance of oral biology with dentistry as reported by dental students and interns and to investigate the most popular teaching approach and learning resource. A questionnaire aiming to ask about the relevance of oral biology to dentistry, most popular teaching method and learning resource was utilised in this study. Study groups encompassed second-year dental students who had completed their course and dental interns. The data were obtained and analysed statistically. The overall response rate for both groups was 60%. Both groups reported high relevance of oral biology to dentistry. Perception of dental interns regarding the relevance of oral biology to dentistry was higher than that of students. Both groups identified student presentations as the most important teaching method. Amongst the most important learning resources, textbooks were considered most imperative by interns, whereas lecture handouts received the highest importance score by students. Dental students and interns considered oral biology to be relevant to dentistry, although greater relevance was reported by interns. Year-wise advancement in dental education and training improves the perception of the students about the relevance of oral biology to dentistry. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comprehensive T-Matrix Reference Database: A 2012 - 2013 Update

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Videen, Gorden; Khlebtsov, Nikolai G.; Wriedt, Thomas

2013-01-01

The T-matrix method is one of the most versatile, efficient, and accurate theoretical techniques widely used for numerically exact computer calculations of electromagnetic scattering by single and composite particles, discrete random media, and particles imbedded in complex environments. This paper presents the fifth update to the comprehensive database of peer-reviewed T-matrix publications initiated by us in 2004 and includes relevant publications that have appeared since 2012. It also lists several earlier publications not incorporated in the original database, including Peter Waterman's reports from the 1960s illustrating the history of the T-matrix approach and demonstrating that John Fikioris and Peter Waterman were the true pioneers of the multi-sphere method otherwise known as the generalized Lorenz - Mie theory.

Systematically evaluating the impact of diagnosis-related groups (DRGs) on health care delivery: a matrix of ethical implications.

PubMed

Fourie, Carina; Biller-Andorno, Nikola; Wild, Verina

2014-04-01

Swiss hospitals were required to implement a prospective payment system for reimbursement using a diagnosis-related groups (DRGs) classification system by the beginning of 2012. Reforms to a health care system should be assessed for their impact, including their impact on ethically relevant factors. Over a number of years and in a number of countries, questions have been raised in the literature about the ethical implications of the implementation of DRGs. However, despite this, researchers have not attempted to identify the major ethical issues associated with DRGs systematically. To address this gap in the literature, we have developed a matrix for identifying the ethical implications of the implementation of DRGs. It was developed using a literature review, and empirical studies on DRGs, as well as a review and analysis of existing ethics frameworks. The matrix consists of the ethically relevant parameters of health care systems on which DRGs are likely to have an impact; the ethical values underlying these parameters; and examples of specific research questions associated with DRGs to illustrate how the matrix can be applied. While the matrix has been developed in light of the Swiss health care reform, it could be used as a basis for identifying the ethical implications of DRG-based systems worldwide and for highlighting the ethical implications of other kinds of provider payment systems (PPS). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Effects of learning content in context on knowledge acquisition and recall: a pretest-posttest control group design.

PubMed

Bergman, Esther M; de Bruin, Anique B H; Vorstenbosch, Marc A T M; Kooloos, Jan G M; Puts, Ghita C W M; Leppink, Jimmie; Scherpbier, Albert J J A; van der Vleuten, Cees P M

2015-08-15

It is generally assumed that learning in context increases performance. This study investigates the relationship between the characteristics of a paper-patient context (relevance and familiarity), the mechanisms through which the cognitive dimension of context could improve learning (activation of prior knowledge, elaboration and increasing retrieval cues), and test performance. A total of 145 medical students completed a pretest of 40 questions, of which half were with a patient vignette. One week later, they studied musculoskeletal anatomy in the dissection room without a paper-patient context (control group) or with (ir)relevant-(un)familiar context (experimental groups), and completed a cognitive load scale. Following a short delay, the students completed a posttest. Surprisingly, our results show that students who studied in context did not perform better than students who studied without context. This finding may be explained by an interaction of the participants' expertise level, the nature of anatomical knowledge and students' approaches to learning. A relevant-familiar context only reduced the negative effect of learning the content in context. Our results suggest discouraging the introduction of an uncommon disease to illustrate a basic science concept. Higher self-perceived learning scores predict higher performance. Interestingly, students performed significantly better on the questions with context in both tests, possibly due to a 'framing effect'. Since studies focusing on the physical and affective dimensions of context have also failed to find a positive influence of learning in a clinically relevant context, further research seems necessary to refine our theories around the role of context in learning.

Learning Styles and Continuing Medical Education.

ERIC Educational Resources Information Center

Van Voorhees, Curtis; And Others

1988-01-01

The Gregorc Style Delineator--Word Matrix was administered to 2,060 physicians in order to gain a better understanding of their participation in continuing medical education. The study showed that 63 percent preferred the concrete sequential learning style. Different style preferences may account for some of the apparent disparity between…

Structural Identification and Comparison of Intelligent Mobile Learning Environment

ERIC Educational Resources Information Center

Upadhyay, Nitin; Agarwal, Vishnu Prakash

2007-01-01

This paper proposes a methodology using graph theory, matrix algebra and permanent function to compare different architecture (structure) design of intelligent mobile learning environment. The current work deals with the development/selection of optimum architecture (structural) model of iMLE. This can be done using the criterion as discussed in…

Languages and the Transfer of Skills: The Relevance of Language Learning for 21st Century Graduates in the World of Work. HEVOCAL Report.

ERIC Educational Resources Information Center

King, Anny, Ed.

This book provides a description and synthesis of a range of relevant practice and offers a framework for making language learning more relevant for new generations of practice. It is intended as a contribution to the debate about the purposes of language studies in higher education in the 21st century. The book is divided into five parts and 15…

Impact of a novel teaching method based on feedback, activity, individuality and relevance on students’ learning

PubMed Central

Brooks, William S.; Laskar, Simone N.; Benjamin, Miles W.; Chan, Philip

2016-01-01

Objectives This study examines the perceived impact of a novel clinical teaching method based on FAIR principles (feedback, activity, individuality and relevance) on students’ learning on clinical placement. Methods This was a qualitative research study. Participants were third year and final year medical students attached to one UK vascular firm over a four-year period (N=108). Students were asked to write a reflective essay on how FAIRness approach differs from previous clinical placement, and its advantages and disadvantages. Essays were thematically analysed and globally rated (positive, negative or neutral) by two independent researchers. Results Over 90% of essays reported positive experiences of feedback, activity, individuality and relevance model.  The model provided multifaceted feedback; active participation; longitudinal improvement; relevance to stage of learning and future goals; structured teaching; professional development; safe learning environment; consultant involvement in teaching. Students perceived preparation for tutorials to be time intensive for tutors/students; a lack of teaching on medical sciences and direct observation of performance; more than once weekly sessions would be beneficial; some issues with peer and public feedback, relevance to upcoming exam and large group sizes. Students described negative experiences of “standard” clinical teaching. Conclusions Progressive teaching programmes based on the FAIRness principles, feedback, activity, individuality and relevance, could be used as a model to improve current undergraduate clinical teaching. PMID:26995588

High engagement, high quality: A guiding framework for developing empirically informed asynchronous e-learning programs for health professional educators.

PubMed

Sinclair, Peter M; Levett-Jones, Tracey; Morris, Amanda; Carter, Ben; Bennett, Paul N; Kable, Ashley

2017-03-01

E-learning involves the transfer of skills and knowledge via technology so that learners can access meaningful and authentic educational materials. While learner engagement is important, in the context of healthcare education, pedagogy must not be sacrificed for edu-tainment style instructional design. Consequently, health professional educators need to be competent in the use of current web-based educational technologies so that learners are able to access relevant and engaging e-learning materials without restriction. The increasing popularity of asynchronous e-learning programs developed for use outside of formal education institutions has made this need more relevant. In these contexts, educators must balance design and functionality to deliver relevant, cost-effective, sustainable, and accessible programs that overcome scheduling and geographic barriers for learners. This paper presents 10 guiding design principles and their application in the development of an e-learning program for general practice nurses focused on behavior change. Consideration of these principles will assist educators to develop high quality, pedagogically sound, engaging, and interactive e-learning resources. © 2017 John Wiley & Sons Australia, Ltd.

Determination of the ionization potentials of security-relevant substances with single photon ionization mass spectrometry using synchrotron radiation.

PubMed

Schramm, E; Mühlberger, F; Mitschke, S; Reichardt, G; Schulte-Ladbeck, R; Pütz, M; Zimmermann, R

2008-02-01

Several ionization potentials (IPs) of security relevant substances were determined with single photon ionization time of flight mass spectrometry (SPI-TOFMS) using monochromatized synchrotron radiation from the "Berliner Elektronenspeicherring-Gesellschaft für Synchrotronstrahlung" (BESSY). In detail, the IPs of nine explosives and related compounds, seven narcotics and narcotics precursors, and one chemical warfare agent (CWA) precursor were determined, whereas six IPs already known from the literature were verified correctly. From seven other substances, including one CWA precursor, the IP could not be determined as the molecule ion peak could not be detected. For these substances the appearance energy (AE) of a main fragment was determined. The analyzed security-relevant substances showed IPs significantly below the IPs of common matrix compounds such as nitrogen and oxygen. Therefore, it is possible to find photon energies in between, whereby the molecules of interest can be detected with SPI in very low concentrations due to the shielding of the matrix. All determined IPs except the one of the explosive EGDN were below 10.5 eV. Hence, laser-generated 118 nm photons can be applied for detecting almost all security-relevant substances by, e.g., SPI-TOFMS.

Integrins and Integrin-Associated Proteins in the Cardiac Myocyte

PubMed Central

Ross, Robert S.

2014-01-01

Integrins are heterodimeric, transmembrane receptors that are expressed in all cells, including those in the heart. They participate in multiple critical cellular processes including adhesion, extracellular matrix organization, signaling, survival, and proliferation. Particularly relevant for a contracting muscle cell, integrins are mechanotransducers, translating mechanical to biochemical information. While it is likely that cardiovascular clinicians and scientists have highest recognition of integrins in the cardiovascular system from drugs used to inhibit platelet aggregation, the focus of this article will be on the role of integrins specifically in the cardiac myocyte. Following a general introduction to integrin biology, the manuscript will discuss important work on integrin signaling, mechanotransduction, and lessons learned about integrin function from a range of model organisms. Then we will detail work on integrin-related proteins in the myocyte, how integrins may interact with ion channels and mediate viral uptake into cells, and also play a role in stem cell biology. Finally, we will discuss directions for future study. PMID:24481847

Dependence structure of the commodity and stock markets, and relevant multi-spread strategy

NASA Astrophysics Data System (ADS)

Kim, Min Jae; Kim, Sehyun; Jo, Yong Hwan; Kim, Soo Yong

2011-10-01

Understanding the dependence structure between the commodity and stock markets is a crucial issue in constructing a portfolio. It can also help us to discover new opportunities to implement spread trading using multiple assets classified in the two different markets. This study analyzed the dependence structure of the commodity and stock markets using the random matrix theory technique and network analysis. Our results show that the stock and commodity markets must be handled as completely separated asset classes except for the oil and gold markets, so the performance enhancement of the mean-variance portfolio is significant as expected. In light of the fact that WTI 1 month futures and four oil-related stocks are strongly correlated, they were selected as basic ingredients to complement the multi-spread convergence trading strategy using a machine learning technique called the AdaBoost algorithm. The performance of this strategy for non-myopic investors, who can endure short-term loss, can be enhanced significantly on a risk measurement basis.

[Contraceptive self efficacy in male and female adolescents: validation of the French version of the Levinson scale].

PubMed

Bilodeau, A; Forget, G; Tétreault, J

1994-01-01

The social learning theory of Bandura leads us to believe that contraceptive self-efficacy supports the adoption and the maintenance of effective contraceptive behaviours during the teenage years. Levinson has developed a validated measure of this concept which consists of an 18-item scale for sexually active girls. However there are no such scales in French or for sexually active boys. The health promotion program, entitled SEXPRIMER, which aims at reducing teenage pregnancy, has incorporated the French version of the Levinson scale, the adapted boy's version and the validity studies. A 15-item scale for girls and a 14-item scale for boys with respective reliability coefficients of .78 and .71 resulted from this program. A logistic regression analysis shows the predictive value of the measures in regard to contraceptive behaviours. According to Levinson's more recent studies, results indicate that new research on the factor matrix of the scale are relevant.

Modelling dynamic fronto-parietal behaviour during minimally invasive surgery--a Markovian trip distribution approach.

PubMed

Leff, Daniel Richard; Orihuela-Espina, Felipe; Leong, Julian; Darzi, Ara; Yang, Guang-Zhong

2008-01-01

Learning to perform Minimally Invasive Surgery (MIS) requires considerable attention, concentration and spatial ability. Theoretically, this leads to activation in executive control (prefrontal) and visuospatial (parietal) centres of the brain. A novel approach is presented in this paper for analysing the flow of fronto-parietal haemodynamic behaviour and the associated variability between subjects. Serially acquired functional Near Infrared Spectroscopy (fNIRS) data from fourteen laparoscopic novices at different stages of learning is projected into a low-dimensional 'geospace', where sequentially acquired data is mapped to different locations. A trip distribution matrix based on consecutive directed trips between locations in the geospace reveals confluent fronto-parietal haemodynamic changes and a gravity model is applied to populate this matrix. To model global convergence in haemodynamic behaviour, a Markov chain is constructed and by comparing sequential haemodynamic distributions to the Markov's stationary distribution, inter-subject variability in learning an MIS task can be identified.

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H

2016-12-01

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

NASA Astrophysics Data System (ADS)

Morales, Francisco J.; Reyes, Antonio; Cáceres, Noelia; Romero, Luis M.; Benitez, Francisco G.; Morgado, Joao; Duarte, Emanuel; Martins, Teresa

2017-09-01

A large percentage of transport infrastructures are composed of linear assets, such as roads and rail tracks. The large social and economic relevance of these constructions force the stakeholders to ensure a prolonged health/durability. Even though, inevitable malfunctioning, breaking down, and out-of-service periods arise randomly during the life cycle of the infrastructure. Predictive maintenance techniques tend to diminish the appearance of unpredicted failures and the execution of needed corrective interventions, envisaging the adequate interventions to be conducted before failures show up. This communication presents: i) A procedural approach, to be conducted, in order to collect the relevant information regarding the evolving state condition of the assets involved in all maintenance interventions; this reported and stored information constitutes a rich historical data base to train Machine Learning algorithms in order to generate reliable predictions of the interventions to be carried out in further time scenarios. ii) A schematic flow chart of the automatic learning procedure. iii) Self-learning rules from automatic learning from false positive/negatives. The description, testing, automatic learning approach and the outcomes of a pilot case are presented; finally some conclusions are outlined regarding the methodology proposed for improving the self-learning predictive capability.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

PubMed

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

A Principle-Attribute Matrix for Environmentally Sustainable Management Education and Its Application: The Case for Change-Oriented Service-Learning Projects

ERIC Educational Resources Information Center

Rands, Gordon P.

2009-01-01

The environmental threats humanity faces have led businesses to increasingly commit to improve their environmental performance and to increasing attempts to address environmental issues in management education. This article presents a matrix of (a) principles that can underlie and (b) attributes that can be generated by environmentally focused…

Grape seed extracts inhibit dentin matrix degradation by MMP-3

PubMed Central

Khaddam, Mayssam; Salmon, Benjamin; Le Denmat, Dominique; Tjaderhane, Leo; Menashi, Suzanne; Chaussain, Catherine; Rochefort, Gaël Y.; Boukpessi, Tchilalo

2014-01-01

Since Matrix metalloproteinases (MMPs) have been suggested to contribute to dentin caries progression, the hypothesis that MMP inhibition would affect the progression of dentin caries is clinically relevant. Grape seed extracts (GSE) have been previously reported to be natural inhibitors of MMPs. Objective: To evaluate the capacity of a GSE mouthrinse to prevent the degradation of demineralized dentin matrix by MMP-3 (stromelysin-1). Materials and Methods: Standardized blocks of dentin obtained from sound permanent teeth extracted for orthodontic reasons were demineralized with Ethylenediaminetetraacetic acid (EDTA) and pretreated either with (A) GSE (0.2% w/v), (B) amine fluoride (AmF) (20% w/v), (C) a mouthrinse which contains both, (D) placebo, (E) sodium fluoride (0.15 mg.ml−1), (F) PBS, (G) Chlorhexidine digluconate (CHX), or (H) zinc chloride (ZnCl2). The dentin blocks were then incubated with activated recombinant MMP-3. The supernatants were analyzed by Western Blot for several dentin matrix proteins known to be MMP-3 substrate. In parallel, scanning electron microscopy (SEM) was performed on resin replica of the dentin blocks. Results: Western blot analysis of the supernatants revealed that MMP-3 released from the dentin matrix small proteoglycans (decorin and biglycan) and dentin sialoprotein (DSP) in the AmF, sodium fluoride, PBS and placebo pretreated groups, but not in the GSE and mouthrinse pretreated groups. SEM examination of resin replica showed that the mouthrinse and its active components not only had an anti-MMP action but also modified the dentin surface accessibility. Conclusion: This study shows that GSE either alone or combined with AmF as in the evaluated mouthrinse limits dentin matrix degradation. This association may be promising to prevent the progression of caries within dentin. However, the procedure should be adapted to clinically relevant durations. PMID:25400590

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Deep learning and non-negative matrix factorization in recognition of mammograms

NASA Astrophysics Data System (ADS)

Swiderski, Bartosz; Kurek, Jaroslaw; Osowski, Stanislaw; Kruk, Michal; Barhoumi, Walid

2017-02-01

This paper presents novel approach to the recognition of mammograms. The analyzed mammograms represent the normal and breast cancer (benign and malignant) cases. The solution applies the deep learning technique in image recognition. To obtain increased accuracy of classification the nonnegative matrix factorization and statistical self-similarity of images are applied. The images reconstructed by using these two approaches enrich the data base and thanks to this improve of quality measures of mammogram recognition (increase of accuracy, sensitivity and specificity). The results of numerical experiments performed on large DDSM data base containing more than 10000 mammograms have confirmed good accuracy of class recognition, exceeding the best results reported in the actual publications for this data base.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385

Cultural Speak: Culturally Relevant Pedagogy and Experiential Learning in a Public Speaking Classroom

ERIC Educational Resources Information Center

Colvin, Janet; Tobler, Nancy

2013-01-01

This study describes the efficacy of modifications made to a higher education Latina/o public speaking course to enhance student growth and understanding. The changes included the addition of a service-learning component and the incorporation of culturally relevant pedagogy. Selected research, particularly related to college students, on…

The Potential Relevance of Cognitive Neuroscience for the Development and Use of Technology-Enhanced Learning

ERIC Educational Resources Information Center

Howard-Jones, Paul; Ott, Michela; van Leeuwen, Theo; De Smedt, Bert

2015-01-01

There is increasing interest in the application of cognitive neuroscience in educational thinking and practice, and here we review findings from neuroscience that demonstrate its potential relevance to technology-enhanced learning (TEL). First, we identify some of the issues in integrating neuroscientific concepts into TEL research. We caution…

Accommodating Learning Styles: Relevance and Good Practice in Vocational Education and Training--Supporting Documents

ERIC Educational Resources Information Center

Smith, Peter; Dalton, Jennifer; Henry, John

2005-01-01

This document was produced by the author(s) based on their research for the Australian report, "Accommodating Learning Styles: Relevance and Good Practice in Vocational Education and Training," and contains three parts. Part 1, Research Methodology and Findings (Peter Smith and Jennifer Dalton), contains: (1) Research Questions; (2)…

Slower Reacquisition after Partial Extinction in Human Contingency Learning

ERIC Educational Resources Information Center

Morís, Joaquín; Barberia, Itxaso; Vadillo, Miguel A.; Andrades, Ainhoa; López, Francisco J.

2017-01-01

Extinction is a very relevant learning phenomenon from a theoretical and applied point of view. One of its most relevant features is that relapse phenomena often take place once the extinction training has been completed. Accordingly, as extinction-based therapies constitute the most widespread empirically validated treatment of anxiety disorders,…

Processing Fluency in Education: How Metacognitive Feelings Shape Learning, Belief Formation, and Affect

ERIC Educational Resources Information Center

Reber, Rolf; Greifeneder, Rainer

2017-01-01

Processing fluency--the experienced ease with which a mental operation is performed--has attracted little attention in educational psychology, despite its relevance. The present article reviews and integrates empirical evidence on processing fluency that is relevant to school education. Fluency is important, for instance, in learning,…

Logical-Semantic Visual Navigators--Aids for Teachers

ERIC Educational Resources Information Center

Khuziakhmetov, Anvar N.; Steinberg, Valery E.

2016-01-01

The relevance of the article in question is that the success of pupils' learning depends not only on the aptitude of pupils, but also on the teaching technique, including universal educational technique of various complexities. This problem is relevant on all levels of learning--from nursery schools up to higher education, including refresher…

Adult Learning Principles and Presentation Pearls

PubMed Central

Palis, Ana G.; Quiros, Peter A.

2014-01-01

Although lectures are one of the most common methods of knowledge transfer in medicine, their effectiveness has been questioned. Passive formats, lack of relevance and disconnection from the student's needs are some of the arguments supporting this apparent lack of efficacy. However, many authors have suggested that applying adult learning principles (i.e., relevance, congruence with student's needs, interactivity, connection to student's previous knowledge and experience) to this method increases learning by lectures and the effectiveness of lectures. This paper presents recommendations for applying adult learning principles during planning, creation and development of lectures to make them more effective. PMID:24791101

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

NASA Astrophysics Data System (ADS)

Li, Zhifu; Hu, Yueming; Li, Di

2016-08-01

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

Phase I Contaminant Transport Parameters for the Groundwater Flow and Contaminant Transport Model of Corrective Action Unit 97: Yucca Flat/Climax Mine, Nevada Test Site, Nye County, Nevada, Revision 0

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

John McCord

2007-09-01

This report documents transport data and data analyses for Yucca Flat/Climax Mine CAU 97. The purpose of the data compilation and related analyses is to provide the primary reference to support parameterization of the Yucca Flat/Climax Mine CAU transport model. Specific task objectives were as follows: • Identify and compile currently available transport parameter data and supporting information that may be relevant to the Yucca Flat/Climax Mine CAU. • Assess the level of quality of the data and associated documentation. • Analyze the data to derive expected values and estimates of the associated uncertainty and variability. The scope of thismore » document includes the compilation and assessment of data and information relevant to transport parameters for the Yucca Flat/Climax Mine CAU subsurface within the context of unclassified source-term contamination. Data types of interest include mineralogy, aqueous chemistry, matrix and effective porosity, dispersivity, matrix diffusion, matrix and fracture sorption, and colloid-facilitated transport parameters.« less

Contribution of Content Knowledge and Learning Ability to the Learning of Facts.

ERIC Educational Resources Information Center

Kuhara-Kojima, Keiko; Hatano, Giyoo

1991-01-01

In 3 experiments, 1,598 Japanese college students were examined concerning the learning of facts in 2 content domains, baseball and music. Content knowledge facilitated fact learning only in the relevant domain; learning ability facilitated fact learning in both domains. Effects of content knowledge and learning ability were additive. (SLD)

Texture zeros and hierarchical masses from flavour (mis)alignment

NASA Astrophysics Data System (ADS)

Hollik, W. G.; Saldana-Salazar, U. J.

2018-03-01

We introduce an unconventional interpretation of the fermion mass matrix elements. As the full rotational freedom of the gauge-kinetic terms renders a set of infinite bases called weak bases, basis-dependent structures as mass matrices are unphysical. Matrix invariants, on the other hand, provide a set of basis-independent objects which are of more relevance. We employ one of these invariants to give a new parametrisation of the mass matrices. By virtue of it, one gains control over its implicit implications on several mass matrix structures. The key element is the trace invariant which resembles the equation of a hypersphere with a radius equal to the Frobenius norm of the mass matrix. With the concepts of alignment or misalignment we can identify texture zeros with certain alignments whereas Froggatt-Nielsen structures in the matrix elements are governed by misalignment. This method allows further insights of traditional approaches to the underlying flavour geometry.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PubMed

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning.

PubMed

Vollmer, Anna-Lisa; Mühlig, Manuel; Steil, Jochen J; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina J; Wrede, Britta

2014-01-01

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.

Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning

PubMed Central

Vollmer, Anna-Lisa; Mühlig, Manuel; Steil, Jochen J.; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina J.; Wrede, Britta

2014-01-01

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction. PMID:24646510

Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

PubMed

Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

2018-02-01

In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Coherent nonlinear optical studies of elementary processes in biological complexes: diagrammatic techniques based on the wave function versus the density matrix

PubMed Central

Biggs, Jason D.; Voll, Judith A.; Mukamel, Shaul

2012-01-01

Two types of diagrammatic approaches for the design and simulation of nonlinear optical experiments (closed-time path loops based on the wave function and double-sided Feynman diagrams for the density matrix) are presented and compared. We give guidelines for the assignment of relevant pathways and provide rules for the interpretation of existing nonlinear experiments in carotenoids. PMID:22753822

Exploring emerging learning needs: a UK-wide consultation on environmental sustainability learning objectives for medical education.

PubMed

Walpole, Sarah C; Mortimer, Frances; Inman, Alice; Braithwaite, Isobel; Thompson, Trevor

2015-12-24

This study aimed to engage wide-ranging stakeholders and develop consensus learning objectives for undergraduate and postgraduate medical education. A UK-wide consultation garnered opinions of healthcare students, healthcare educators and other key stakeholders about environmental sustainability in medical education. The policy Delphi approach informed this study. Draft learning objectives were revised iteratively during three rounds of consultation: online questionnaire or telephone interview, face-to-face seminar and email consultation. Twelve draft learning objectives were developed based on review of relevant literature. In round one, 64 participants' median ratings of the learning objectives were 3.5 for relevance and 3.0 for feasibility on a Likert scale of one to four. Revisions were proposed, e.g. to highlight relevance to public health and professionalism. Thirty three participants attended round two. Conflicting opinions were explored. Added content areas included health benefits of sustainable behaviours. To enhance usability, restructuring provided three overarching learning objectives, each with subsidiary points. All participants from rounds one and two were contacted in round three, and no further edits were required. This is the first attempt to define consensus learning objectives for medical students about environmental sustainability. Allowing a wide range of stakeholders to comment on multiple iterations of the document stimulated their engagement with the issues raised and ownership of the resulting learning objectives.

Category learning increases discriminability of relevant object dimensions in visual cortex.

PubMed

Folstein, Jonathan R; Palmeri, Thomas J; Gauthier, Isabel

2013-04-01

Learning to categorize objects can transform how they are perceived, causing relevant perceptual dimensions predictive of object category to become enhanced. For example, an expert mycologist might become attuned to species-specific patterns of spacing between mushroom gills but learn to ignore cap textures attributable to varying environmental conditions. These selective changes in perception can persist beyond the act of categorizing objects and influence our ability to discriminate between them. Using functional magnetic resonance imaging adaptation, we demonstrate that such category-specific perceptual enhancements are associated with changes in the neural discriminability of object representations in visual cortex. Regions within the anterior fusiform gyrus became more sensitive to small variations in shape that were relevant during prior category learning. In addition, extrastriate occipital areas showed heightened sensitivity to small variations in shape that spanned the category boundary. Visual representations in cortex, just like our perception, are sensitive to an object's history of categorization.

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Mazzoni, Dominic

2009-01-01

An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item to present to the expert for labeling. Experiments on several data sets have demonstrated that the Relevance Bias approach significantly decreases the number of irrelevant items queried and also accelerates learning speed.

Twelve tips for utilizing principles of learning to support medical education.

PubMed

Cutting, Maris F; Saks, Norma Susswein

2012-01-01

Research in the cognitive sciences on learning and memory conducted across a range of domains, settings, and age groups has resulted in the identification and formulation of a set of generic learning principles. These learning principles have proven relevant and applicable to a wide range of learning situations in a variety of settings, and can be useful in supporting medical education. They can provide guidance to medical students for efficient and effective study, and can be helpful to faculty to support instructional planning and decisions relating to curriculum. This article discusses evidence-based principles of learning and their relationship to effective learning, teaching, pedagogy and curriculum development. We reviewed important principles of learning to determine those most relevant to improving medical student learning, guiding faculty toward more effective teaching, and in designing a curriculum. Our analysis has resulted in the articulation of key learning principles and specific strategies that are broadly applicable to medical school learning, teaching, and instructional planning. The twelve tips highlight principles of learning that can be effectively applied in the complex learning environment of medical education.

Networked Teacher Professional Development: The Case of Globaloria

ERIC Educational Resources Information Center

Whitehouse, Pamela

2011-01-01

The purpose of this paper is to explore a teacher professional development program embedded in a networked learning environment, and to offer an emerging model and analytic matrix of 21st century teacher professional development. The Globaloria program is based on theories of learning by design and facilitates teachers and students as they create…

Kids Learn from the Inside Out: How To Enhance the Human Matrix.

ERIC Educational Resources Information Center

Randolph, Shirley L.; And Others

Noting that humans develop according to a genetically encoded timeline and that departure from the timeline limits human potential, this book provides an illustrated practical guide to intervention to help children's bodies work as they should, to nurture children along their developmental timeline, to help children learn "from the inside…

Energy Conservation Education for New York State. Interdisciplinary Learning Activities. Grades 7-12.

ERIC Educational Resources Information Center

New York State Education Dept., Albany.

Provided in this document are 18 energy conservation activities designed to supplement regular classroom learning activities. A matrix correlating activity number with grade level and subject areas is included. Titles of activities are: puzzles; energy quiz; energy-related careers; reading a meter; trading calories for kilo-watts; conserving home…

Transition Matrices: A Tool to Assess Student Learning and Improve Instruction

ERIC Educational Resources Information Center

Morris, Gary A.; Walter, Paul; Skees, Spencer; Schwartz, Samantha

2017-01-01

This paper introduces a new spreadsheet tool for adoption by high school or college-level physics teachers who use common assessments in a pre-instruction/post-instruction mode to diagnose student learning and teaching effectiveness. The spreadsheet creates a simple matrix that identifies the percentage of students who select each possible…

Comprehensive Thematic T-Matrix Reference Database: A 2015-2017 Update

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Zakharova, Nadezhda; Khlebtsov, Nikolai G.; Videen, Gorden; Wriedt, Thomas

2017-01-01

The T-matrix method pioneered by Peter C. Waterman is one of the most versatile and efficient numerically exact computer solvers of the time-harmonic macroscopic Maxwell equations. It is widely used for the computation of electromagnetic scattering by single and composite particles, discrete random media, periodic structures (including metamaterials), and particles in the vicinity of plane or rough interfaces separating media with different refractive indices. This paper is the eighth update to the comprehensive thematic database of peer-reviewed T-matrix publications initiated in 2004 and lists relevant publications that have appeared since 2015. It also references a small number of earlier publications overlooked previously.

Comprehensive thematic T-matrix reference database: A 2015-2017 update

NASA Astrophysics Data System (ADS)

Mishchenko, Michael I.; Zakharova, Nadezhda T.; Khlebtsov, Nikolai G.; Videen, Gorden; Wriedt, Thomas

2017-11-01

The T-matrix method pioneered by Peter C. Waterman is one of the most versatile and efficient numerically exact computer solvers of the time-harmonic macroscopic Maxwell equations. It is widely used for the computation of electromagnetic scattering by single and composite particles, discrete random media, periodic structures (including metamaterials), and particles in the vicinity of plane or rough interfaces separating media with different refractive indices. This paper is the eighth update to the comprehensive thematic database of peer-reviewed T-matrix publications initiated in 2004 and lists relevant publications that have appeared since 2015. It also references a small number of earlier publications overlooked previously.

Thermomechanical and Environmental Durability of Environmental Barrier Coated Ceramic Matrix Composites Under Thermal Gradients

NASA Technical Reports Server (NTRS)

Zhu, Dongming; Bhatt, Ramakrishna T.; Harder, Bryan

2016-01-01

This paper presents the developments of thermo-mechanical testing approaches and durability performance of environmental barrier coatings (EBCs) and EBC coated SiCSiC ceramic matrix composites (CMCs). Critical testing aspects of the CMCs will be described, including state of the art instrumentations such as temperature, thermal gradient, and full field strain measurements; materials thermal conductivity evolutions and thermal stress resistance; NDE methods; thermo-mechanical stress and environment interactions associated damage accumulations. Examples are also given for testing ceramic matrix composite sub-elements and small airfoils to help better understand the critical and complex CMC and EBC properties in engine relevant testing environments.

Quantitative evaluation of the matrix effect in bioanalytical methods based on LC-MS: A comparison of two approaches.

PubMed

Rudzki, Piotr J; Gniazdowska, Elżbieta; Buś-Kwaśnik, Katarzyna

2018-06-05

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for studying pharmacokinetics and toxicokinetics. Reliable bioanalysis requires the characterization of the matrix effect, i.e. influence of the endogenous or exogenous compounds on the analyte signal intensity. We have compared two methods for the quantitation of matrix effect. The CVs(%) of internal standard normalized matrix factors recommended by the European Medicines Agency were evaluated against internal standard normalized relative matrix effects derived from Matuszewski et al. (2003). Both methods use post-extraction spiked samples, but matrix factors require also neat solutions. We have tested both approaches using analytes of diverse chemical structures. The study did not reveal relevant differences in the results obtained with both calculation methods. After normalization with the internal standard, the CV(%) of the matrix factor was on average 0.5% higher than the corresponding relative matrix effect. The method adopted by the European Medicines Agency seems to be slightly more conservative in the analyzed datasets. Nine analytes of different structures enabled a general overview of the problem, still, further studies are encouraged to confirm our observations. Copyright © 2018 Elsevier B.V. All rights reserved.

Using Culturally Relevant Teaching in a Co-Educational Mathematics Class of a Patriarchal Community

ERIC Educational Resources Information Center

Mogari, David

2017-01-01

The paper reports on the use of culturally relevant teaching in a class located in a patriarchal community. The paper is conceptualised around the notion that learners' familiar context provided by the socio-cultural activities can facilitate mathematics learning and make it fun to learn. Data were derived from a lesson activity using…

Psychological and Pedagogical Support for Students' Adaptation to Learning Activity in High Science School

ERIC Educational Resources Information Center

Zeleeva, Vera P.; Bykova, Svetlana S.; Varbanova, Silvia

2016-01-01

The relevance of the study is due to the importance of psychological and pedagogical support for students in university that would prevent difficulties in learning activities and increase adaptive capacity through the development of relevant personal traits. Therefore, this article is aimed at solving the problem of arranging psychological and…

Memory and Learning--A Study from Neurological Perspective

ERIC Educational Resources Information Center

Fathima, M. Parimala; Sasikumar, N.; Roja, M. Panimalar

2012-01-01

Learning is the acquisition and storage of information as a consequence of experience. The human brain is designed in such a way that thousands bits of sensory data are processed each minute. The brain pays attention to what is relevant to daily life, always asking: "What's going on?" and "How is it important relevant to me?"…

A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

NASA Astrophysics Data System (ADS)

Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

2018-04-01

The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

Students developing resources for students.

PubMed

Pearce, Michael; Evans, Darrell

2012-06-01

The development of new technologies has provided medical education with the ability to enhance the student learning experience and meet the needs of changing curricula. Students quickly adapt to using multimedia learning resources, but these need to be well designed, learner-centred and interactive for students to become significantly engaged. One way to ensure that students become committed users and that resources become distinct elements of the learning cycle is to involve students in resource design and production. Such an approach enables resources to accommodate student needs and preferences, but also provides opportunities for them to develop their own teaching and training skills. The aim of the medical student research project was to design and produce an electronic resource that was focused on a particular anatomical region. The views of other medical students were used to decide what features were suitable for inclusion and the resulting package contained basic principles and clinical relevance, and used a variety of approaches such as images of cadaveric material, living anatomy movies and quizzes. The completed package was assessed using a survey matrix and found to compare well with commercially available products. Given the ever-diversifying arena of multimedia instruction and the ability of students to be fully conversant with technology, this project demonstrates that students are ideal participants and creators of multimedia resources. It is hoped that such an approach will help to further develop the skill base of students, but will also provide an avenue of developing packages that are student user friendly, and that are focused towards particular curricula requirements. © Blackwell Publishing Ltd 2012.

Application of machine learning for the evaluation of turfgrass plots using aerial images

NASA Astrophysics Data System (ADS)

Ding, Ke; Raheja, Amar; Bhandari, Subodh; Green, Robert L.

2016-05-01

Historically, investigation of turfgrass characteristics have been limited to visual ratings. Although relevant information may result from such evaluations, final inferences may be questionable because of the subjective nature in which the data is collected. Recent advances in computer vision techniques allow researchers to objectively measure turfgrass characteristics such as percent ground cover, turf color, and turf quality from the digital images. This paper focuses on developing a methodology for automated assessment of turfgrass quality from aerial images. Images of several turfgrass plots of varying quality were gathered using a camera mounted on an unmanned aerial vehicle. The quality of these plots were also evaluated based on visual ratings. The goal was to use the aerial images to generate quality evaluations on a regular basis for the optimization of water treatment. Aerial images are used to train a neural network so that appropriate features such as intensity, color, and texture of the turfgrass are extracted from these images. Neural network is a nonlinear classifier commonly used in machine learning. The output of the neural network trained model is the ratings of the grass, which is compared to the visual ratings. Currently, the quality and the color of turfgrass, measured as the greenness of the grass, are evaluated. The textures are calculated using the Gabor filter and co-occurrence matrix. Other classifiers such as support vector machines and simpler linear regression models such as Ridge regression and LARS regression are also used. The performance of each model is compared. The results show encouraging potential for using machine learning techniques for the evaluation of turfgrass quality and color.

Advances in biomimetic regeneration of elastic matrix structures

PubMed Central

Sivaraman, Balakrishnan; Bashur, Chris A.

2012-01-01

Elastin is a vital component of the extracellular matrix, providing soft connective tissues with the property of elastic recoil following deformation and regulating the cellular response via biomechanical transduction to maintain tissue homeostasis. The limited ability of most adult cells to synthesize elastin precursors and assemble them into mature crosslinked structures has hindered the development of functional tissue-engineered constructs that exhibit the structure and biomechanics of normal native elastic tissues in the body. In diseased tissues, the chronic overexpression of proteolytic enzymes can cause significant matrix degradation, to further limit the accumulation and quality (e.g., fiber formation) of newly deposited elastic matrix. This review provides an overview of the role and importance of elastin and elastic matrix in soft tissues, the challenges to elastic matrix generation in vitro and to regenerative elastic matrix repair in vivo, current biomolecular strategies to enhance elastin deposition and matrix assembly, and the need to concurrently inhibit proteolytic matrix disruption for improving the quantity and quality of elastogenesis. The review further presents biomaterial-based options using scaffolds and nanocarriers for spatio-temporal control over the presentation and release of these biomolecules, to enable biomimetic assembly of clinically relevant native elastic matrix-like superstructures. Finally, this review provides an overview of recent advances and prospects for the application of these strategies to regenerating tissue-type specific elastic matrix structures and superstructures. PMID:23355960

Thinking outside the (voice) box: a case study of students' perceptions of the relevance of anatomy to speech pathology.

PubMed

Weir, Kristy A

2008-01-01

Speech pathology students readily identify the importance of a sound understanding of anatomical structures central to their intended profession. In contrast, they often do not recognize the relevance of a broader understanding of structure and function. This study aimed to explore students' perceptions of the relevance of anatomy to speech pathology. The effect of two learning activities on students' perceptions was also evaluated. First, a written assignment required students to illustrate the relevance of anatomy to speech pathology by using an example selected from one of the four alternative structures. The second approach was the introduction of brief "scenarios" with directed questions into the practical class. The effects of these activities were assessed via two surveys designed to evaluate students' perceptions of the relevance of anatomy before and during the course experience. A focus group was conducted to clarify and extend discussion of issues arising from the survey data. The results showed that the students perceived some course material as irrelevant to speech pathology. The importance of relevance to the students' "state" motivation was well supported by the data. Although the students believed that the learning activities helped their understanding of the relevance of anatomy, some structures were considered less relevant at the end of the course. It is likely that the perceived amount of content and surface approach to learning may have prevented students from "thinking outside the box" regarding which anatomical structures are relevant to the profession.

Action and Organizational Learning in an Elevator Company

ERIC Educational Resources Information Center

De Loo, Ivo

2006-01-01

Purpose: To highlight the relevance of management control in action learning programs that aim to foster organizational learning. Design/methodology/approach: Literature review plus case study. The latter consists of archival analysis and multiple interviews. Findings: When action learning programs are built around singular learning experiences,…

Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2018-03-01

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array) and fragments with diferent dimensions for clustering is carried out. The experiments, using the software environment Mathcad showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. The experimental results show that such models can be successfully used for auto- and hetero-associative recognition. Also they can be used to explain some mechanisms, known as "the reinforcementinhibition concept". Also we demonstrate a real model experiments, which confirm that the nonlinear processing by equivalent function allow to determine the neuron-winners and customize the weight matrix. At the end of the report, we will show how to use the obtained results and to propose new more efficient hardware architecture of SL_EC_RMNS based on matrix-tensor multipliers. Also we estimate the parameters and performance of such architectures.

Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

ERIC Educational Resources Information Center

Kyndt, Eva; Vermeire, Eva; Cabus, Shana

2016-01-01

Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

Cholinergic enhancement augments magnitude and specificity of visual perceptual learning in healthy humans

PubMed Central

Rokem, Ariel; Silver, Michael A.

2010-01-01

Summary Learning through experience underlies the ability to adapt to novel tasks and unfamiliar environments. However, learning must be regulated so that relevant aspects of the environment are selectively encoded. Acetylcholine (ACh) has been suggested to regulate learning by enhancing the responses of sensory cortical neurons to behaviorally-relevant stimuli [1]. In this study, we increased synaptic levels of ACh in the brains of healthy human subjects with the cholinesterase inhibitor donepezil (trade name: Aricept) and measured the effects of this cholinergic enhancement on visual perceptual learning. Each subject completed two five-day courses of training on a motion direction discrimination task [2], once while ingesting 5 mg of donepezil before every training session and once while placebo was administered. We found that cholinergic enhancement augmented perceptual learning for stimuli having the same direction of motion and visual field location used during training. In addition, perceptual learning under donepezil was more selective to the trained direction of motion and visual field location. These results, combined with previous studies demonstrating an increase in neuronal selectivity following cholinergic enhancement [3–5], suggest a possible mechanism by which ACh augments neural plasticity by directing activity to populations of neurons that encode behaviorally-relevant stimulus features. PMID:20850321

Learning a New Selection Rule in Visual and Frontal Cortex.

PubMed

van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

2016-08-01

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new icon-selection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey's choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process. © The Author 2016. Published by Oxford University Press.

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

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

Giving Meaning to the Numbers

ERIC Educational Resources Information Center

Marshall, Jeff; Horton, Bob; Austin-Wade, Joyce

2007-01-01

When learning, students yearn for meaning, challenge, and relevance. Integrated learning fulfills these desires by limiting the compartmentalization of learning--providing a more coherent learning environment. Too often, mathematics and the physical sciences are taught as separate entities. Yet, many commonalities exist, especially between…

Reinforcement learning in multidimensional environments relies on attention mechanisms.

PubMed

Niv, Yael; Daniel, Reka; Geana, Andra; Gershman, Samuel J; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C

2015-05-27

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning. Copyright © 2015 the authors 0270-6474/15/358145-13$15.00/0.

Blended learning in anesthesia education: current state and future model.

PubMed

Kannan, Jaya; Kurup, Viji

2012-12-01

Educators in anesthesia residency programs across the country are facing a number of challenges as they attempt to integrate blended learning techniques in their curriculum. Compared with the rest of higher education, which has made advances to varying degrees in the adoption of online learning anesthesiology education has been sporadic in the active integration of blended learning. The purpose of this review is to discuss the challenges in anesthesiology education and relevance of the Universal Design for Learning framework in addressing them. There is a wide chasm between student demand for online education and the availability of trained faculty to teach. The design of the learning interface is important and will significantly affect the learning experience for the student. This review examines recent literature pertaining to this field, both in the realm of higher education in general and medical education in particular, and proposes the application of a comprehensive learning model that is new to anesthesiology education and relevant to its goals of promoting self-directed learning.

Cell–material interactions on biphasic polyurethane matrix

PubMed Central

Dicesare, Patrick; Fox, Wade M.; Hill, Michael J.; Krishnan, G. Rajesh; Yang, Shuying; Sarkar, Debanjan

2013-01-01

Cell–matrix interaction is a key regulator for controlling stem cell fate in regenerative tissue engineering. These interactions are induced and controlled by the nanoscale features of extracellular matrix and are mimicked on synthetic matrices to control cell structure and functions. Recent studies have shown that nanostructured matrices can modulate stem cell behavior and exert specific role in tissue regeneration. In this study, we have demonstrated that nanostructured phase morphology of synthetic matrix can control adhesion, proliferation, organization and migration of human mesenchymal stem cells (MSCs). Nanostructured biodegradable polyurethanes (PU) with segmental composition exhibit biphasic morphology at nanoscale dimensions and can control cellular features of MSCs. Biodegradable PU with polyester soft segment and hard segment composed of aliphatic diisocyanates and dipeptide chain extender were designed to examine the effect polyurethane phase morphology. By altering the polyurethane composition, morphological architecture of PU was modulated and its effect was examined on MSC. Results show that MSCs can sense the nanoscale morphology of biphasic polyurethane matrix to exhibit distinct cellular features and, thus, signifies the relevance of matrix phase morphology. The role of nanostructured phases of a synthetic matrix in controlling cell–matrix interaction provides important insights for regulation of cell behavior on synthetic matrix and, therefore, is an important tool for engineering tissue regeneration. PMID:23255285

(q,{mu}) and (p,q,{zeta})-exponential functions: Rogers-Szego'' polynomials and Fourier-Gauss transform

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

Hounkonnou, Mahouton Norbert; Nkouankam, Elvis Benzo Ngompe

2010-10-15

From the realization of q-oscillator algebra in terms of generalized derivative, we compute the matrix elements from deformed exponential functions and deduce generating functions associated with Rogers-Szego polynomials as well as their relevant properties. We also compute the matrix elements associated with the (p,q)-oscillator algebra (a generalization of the q-one) and perform the Fourier-Gauss transform of a generalization of the deformed exponential functions.

Cognitive Pruning and Second Language Acquisition.

ERIC Educational Resources Information Center

Brown, H. Douglas

Ausubel distinguishes two kinds of human learning: (1) rote learning, relevant only to a small fraction of human learning, is the mechanistic formation of discrete, isolated traces in cognitive structure, usually through a process of conditioning; (2) meaningful learning, characteristic of most human learning, is a process of "subsuming" material…

Teachers' Self-Initiated Professional Learning through Personal Learning Networks

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

Piedmont City School District: Piedmont Middle School

ERIC Educational Resources Information Center

EDUCAUSE, 2015

2015-01-01

At Piedmont Middle School, the future for students is changing through relevant, engaging learning opportunities, a school culture filled with hope, and a redesigned teaching and learning environment that utilizes blended learning, project-based learning, and competency-based learning to personalize education. The academic model is anchored by a…

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Simple derivation of the Lindblad equation

NASA Astrophysics Data System (ADS)

Pearle, Philip

2012-07-01

The Lindblad equation is an evolution equation for the density matrix in quantum theory. It is the general linear, Markovian, form which ensures that the density matrix is Hermitian, trace 1, positive and completely positive. Some elementary examples of the Lindblad equation are given. The derivation of the Lindblad equation presented here is ‘simple’ in that all it uses is the expression of a Hermitian matrix in terms of its orthonormal eigenvectors and real eigenvalues. Thus, it is appropriate for students who have learned the algebra of quantum theory. Where helpful, arguments are first given in a two-dimensional Hilbert space.

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Commognitive analysis of undergraduate mathematics students' first encounter with the subgroup test

NASA Astrophysics Data System (ADS)

Ioannou, Marios

2018-06-01

This study analyses learning aspects of undergraduate mathematics students' first encounter with the subgroup test, using the commognitive theoretical framework. It focuses on students' difficulties as these are related to the object-level and metalevel mathematical learning in group theory, and, when possible, highlights any commognitive conflicts. In the data analysis, one can identify three types of difficulties, relevant to object-level learning: namely regarding the frequently observed confusion between groups and sets, the object-level rules of visual mediators, and the object-level rules of contextual notions, such as permutations, exponentials, sets and matrices. In addition, data analysis suggests two types of difficulties, relevant to metalevel learning. The first refers to the actual proof that the three conditions of subgroup test hold, and the second is related to syntactic inaccuracies, incomplete argumentation and problematic use of visual mediators. Finally, this study suggests that there are clear links between object-level and metalevel learning, mainly due to the fact that objectification of the various relevant mathematical notions influences the endorsement of the governing metarules.

Prioritising the relevant information for learning and decision making within orbital and ventromedial prefrontal cortex.

PubMed

Walton, Mark E; Chau, Bolton K H; Kennerley, Steven W

2015-02-01

Our environment and internal states are frequently complex, ambiguous and dynamic, meaning we need to have selection mechanisms to ensure we are basing our decisions on currently relevant information. Here, we review evidence that orbitofrontal (OFC) and ventromedial prefrontal cortex (VMPFC) play conserved, critical but distinct roles in this process. While OFC may use specific sensory associations to enhance task-relevant information, particularly in the context of learning, VMPFC plays a role in ensuring irrelevant information does not impinge on the decision in hand.

Perceived barriers to medical-error reporting: an exploratory investigation.

PubMed

Uribe, Claudia L; Schweikhart, Sharon B; Pathak, Dev S; Dow, Merrell; Marsh, Gail B

2002-01-01

Medical-error reporting is an essential component for patient safety enhancement. Unfortunately, medical errors are largely underreported across healthcare institutions. This problem can be attributed to different factors and barriers present at organizational and individual levels that ultimately prevent individuals from generating the report. This study explored the factors that affect medical-error reporting among physicians and nurses at a large academic medical center located in the midwest United States. A nominal group session was conducted to identify the most relevant factors that act as barriers for error reporting. These factors were then used to design a questionnaire that explored the likelihood of the factors to act as barriers and their likelihood to be modified. Using these two parameters, the results were analyzed and combined into a Factor Relevance Matrix. The matrix identifies the factors for which immediate actions should be undertaken to improve medical-error reporting (immediate action factors). It also identifies factors that require long-term strategies (long-term strategy factors) as well as factors that the organization should be aware of but that are of lower priority (awareness factors). The strategies outlined in this study may assist healthcare organizations in improving medical-error reporting, as part of the efforts toward patient-safety enhancement. Although factors affecting medical-error reporting may vary between different organizations, the process used in identifying the factors and the Factor Relevance Matrix developed in this study are easily adaptable to any organizational setting.

[Characteristics, advantages, and limits of matrix tests].

PubMed

Brand, T; Wagener, K C

2017-03-01

Deterioration of communication abilities due to hearing problems is particularly relevant in listening situations with noise. Therefore, speech intelligibility tests in noise are required for audiological diagnostics and evaluation of hearing rehabilitation. This study analyzed the characteristics of matrix tests assessing the 50 % speech recognition threshold in noise. What are their advantages and limitations? Matrix tests are based on a matrix of 50 words (10 five-word sentences with same grammatical structure). In the standard setting, 20 sentences are presented using an adaptive procedure estimating the individual 50 % speech recognition threshold in noise. At present, matrix tests in 17 different languages are available. A high international comparability of matrix tests exists. The German language matrix test (OLSA, male speaker) has a reference 50 % speech recognition threshold of -7.1 (± 1.1) dB SNR. Before using a matrix test for the first time, the test person has to become familiar with the basic speech material using two training lists. Hereafter, matrix tests produce constant results even if repeated many times. Matrix tests are suitable for users of hearing aids and cochlear implants, particularly for assessment of benefit during the fitting process. Matrix tests can be performed in closed form and consequently with non-native listeners, even if the experimenter does not speak the test person's native language. Short versions of matrix tests are available for listeners with a shorter memory span, e.g., children.

Minimal Groups Increase Young Children's Motivation and Learning on Group-Relevant Tasks

ERIC Educational Resources Information Center

Master, Allison; Walton, Gregory M.

2013-01-01

Three experiments ("N" = 130) used a minimal group manipulation to show that just perceived membership in a social group boosts young children's motivation for and learning from group-relevant tasks. In Experiment 1, 4-year-old children assigned to a minimal "puzzles group" persisted longer on a challenging puzzle than children identified as the…

Transformational Teaching in the Information Age: Making Why and How We Teach Relevant to Students

ERIC Educational Resources Information Center

Rosebrough, Thomas R.; Leverett, Ralph G.

2011-01-01

Yes, it's true that today's students have tons of distractions that take their attention away from the hard work of learning. That's why it's more important than ever to establish a teaching relationship with students that makes academic learning relevant to their lives. Here's a book that explains how to do that by changing teaching practices…

The Relevance of Multi Media Skills in Teaching and Learning of Scientific Concepts in Secondary Schools in Lagos State, Nigeria

ERIC Educational Resources Information Center

Okedeyi, Abiodun S.; Oginni, Aderonke M.; Adegorite, Solomon O.; Saibu, Sakibu O.

2015-01-01

This study investigated the relevance of multi media skills in teaching and learning of scientific concepts in secondary schools. Self constructed questionnaire was administered to 120 students randomly selected in four secondary schools in Ojo Local Government Area of Lagos state. Data generated were analyzed using chi-square statistical…

Factors that Influence the Perceived Advantages and Relevance of Facebook as a Learning Tool: An Extension of the UTAUT

ERIC Educational Resources Information Center

Escobar-Rodríguez, Tomás; Carvajal-Trujillo, Elena; Monge-Lozano, Pedro

2014-01-01

Social media technologies are becoming a fundamental component of education. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify factors that influence the perceived advantages and relevance of Facebook as a learning tool. The proposed model is based on previous models of UTAUT. Constructs from previous…

Practice reduces task relevant variance modulation and forms nominal trajectory

NASA Astrophysics Data System (ADS)

Osu, Rieko; Morishige, Ken-Ichi; Nakanishi, Jun; Miyamoto, Hiroyuki; Kawato, Mitsuo

2015-12-01

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

The Disciplinary Literacy Discussion Matrix: A Heuristic Tool for Initiating Collaboration in Higher Education

ERIC Educational Resources Information Center

Airey, John

2011-01-01

In this paper I address the issue of collaboration between content lecturers and language lecturers or educational researchers. Whilst such collaboration is a desirable goal for disciplinary learning in monolingual settings, I suggest it takes on extra significance when two or more languages are involved in teaching and learning a discipline.…

49 CFR 192.913 - When may an operator deviate its program from certain requirements of this subpart?

Code of Federal Regulations, 2014 CFR

2014-10-01

... comprehensive data integration process; (iv) A procedure for applying lessons learned from assessment of covered... performance matrix that demonstrates the program has been effective in ensuring the integrity of the covered... requirements in § 192.933, and incorporate the results and lessons learned from the more recent assessment into...

49 CFR 192.913 - When may an operator deviate its program from certain requirements of this subpart?

Code of Federal Regulations, 2013 CFR

2013-10-01

... comprehensive data integration process; (iv) A procedure for applying lessons learned from assessment of covered... performance matrix that demonstrates the program has been effective in ensuring the integrity of the covered... requirements in § 192.933, and incorporate the results and lessons learned from the more recent assessment into...

49 CFR 192.913 - When may an operator deviate its program from certain requirements of this subpart?

Code of Federal Regulations, 2012 CFR

2012-10-01

... comprehensive data integration process; (iv) A procedure for applying lessons learned from assessment of covered... performance matrix that demonstrates the program has been effective in ensuring the integrity of the covered... requirements in § 192.933, and incorporate the results and lessons learned from the more recent assessment into...

Structural Conditions for Collaboration and Learning in Innovation Networks: Using an Innovation System Performance Lens to Analyse Agricultural Knowledge Systems

ERIC Educational Resources Information Center

Hermans, Frans; Klerkx, Laurens; Roep, Dirk

2015-01-01

Purpose: We investigate how the structural conditions of eight different European agricultural innovation systems can facilitate or hinder collaboration and social learning in multidisciplinary innovation networks. Methodology: We have adapted the Innovation System Failure Matrix to investigate the main barriers and enablers eight countries…

Collaborative Design of Assessment Criteria to Improve Undergraduate Student Engagement and Performance

ERIC Educational Resources Information Center

Leslie, Laura J.; Gorman, Paul C.

2017-01-01

Student engagement is vital in enhancing the student experience and encouraging deeper learning. Involving students in the design of assessment criteria is one way in which to increase student engagement. In 2011, a marking matrix was used at Aston University (UK) for logbook assessment (Group One) in a project-based learning module. The next…

Web 2.0--E-Learning 2.0--Quality 2.0? Quality for New Learning Cultures

ERIC Educational Resources Information Center

Ehlers, Ulf Daniel

2009-01-01

Purpose: The purpose of this paper is to analyse the changes taking place when learning moves from a transmissive learning model to a collaborative and reflective learning model and proposes consequences for quality development. Design/methodology/approach: The paper summarises relevant research in the field of e-learning to outline the…

Developing an English Mobile Learning Attitude Scale for Adult Learners

ERIC Educational Resources Information Center

Liu, Tzu-Ying

2017-01-01

In recent years, with the rapid development of mobile devices, mobile learning (m-learning) has becoming another popular topic. There is a strong need for both researchers and educators to be aware of adult learners' attitudes toward English mobile learning, yet relevant studies on mobile learning to promote English learning for adult learners are…

Learning How to Learn: Implications for Non Traditional Adult Students

ERIC Educational Resources Information Center

Tovar, Lynn A.

2008-01-01

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

Knowledge Transfer: What, How, and Why

ERIC Educational Resources Information Center

Chin, Si-Chi

2013-01-01

People learn from prior experiences. We first learn how to use a spoon and then know how to use a different size of spoon. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed of learning. This observation is relevant to human learning, as well…

Models of Learning Space: Integrating Research on Space, Place and Learning in Higher Education

ERIC Educational Resources Information Center

Ellis, R. A.; Goodyear, P.

2016-01-01

Learning space research is a relatively new field of study that seeks to inform the design, evaluation and management of learning spaces. This paper reviews a dispersed and fragmented literature relevant to understanding connections between university learning spaces and student learning activities. From this review, the paper distils a number of…

Efficient Matrix Models for Relational Learning

DTIC Science & Technology

2009-10-01

74 4.5.3 Comparison to pLSI- pHITS . . . . . . . . . . . . . . . . . . . . 76 5 Hierarchical Bayesian Collective...Behaviour of Newton vs. Stochastic Newton on a three-factor model. 4.5.3 Comparison to pLSI- pHITS Caveat: Collective Matrix Factorization makes no guarantees...leads to better results; and another where a co-clustering model, pLSI- pHITS , has the advantage. pLSI- pHITS [24] is a relational clustering technique

Using an Ishikawa diagram as a tool to assist memory and retrieval of relevant medical cases from the medical literature.

PubMed

Wong, Kam Cheong

2011-03-29

Studying medical cases is an effective way to enhance clinical reasoning skills and reinforce clinical knowledge. An Ishikawa diagram, also known as a cause-and-effect diagram or fishbone diagram, is often used in quality management in manufacturing industries.In this report, an Ishikawa diagram is used to demonstrate how to relate potential causes of a major presenting problem in a clinical setting. This tool can be used by teams in problem-based learning or in self-directed learning settings.An Ishikawa diagram annotated with references to relevant medical cases and literature can be continually updated and can assist memory and retrieval of relevant medical cases and literature. It could also be used to cultivate a lifelong learning habit in medical professionals.

Ultrafast learning in a hard-limited neural network pattern recognizer

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

1996-03-01

As we published in the last five years, the supervised learning in a hard-limited perceptron system can be accomplished in a noniterative manner if the input-output mapping to be learned satisfies a certain positive-linear-independency (or PLI) condition. When this condition is satisfied (for most practical pattern recognition applications, this condition should be satisfied,) the connection matrix required to meet this mapping can be obtained noniteratively in one step. Generally, there exist infinitively many solutions for the connection matrix when the PLI condition is satisfied. We can then select an optimum solution such that the recognition of any untrained patterns will become optimally robust in the recognition mode. The learning speed is very fast and close to real-time because the learning process is noniterative and one-step. This paper reports the theoretical analysis and the design of a practical charter recognition system for recognizing hand-written alphabets. The experimental result is recorded in real-time on an unedited video tape for demonstration purposes. It is seen from this real-time movie that the recognition of the untrained hand-written alphabets is invariant to size, location, orientation, and writing sequence, even the training is done with standard size, standard orientation, central location and standard writing sequence.

Exploring undergraduate students' attitudes towards interprofessional learning, motivation-to-learn, and perceived impact of learning conflict resolution skills.

PubMed

Vandergoot, Sonya; Sarris, Aspa; Kirby, Neil; Ward, Helena

2018-03-01

Conflict resolution skills are important for all healthcare professionals as conflict and mis-communication can have detrimental effects on decision-making, potentially impacting significantly on patient care, morbidity, and mortality. Interprofessional learning (IPL) has been found to increase collaboration and improve collegial relationships and hence may be an appropriate way to increase conflict resolution skills among healthcare graduates. This study examined transference of conflict resolution skills, motivation-to-learn, and attitudes to IPL of medical (n = 52) and nursing (n = 74) undergraduate students who undertook an IPL conflict resolution program. Results indicated that motivation-to-learn, attitudes to IPL, and transfer of conflict resolution skills were significantly related to each other, even when controlling for other variables, such as age and gender. When comparing the two groups, undergraduate nursing students were found to have statistically higher motivation-to-learn and transference of conflict resolution skills, and reported a more positive attitude to IPL than medical students. Some of these differences may be attributed to lack of clinical placements for medical students in the first half of their degree at their university, giving them less opportunity to apply the conflict resolution skills taught, as well as less contextual relevance. This may potentially affect their motivation-to-learn and attitude to IPL thus impacting on how they perceive the relevance of learning conflict resolution skills. Without the contextual relevancy of placements at the time of learning for medical students, the newly acquired conflict resolution skills are less likely to transfer to practice in an optimal fashion.

e-Learning in Surgical Education: A Systematic Review.

PubMed

Jayakumar, Nithish; Brunckhorst, Oliver; Dasgupta, Prokar; Khan, Muhammad Shamim; Ahmed, Kamran

2015-01-01

e-Learning involves the delivery of educational content through web-based methods. Owing to work-hour restrictions and changing practice patterns in surgery, e-learning can offer an effective alternative to traditional teaching. Our aims were to (1) identify current modalities of e-learning, (2) assess the efficacy of e-learning as an intervention in surgical education through a systematic review of the literature, and (3) discuss the relevance of e-learning as an educational tool in surgical education. This is the first such systematic review in this field. A systematic search of MEDLINE and EMBASE was conducted for relevant articles published until July 2014, using a predefined search strategy. The database search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 38 articles were found which met the inclusion criteria. In these studies, e-learning was used as an intervention in 3 different ways: (1) to teach cases through virtual patients (18/38); (2) to teach theoretical knowledge through online tutorials, or other means (18/38); and (3) to teach surgical skills (2/38). Nearly all of the studies reviewed report significant knowledge gain from e-learning; however, 2 in 3 studies did not use a control group. e-Learning has emerged as an effective mode of teaching with particular relevance for surgical education today. Published studies have demonstrated the efficacy of this method; however, future work must involve well-designed randomized controlled trials comparing e-learning against standard teaching. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Implementing Infrastructures for Managing Learning Objects

ERIC Educational Resources Information Center

Klemke, Roland; Ternier, Stefaan; Kalz, Marco; Specht, Marcus

2010-01-01

Making learning objects available is critical to reuse learning resources. Making content transparently available and providing added value to different stakeholders is among the goals of the European Commission's eContentplus programme. This paper analyses standards and protocols relevant for making learning objects accessible in distributed data…

Online Collaborative Learning: Theory and Practice

ERIC Educational Resources Information Center

Roberts, Tim, Ed.

2004-01-01

"Online Collaborative Learning: Theory and Practice" provides a resource for researchers and practitioners in the area of online collaborative learning (also known as CSCL, computer-supported collaborative learning), particularly those working within a tertiary education environment. It includes articles of relevance to those interested in both…

Brain-Based Teaching/Learning and Implications for Religious Education.

ERIC Educational Resources Information Center

Weber, Jean Marie

2002-01-01

Argues that physical activity and water can increase brain activity, and hence, learning. Findings of neuroscientists regarding the brain can inform educators. Brain-based teaching emphasizes teamwork, cooperative learning, and global responsibility. Argues against gathering information without relevance. Connects brain-based learning concepts to…

Simulations and Games as Chaordic Learning Contexts

ERIC Educational Resources Information Center

Leigh, Elyssebeth; Spindler, Laraine

2004-01-01

Effective facilitation of experiential learning involves an array of knowledge and skills. Educators who facilitate open simulations--one form of experiential learning--benefit from having relevant theoretical frameworks to sustain an appropriate balance between being directive and supportive of their participants' freedom to learn. This ongoing…

Learning Analytics: Readiness and Rewards

ERIC Educational Resources Information Center

Friesen, Norm

2013-01-01

This position paper introduces the relatively new field of learning analytics, first by considering the relevant meanings of both "learning" and "analytics," and then by looking at two main levels at which learning analytics can be or has been implemented in educational organizations. Although integrated turnkey systems or…

Towards Entrepreneurial Learning Competencies: The Perspective of Built Environment Students

ERIC Educational Resources Information Center

Ernest, Kissi; Matthew, Somiah K.; Samuel, Ansah K.

2015-01-01

This paper sought to discuss entrepreneurial learning competencies by determining the outcome of entrepreneurial learning on the views of built environment students in the university setting. In this study, three relevant competencies were identified for entrepreneurial learning through literature, namely: entrepreneurial attitude, entrepreneurial…

Multimedia Principle in Teaching Lessons

ERIC Educational Resources Information Center

Kari Jabbour, Khayrazad

2012-01-01

Multimedia learning principle occurs when we create mental representations from combining text and relevant graphics into lessons. This article discusses the learning advantages that result from adding multimedia learning principle into instructions; and how to select graphics that support learning. There is a balance that instructional designers…

Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

PubMed Central

Daniel, Reka; Geana, Andra; Gershman, Samuel J.; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C.

2015-01-01

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this “representation learning” process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the “curse of dimensionality” in reinforcement learning. PMID:26019331

Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.

PubMed

Leong, Yuan Chang; Radulescu, Angela; Daniel, Reka; DeWoskin, Vivian; Niv, Yael

2017-01-18

Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants' choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants' focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep convolutional neural network based antenna selection in multiple-input multiple-output system

NASA Astrophysics Data System (ADS)

Cai, Jiaxin; Li, Yan; Hu, Ying

2018-03-01

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

Manifold learning-based subspace distance for machinery damage assessment

NASA Astrophysics Data System (ADS)

Sun, Chuang; Zhang, Zhousuo; He, Zhengjia; Shen, Zhongjie; Chen, Binqiang

2016-03-01

Damage assessment is very meaningful to keep safety and reliability of machinery components, and vibration analysis is an effective way to carry out the damage assessment. In this paper, a damage index is designed by performing manifold distance analysis on vibration signal. To calculate the index, vibration signal is collected firstly, and feature extraction is carried out to obtain statistical features that can capture signal characteristics comprehensively. Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace. The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment. Finally, Grassmann distance between manifold subspaces is defined as a damage index. The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold. The defined damage index is applied to damage assessment of a rotor and the bearing, and the result validates its effectiveness for damage assessment of machinery component.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Deep and Surface Learning in Problem-Based Learning: A Review of the Literature

ERIC Educational Resources Information Center

Dolmans, Diana H. J. M.; Loyens, Sofie M. M.; Marcq, Hélène; Gijbels, David

2016-01-01

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review…

Mobile and Online Learning Journal: Effects on Apprentices' Reflection in Vocational Education and Training

ERIC Educational Resources Information Center

Mauroux, Laetitia; Könings, Karen D.; Zufferey, Jessica Dehler; Gurtner, Jean-Luc

2014-01-01

While learning journals (LJs) have been shown to support self-regulated learning strategies, reflection and learning outcomes in academic contexts, few studies have investigated their relevance in vocational education. A mobile and online learning journal (MOLJ) was developed to support reflection on workplace experiences. However, acceptance of…

Cognitive Strategies for Learning from Static and Dynamic Visuals.

ERIC Educational Resources Information Center

Lewalter, D.

2003-01-01

Studied the effects of including static or dynamic visuals in an expository text on a learning outcome and the use of learning strategies when working with these visuals. Results for 60 undergraduates for both types of illustration indicate different frequencies in the use of learning strategies relevant for the learning outcome. (SLD)

Utilizing Shulman's Table of Learning to Understand Learning in Professional Health Science Programs

ERIC Educational Resources Information Center

Mortier, Teresa; Yatczak, Jayne

2016-01-01

Understanding student learning in health science professional programs is both timely and relevant and is the focus of this article. "The Table of Learning" by Lee Shulman (2002) provided a tool for an interdisciplinary reflection surrounding student learning in clinical laboratory science and occupational therapy. Utilizing the taxonomy…

Managing Dynamics of Power and Learning in Community Development: A Case Study of Iowan Farmers in Uganda

ERIC Educational Resources Information Center

Stephen, Lauer; Owusu, Francis Y.

2015-01-01

Extension professionals facilitate community development through the strategic manipulation of learning and power in peer-to-peer learning partnerships. We discuss the relationship between empowerment and power, highlight relevant literature on the difficulties power presents to learning and the efficacy of service learning tools to facilitate…

The Credentials of Brain-Based Learning

ERIC Educational Resources Information Center

Davis, Andrew

2004-01-01

This paper discusses the current fashion for brain-based learning, in which value-laden claims about learning are grounded in neurophysiology. It argues that brain science cannot have the authority about learning that some seek to give it. It goes on to discuss whether the claim that brain science is relevant to learning involves a category…

Predicting Reading and Mathematics from Neural Activity for Feedback Learning

ERIC Educational Resources Information Center

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A.

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task…

E-Learning System Overview Based on Semantic Web

ERIC Educational Resources Information Center

Alsultanny, Yas A.

2006-01-01

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. e-Learning is efficient task relevant and just-in-time learning grown from the learning requirements of the new dynamically changing, distributed business world. In this paper we design an e-Learning system…

An Integrated Learning Management System for Location-Based Mobile Learning

ERIC Educational Resources Information Center

Sailer, Christian; Kiefer, Peter; Raubal, Martin

2015-01-01

This paper discusses the relevance and challenges of a location-based learning platform that supports mobile learning in education. We present the design of an integrated management system for location-based mobile learning. Independent of the taught subject, the objective of the system is an easy-to-understand user interface for both - teachers…

Cross-Disciplinary Contributions to E-Learning Design: A Tripartite Design Model

ERIC Educational Resources Information Center

Hutchins, Holly M.; Hutchison, Dennis

2008-01-01

Purpose: The purpose of this paper is to review cross-disciplinary research on e-learning from workplace learning, educational technology, and instructional communication disciplines to identify relevant e-learning design principles. It aims to use these principles to propose an e-learning model that can guide the design of instructionally sound,…

Possible world based consistency learning model for clustering and classifying uncertain data.

PubMed

Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

2018-06-01

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Free energy and phase transition of the matrix model on a plane wave

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

Hadizadeh, Shirin; Ramadanovic, Bojan; Semenoff, Gordon W.

2005-03-15

It has recently been observed that the weakly coupled plane-wave matrix model has a density of states which grows exponentially at high energy. This implies that the model has a phase transition. The transition appears to be of first order. However, its exact nature is sensitive to interactions. In this paper, we analyze the effect of interactions by computing the relevant parts of the effective potential for the Polyakov loop operator in the finite temperature plane-wave matrix model to three-loop order. We show that the phase transition is indeed of first order. We also compute the correction to the Hagedornmore » temperature to order two loops.« less

Novel Entries in a Fungal Biofilm Matrix Encyclopedia

PubMed Central

Zarnowski, Robert; Westler, William M.; Lacmbouh, Ghislain Ade; Marita, Jane M.; Bothe, Jameson R.; Bernhardt, Jörg; Lounes-Hadj Sahraoui, Anissa; Fontaine, Joël; Sanchez, Hiram; Hatfield, Ronald D.; Ntambi, James M.; Nett, Jeniel E.; Mitchell, Aaron P.

2014-01-01

ABSTRACT Virulence of Candida is linked with its ability to form biofilms. Once established, biofilm infections are nearly impossible to eradicate. Biofilm cells live immersed in a self-produced matrix, a blend of extracellular biopolymers, many of which are uncharacterized. In this study, we provide a comprehensive analysis of the matrix manufactured by Candida albicans both in vitro and in a clinical niche animal model. We further explore the function of matrix components, including the impact on drug resistance. We uncovered components from each of the macromolecular classes (55% protein, 25% carbohydrate, 15% lipid, and 5% nucleic acid) in the C. albicans biofilm matrix. Three individual polysaccharides were identified and were suggested to interact physically. Surprisingly, a previously identified polysaccharide of functional importance, β-1,3-glucan, comprised only a small portion of the total matrix carbohydrate. Newly described, more abundant polysaccharides included α-1,2 branched α-1,6-mannans (87%) associated with unbranched β-1,6-glucans (13%) in an apparent mannan-glucan complex (MGCx). Functional matrix proteomic analysis revealed 458 distinct activities. The matrix lipids consisted of neutral glycerolipids (89.1%), polar glycerolipids (10.4%), and sphingolipids (0.5%). Examination of matrix nucleic acid identified DNA, primarily noncoding sequences. Several of the in vitro matrix components, including proteins and each of the polysaccharides, were also present in the matrix of a clinically relevant in vivo biofilm. Nuclear magnetic resonance (NMR) analysis demonstrated interaction of aggregate matrix with the antifungal fluconazole, consistent with a role in drug impedance and contribution of multiple matrix components. PMID:25096878

Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

PubMed

Galleske, I; Castellanos, J

2002-05-01

This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

Analysis of Science Attitudes for K2 Planet Hunter Mission

DTIC Science & Technology

2015-03-01

15 1. International Astronomical Union ...................................................15 2. IAU Planet Definition ...16 3. Planet Definition Relevant to Kepler Mission .................................16 B. STAR...73 a. Definition Based on Direction Cosine Matrix .......................73 b. Definition Based

A group matrix representation relevant to scales of measurement of clinical disease states via stratified vectors.

PubMed

Sawamura, Jitsuki; Morishita, Shigeru; Ishigooka, Jun

2016-02-09

Previously, we applied basic group theory and related concepts to scales of measurement of clinical disease states and clinical findings (including laboratory data). To gain a more concrete comprehension, we here apply the concept of matrix representation, which was not explicitly exploited in our previous work. Starting with a set of orthonormal vectors, called the basis, an operator Rj (an N-tuple patient disease state at the j-th session) was expressed as a set of stratified vectors representing plural operations on individual components, so as to satisfy the group matrix representation. The stratified vectors containing individual unit operations were combined into one-dimensional square matrices [Rj]s. The [Rj]s meet the matrix representation of a group (ring) as a K-algebra. Using the same-sized matrix of stratified vectors, we can also express changes in the plural set of [Rj]s. The method is demonstrated on simple examples. Despite the incompleteness of our model, the group matrix representation of stratified vectors offers a formal mathematical approach to clinical medicine, aligning it with other branches of natural science.

Survey of Quantification and Distance Functions Used for Internet-based Weak-link Sociological Phenomena

DTIC Science & Technology

2016-03-01

well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology literature for the relevant...Google matrix, PageRank as well as the Yahoo search engine and a classic SearchKing HIST algorithm. The co-PI immersed herself in the sociology...The PI studied all mathematical literature he can find related to the Google search engine, Google matrix, PageRank as well as the Yahoo search

Neutrino Mixing and the Double Tetrahedral Group

NASA Astrophysics Data System (ADS)

Bentov, Yoni; Zee, A.

2013-11-01

In the spirit of a previous study of the tetrahedral group T ≃A4, we discuss a minimalist scheme to derive the neutrino mixing matrix using the double tetrahedral group T‧, the double cover of T. The new features are three distinct two-dimensional representations and complex Clebsch-Gordan coefficients, which can result in a geometric source of CP violation in the neutrino mass matrix. In an appendix, we derive explicitly the relevant group theory for the tetrahedral group T and its double cover T‧.

Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions.

PubMed

Agarwal, Shashank; Liu, Feifan; Yu, Hong

2011-10-03

Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles. Simple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.

The Strength of Ethical Matrixes as a Tool for Normative Analysis Related to Technological Choices: The Case of Geological Disposal for Radioactive Waste.

PubMed

Kermisch, Céline; Depaus, Christophe

2018-02-01

The ethical matrix is a participatory tool designed to structure ethical reflection about the design, the introduction, the development or the use of technologies. Its collective implementation, in the context of participatory decision-making, has shown its potential usefulness. On the contrary, its implementation by a single researcher has not been thoroughly analyzed. The aim of this paper is precisely to assess the strength of ethical matrixes implemented by a single researcher as a tool for conceptual normative analysis related to technological choices. Therefore, the ethical matrix framework is applied to the management of high-level radioactive waste, more specifically to retrievable and non-retrievable geological disposal. The results of this analysis show that the usefulness of ethical matrixes is twofold and that they provide a valuable input for further decision-making. Indeed, by using ethical matrixes, implicit ethically relevant issues were revealed-namely issues of equity associated with health impacts and differences between close and remote future generations regarding ethical impacts. Moreover, the ethical matrix framework was helpful in synthesizing and comparing systematically the ethical impacts of the technologies under scrutiny, and hence in highlighting the potential ethical conflicts.

Modeling food matrix effects on chemical reactivity: Challenges and perspectives.

PubMed

Capuano, Edoardo; Oliviero, Teresa; van Boekel, Martinus A J S

2017-06-29

The same chemical reaction may be different in terms of its position of the equilibrium (i.e., thermodynamics) and its kinetics when studied in different foods. The diversity in the chemical composition of food and in its structural organization at macro-, meso-, and microscopic levels, that is, the food matrix, is responsible for this difference. In this viewpoint paper, the multiple, and interconnected ways the food matrix can affect chemical reactivity are summarized. Moreover, mechanistic and empirical approaches to explain and predict the effect of food matrix on chemical reactivity are described. Mechanistic models aim to quantify the effect of food matrix based on a detailed understanding of the chemical and physical phenomena occurring in food. Their applicability is limited at the moment to very simple food systems. Empirical modeling based on machine learning combined with data-mining techniques may represent an alternative, useful option to predict the effect of the food matrix on chemical reactivity and to identify chemical and physical properties to be further tested. In such a way the mechanistic understanding of the effect of the food matrix on chemical reactions can be improved.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Spatial patterns of schistosomiasis in Burkina Faso: relevance of human mobility and water resources development

NASA Astrophysics Data System (ADS)

Perez-Saez, Javier; Bertuzzo, Enrico; Frohelich, Jean-Marc; Mande, Theophile; Ceperley, Natalie; Sou, Mariam; Yacouba, Hamma; Maiga, Hamadou; Sokolow, Susanne; De Leo, Giulio; Casagrandi, Renato; Gatto, Marino; Mari, Lorenzo; Rinaldo, Andrea

2015-04-01

We study the spatial geography of schistosomiasis in the african context of Burkina Faso by means of a spatially explicit model of disease dynamics and spread. The relevance of our work lies in its ability to describe quantitatively a geographic stratification of the disease burden capable of reproducing important spatial differences, and drivers/controls of disease spread. Among the latters, we consider specifically the development and management of water resources which have been singled out empirically as an important risk factor for schistosomiasis. The model includes remotely acquired and objectively manipulated information on the distributions of population, infrastructure, elevation and climatic drivers. It also includes a general description of human mobility and addresses a first-order characterization of the ecology of the intermediate host of the parasite causing the disease based on maximum entropy learning of relevant environmenal covariates. Spatial patterns of the disease were analyzed about their disease-free equilibrium by proper extraction and mapping of suitable eigenvectors of the Jacobian matrix subsuming all stability properties of the system. Human mobility was found to be a primary control of both pathogen invasion success and of the overall distribution of disease burden. The effects of water resources development were studied by accounting for the (prior and posterior) average distances of human settlements from water bodies that may serve as suitable habitats to the intermediate host of the parasite. Water developments, in combination with human mobility, were quantitatively related to disease spread into regions previously nearly disease-free and to large-scale empirical incidence patterns. We concluded that while the model still needs refinements based on field and epidemiological evidence, the framework proposed provides a powerful tool for large-scale, long-term public health planning and management of schistosomiasis.

Creating a Model of Acceptance: Preservice Teachers Interact with Non-English-Speaking Latino Parents Using Culturally Relevant Mathematics and Science Activities at Family Learning Events

ERIC Educational Resources Information Center

Ramirez, Olga; McCollough, Cherie A.; Diaz, Zulmaris

2016-01-01

The following describes a culturally relevant mathematics and science content program implemented by preservice teachers (PSTs) at Family Math/Science Learning Events (FM/SLEs) conducted through two different university programs in south Texas. These experiences are required course activities designed to inform PSTs of the importance of…

Reconstructionist Analysis on the Relevance of Secondary School Learning Opportunities in Promoting National Cohesion among Students in Machakos Town Sub-County, Kenya

ERIC Educational Resources Information Center

Mwanzia, Ruth Mutunge; Mwangi, Simon Nyagah

2016-01-01

The study investigated the relevance of secondary school learning opportunities in promoting national cohesion. The study was based on the ideals and principles of a school curriculum as advocated by Brameld Theodore on reconstructionism philosophy. Descriptive survey research design was adopted for the study. A sample size of four hundred and…

Learning Theories and Assessment Methodologies--An Engineering Educational Perspective

ERIC Educational Resources Information Center

Hassan, O. A. B.

2011-01-01

This paper attempts to critically review theories of learning from the perspective of engineering education in order to align relevant assessment methods with each respective learning theory, considering theoretical aspects and practical observations and reflections. The role of formative assessment, taxonomies, peer learning and educational…

Cross-Situational Word Learning in the Right Situations

ERIC Educational Resources Information Center

Dautriche, Isabelle; Chemla, Emmanuel

2014-01-01

Upon hearing a novel word, language learners must identify its correct meaning from a diverse set of situationally relevant options. Such referential ambiguity could be reduced through "repetitive" exposure to the novel word across diverging learning situations, a learning mechanism referred to as "cross-situational learning."…

Improving Learning in Rural Schools through Instructional Computing.

ERIC Educational Resources Information Center

Friedman, Daniel

Effective individualized learning continues to be the primary educational goal of small-rural schools. Three thrusts towards individualized learning--non-graded instruction, an emphasis on basic skills, and socially relevant education--can be enhanced by instructional computing, the use of microcomputers to facilitate learning. However, most…

Learning Radiological Appearances of Diseases: Does Comparison Help?

ERIC Educational Resources Information Center

Kok, Ellen M.; de Bruin, Anique B. H.; Robben, Simon G. F.; van Merrienboer, Jeroen J. G.

2013-01-01

Comparison learning is a promising approach for learning complex real-life visual tasks. When medical students study radiological appearances of diseases, comparison of images showing diseases with images showing no abnormalities could help them learn to discriminate relevant, disease-related information. Medical students studied 12 diseases on…

Iron oxide nanomatrix facilitating metal ionization in matrix-assisted laser desorption/ionization mass spectrometry.

PubMed

Obena, Rofeamor P; Lin, Po-Chiao; Lu, Ying-Wei; Li, I-Che; del Mundo, Florian; Arco, Susan dR; Nuesca, Guillermo M; Lin, Chung-Chen; Chen, Yu-Ju

2011-12-15

The significance and epidemiological effects of metals to life necessitate the development of direct, efficient, and rapid method of analysis. Taking advantage of its simple, fast, and high-throughput features, we present a novel approach to metal ion detection by matrix-functionalized magnetic nanoparticle (matrix@MNP)-assisted MALDI-MS. Utilizing 21 biologically and environmentally relevant metal ion solutions, the performance of core and matrix@MNP against conventional matrixes in MALDI-MS and laser desorption ionization (LDI) MS were systemically tested to evaluate the versatility of matrix@MNP as ionization element. The matrix@MNPs provided 20- to >100-fold enhancement on detection sensitivity of metal ions and unambiguous identification through characteristic isotope patterns and accurate mass (<5 ppm), which may be attributed to its multifunctional role as metal chelator, preconcentrator, absorber, and reservoir of energy. Together with the comparison on the ionization behaviors of various metals having different ionization potentials (IP), we formulated a metal ionization mechanism model, alluding to the role of exciton pooling in matrix@MNP-assisted MALDI-MS. Moreover, the detection of Cu in spiked tap water demonstrated the practicability of this new approach as an efficient and direct alternative tool for fast, sensitive, and accurate determination of trace metal ions in real samples.

Physical, Spatial, and Molecular Aspects of Extracellular Matrix of In Vivo Niches and Artificial Scaffolds Relevant to Stem Cells Research

PubMed Central

Akhmanova, Maria; Osidak, Egor; Domogatsky, Sergey; Rodin, Sergey; Domogatskaya, Anna

2015-01-01

Extracellular matrix can influence stem cell choices, such as self-renewal, quiescence, migration, proliferation, phenotype maintenance, differentiation, or apoptosis. Three aspects of extracellular matrix were extensively studied during the last decade: physical properties, spatial presentation of adhesive epitopes, and molecular complexity. Over 15 different parameters have been shown to influence stem cell choices. Physical aspects include stiffness (or elasticity), viscoelasticity, pore size, porosity, amplitude and frequency of static and dynamic deformations applied to the matrix. Spatial aspects include scaffold dimensionality (2D or 3D) and thickness; cell polarity; area, shape, and microscale topography of cell adhesion surface; epitope concentration, epitope clustering characteristics (number of epitopes per cluster, spacing between epitopes within cluster, spacing between separate clusters, cluster patterns, and level of disorder in epitope arrangement), and nanotopography. Biochemical characteristics of natural extracellular matrix molecules regard diversity and structural complexity of matrix molecules, affinity and specificity of epitope interaction with cell receptors, role of non-affinity domains, complexity of supramolecular organization, and co-signaling by growth factors or matrix epitopes. Synergy between several matrix aspects enables stem cells to retain their function in vivo and may be a key to generation of long-term, robust, and effective in vitro stem cell culture systems. PMID:26351461

Aspects of matrix effects in applications of liquid chromatography-mass spectrometry to forensic and clinical toxicology--a review.

PubMed

Peters, Frank T; Remane, Daniela

2012-06-01

In the last decade, liquid chromatography coupled to (tandem) mass spectrometry (LC-MS(-MS)) has become a versatile technique with many routine applications in clinical and forensic toxicology. However, it is well-known that ionization in LC-MS(-MS) is prone to so-called matrix effects, i.e., alteration in response due to the presence of co-eluting compounds that may increase (ion enhancement) or reduce (ion suppression) ionization of the analyte. Since the first reports on such matrix effects, numerous papers have been published on this matter and the subject has been reviewed several times. However, none of the existing reviews has specifically addressed aspects of matrix effects of particular interest and relevance to clinical and forensic toxicology, for example matrix effects in methods for multi-analyte or systematic toxicological analysis or matrix effects in (alternative) matrices almost exclusively analyzed in clinical and forensic toxicology, for example meconium, hair, oral fluid, or decomposed samples in postmortem toxicology. This review article will therefore focus on these issues, critically discussing experiments and results of matrix effects in LC-MS(-MS) applications in clinical and forensic toxicology. Moreover, it provides guidance on performance of studies on matrix effects in LC-MS(-MS) procedures in systematic toxicological analysis and postmortem toxicology.

Lessons Learned and Technical Standards: A Logical Marriage for Future Space Systems Design

NASA Technical Reports Server (NTRS)

Gill, Paul S.; Garcia, Danny; Vaughan, William W.; Parker, Nelson C. (Technical Monitor)

2002-01-01

A comprehensive database of engineering lessons learned that corresponds with relevant technical standards will be a valuable asset to those engaged in studies on future space vehicle developments, especially for structures, materials, propulsion, control, operations and associated elements. In addition, this will enable the capturing of technology developments applicable to the design, development, and operation of future space vehicles as planned in the Space Launch Initiative. Using the time-honored tradition of passing on lessons learned while utilizing the newest information technology, NASA has launched an intensive effort to link lessons learned acquired through various Internet databases with applicable technical standards. This paper will discuss the importance of lessons learned, the difficulty in finding relevant lessons learned while engaged in a space vehicle development, and the new NASA effort to relate them to technical standards that can help alleviate this difficulty.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

How category learning affects object representations: Not all morphspaces stretch alike

PubMed Central

Folstein, Jonathan R.; Gauthier, Isabel; Palmeri, Thomas J.

2012-01-01

How does learning to categorize objects affect how we visually perceive them? Behavioral, neurophysiological, and neuroimaging studies have tested the degree to which category learning influences object representations, with conflicting results. Some studies find that objects become more visually discriminable along dimensions relevant to previously learned categories, while others find no such effect. One critical factor we explore here lies in the structure of the morphspaces used in different studies. Studies finding no increase in discriminability often use “blended” morphspaces, with morphparents lying at corners of the space. By contrast, studies finding increases in discriminability use “factorial” morphspaces, defined by separate morphlines forming axes of the space. Using the same four morphparents, we created both factorial and blended morphspaces matched in pairwise discriminability. Category learning caused a selective increase in discriminability along the relevant dimension of the factorial space, but not in the blended space, and led to the creation of functional dimensions in the factorial space, but not in the blended space. These findings demonstrate that not all morphspaces stretch alike: Only some morphspaces support enhanced discriminability to relevant object dimensions following category learning. Our results have important implications for interpreting neuroimaging studies reporting little or no effect of category learning on object representations in the visual system: Those studies may have been limited by their use of blended morphspaces. PMID:22746950

On non-negative matrix factorization algorithms for signal-dependent noise with application to electromyography data

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H where V ~ WH. It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this paper, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse Gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness-of-fit on data. Our methods are demonstrated using experimental data from electromyography studies as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise. PMID:24684448

Convergence of the standard RLS method and UDUT factorisation of covariance matrix for solving the algebraic Riccati equation of the DLQR via heuristic approximate dynamic programming

NASA Astrophysics Data System (ADS)

Moraes Rêgo, Patrícia Helena; Viana da Fonseca Neto, João; Ferreira, Ernesto M.

2015-08-01

The main focus of this article is to present a proposal to solve, via UDUT factorisation, the convergence and numerical stability problems that are related to the covariance matrix ill-conditioning of the recursive least squares (RLS) approach for online approximations of the algebraic Riccati equation (ARE) solution associated with the discrete linear quadratic regulator (DLQR) problem formulated in the actor-critic reinforcement learning and approximate dynamic programming context. The parameterisations of the Bellman equation, utility function and dynamic system as well as the algebra of Kronecker product assemble a framework for the solution of the DLQR problem. The condition number and the positivity parameter of the covariance matrix are associated with statistical metrics for evaluating the approximation performance of the ARE solution via RLS-based estimators. The performance of RLS approximators is also evaluated in terms of consistence and polarisation when associated with reinforcement learning methods. The used methodology contemplates realisations of online designs for DLQR controllers that is evaluated in a multivariable dynamic system model.

Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control

NASA Astrophysics Data System (ADS)

Roslund, Jonathan; Shir, Ofer M.; Bäck, Thomas; Rabitz, Herschel

2009-10-01

Optimization of quantum systems by closed-loop adaptive pulse shaping offers a rich domain for the development and application of specialized evolutionary algorithms. Derandomized evolution strategies (DESs) are presented here as a robust class of optimizers for experimental quantum control. The combination of stochastic and quasi-local search embodied by these algorithms is especially amenable to the inherent topology of quantum control landscapes. Implementation of DES in the laboratory results in efficiency gains of up to ˜9 times that of the standard genetic algorithm, and thus is a promising tool for optimization of unstable or fragile systems. The statistical learning upon which these algorithms are predicated also provide the means for obtaining a control problem’s Hessian matrix with no additional experimental overhead. The forced optimal covariance adaptive learning (FOCAL) method is introduced to enable retrieval of the Hessian matrix, which can reveal information about the landscape’s local structure and dynamic mechanism. Exploitation of such algorithms in quantum control experiments should enhance their efficiency and provide additional fundamental insights.

Formulating face verification with semidefinite programming.

PubMed

Yan, Shuicheng; Liu, Jianzhuang; Tang, Xiaoou; Huang, Thomas S

2007-11-01

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.

Medical students' views about an undergraduate curriculum in psychiatry before and after clinical placements

PubMed Central

Oakley, Clare; Oyebode, Femi

2008-01-01

Background It has been suggested that medical students wish to focus their learning in psychiatry on general skills that are applicable to all doctors. This study seeks to establish what aspects of psychiatry students perceive to be relevant to their future careers and what psychiatric knowledge and skills they consider to be important. It is relevant to consider whether these expectations about learning needs vary prior to and post-placement in psychiatry. To what extent these opinions should influence curriculum development needs to be assessed. Methods A questionnaire was distributed to medical students before they commenced their psychiatry placement and after they had completed it. The questionnaire considered the relevance of psychiatry to their future careers, the relevance of particular knowledge and skills, the utility of knowledge of psychiatric specialties and the utility of different settings for learning psychiatry. Results The students felt skills relevant to all doctors, such as assessment of suicide risk, were more important than more specialist psychiatric skills, such as the management of schizophrenia. They felt that knowledge of how psychiatric illnesses present in general practice was important and it was a useful setting in which to learn psychiatry. They thought that conditions that are commonly seen in the general hospital are important and that liaison psychiatry was useful. Conclusion Two ways that medical students believe their teaching can be made more relevant to their future careers are highlighted in this study. Firstly, there is a need to focus on scenarios which students will commonly encounter in their initial years of employment. Secondly, psychiatry should be better integrated into the overall curriculum, with the opportunity for teaching in different settings. However, when developing curricula the need to listen to what students believe they should learn needs to be balanced against the necessity of teaching the fundamentals and principles of a speciality. PMID:18439278

Do Levels of Evidence Affect Breadth of Service? A Study on the Use of Clinical Guidance in a Learning Disability Service

ERIC Educational Resources Information Center

Pateraki, Eleni; Macmahon, Kenneth

2017-01-01

Abstract: For services across the UK, increasing emphasis is placed on the use of evidence-based psychological treatments. In this context, the Scottish Government published the MATRIX, a best-practice clinical governance document, with a brief section on therapies for people with learning disabilities. As with most clinical guidelines, randomised…

A Taxonomy for Choosing, Evaluating, and Integrating In-the-Cloud Resources in a University Environment

ERIC Educational Resources Information Center

Kahn, Russell L.

2013-01-01

This article develops and applies an analytic matrix for searching and using Web 2.0 resources along a learning continuum based on learning styles. This continuum applies core concepts of cognitive psychology, which places an emphasis on internal processes, such as motivation, thinking, attitudes, and reflection. A pilot study found that access to…

Introducing "The Matrix Classroom" University Course Design That Facilitates Active and Situated Learning though Creating Two Temporary Communities of Practice

ERIC Educational Resources Information Center

Roberts, Emma; Sayer, Karen

2017-01-01

This paper illustrates a radical course design structured to create active and situated learning in which students participate in communities of practice within the classroom, replicating real-life work situations. This paper illustrates the approach through a People Management module, but the approach is also used across a range of disciplines…

Being Ethically Minded: Practising the Scholarship of Teaching and Learning in an Ethical Manner

ERIC Educational Resources Information Center

Healey, Ruth L.; Bass, Tina; Caulfield, Jay; Hoffman, Adam; McGinn, Michelle K.; Miller-Young, Janice; Haigh, Martin

2013-01-01

The authors propose a working definition of ethical Scholarship of Teaching and Learning (SoTL), advance an ethical framework for SoTL inquiry, and present a case study that illustrates the complexity of ethical issues in SoTL. The Ethical SoTL Matrix is a flexible framework designed to support SoTL practitioners, particularly in the formative…

Application of Matrix Outcome Mapping to Constructively Align Program Outcomes and Course Outcomes in Higher Education

ERIC Educational Resources Information Center

Mazouz, Abdelkader; Crane, Keenan

2013-01-01

Establishing a link between Course Learning Outcomes (LOs) and Program Outcomes (POs) while assessing the course contents and delivery are among the most challenging issues in Higher Education. In the present study two forms were generated based on specific Course Learning Outcomes identified in the syllabus at the beginning of the teaching term:…

Mars-Learning AN Open Access Educational Database

NASA Astrophysics Data System (ADS)

Kolankowski, S. M.; Fox, P. A.

2016-12-01

Schools across America have begun focusing more and more on science and technology, giving their students greater opportunities to learn about planetary science and engineering. With the development of rovers and advanced scientific instrumentation, we are learning about Mars' geologic history on a daily basis. These discoveries are crucial to our understanding of Earth and our solar system. By bringing these findings into the classroom, students can learn key concepts about Earth and Planetary sciences while focusing on a relevant current event. However, with an influx of readily accessible information, it is difficult for educators and students to find accurate and relevant material. Mars-Learning seeks to unify these discoveries and resources. This site will provide links to educational resources, software, and blogs with a focus on Mars. Activities will be grouped by grade for the middle and high school levels. Programs and software will be labeled, open access, free, or paid to ensure users have the proper tools to get the information they need. For new educators or those new to the subject, relevant blogs and pre-made lesson plans will be available so instructors can ensure their success. The expectation of Mars-Learning is to provide stress-free access to learning materials that falls within a wide range of curriculum. By providing a thorough and encompassing site, Mars-Learning hopes to further our understanding of the Red Planet and equip students with the knowledge and passion to continue this research.

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

2015-01-01

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359

Evolutionary perspectives on learning: conceptual and methodological issues in the study of adaptive specializations.

PubMed

Krause, Mark A

2015-07-01

Inquiry into evolutionary adaptations has flourished since the modern synthesis of evolutionary biology. Comparative methods, genetic techniques, and various experimental and modeling approaches are used to test adaptive hypotheses. In psychology, the concept of adaptation is broadly applied and is central to comparative psychology and cognition. The concept of an adaptive specialization of learning is a proposed account for exceptions to general learning processes, as seen in studies of Pavlovian conditioning of taste aversions, sexual responses, and fear. The evidence generally consists of selective associations forming between biologically relevant conditioned and unconditioned stimuli, with conditioned responses differing in magnitude, persistence, or other measures relative to non-biologically relevant stimuli. Selective associations for biologically relevant stimuli may suggest adaptive specializations of learning, but do not necessarily confirm adaptive hypotheses as conceived of in evolutionary biology. Exceptions to general learning processes do not necessarily default to an adaptive specialization explanation, even if experimental results "make biological sense". This paper examines the degree to which hypotheses of adaptive specializations of learning in sexual and fear response systems have been tested using methodologies developed in evolutionary biology (e.g., comparative methods, quantitative and molecular genetics, survival experiments). A broader aim is to offer perspectives from evolutionary biology for testing adaptive hypotheses in psychological science.

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

2015-10-01

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

Is Implicit Motor Learning Preserved after Stroke? A Systematic Review with Meta-Analysis

PubMed Central

Kal, E.; Winters, M.; van der Kamp, J.; Houdijk, H.; Groet, E.; van Bennekom, C.; Scherder, E.

2016-01-01

Many stroke patients experience difficulty with performing dual-tasks. A promising intervention to target this issue is implicit motor learning, as it should enhance patients’ automaticity of movement. Yet, although it is often thought that implicit motor learning is preserved post-stroke, evidence for this claim has not been systematically analysed yet. Therefore, we systematically reviewed whether implicit motor learning is preserved post-stroke, and whether patients benefit more from implicit than from explicit motor learning. We comprehensively searched conventional (MEDLINE, Cochrane, Embase, PEDro, PsycINFO) and grey literature databases (BIOSIS, Web of Science, OpenGrey, British Library, trial registries) for relevant reports. Two independent reviewers screened reports, extracted data, and performed a risk of bias assessment. Overall, we included 20 out of the 2177 identified reports that allow for a succinct evaluation of implicit motor learning. Of these, only 1 study investigated learning on a relatively complex, whole-body (balance board) task. All 19 other studies concerned variants of the serial-reaction time paradigm, with most of these focusing on learning with the unaffected hand (N = 13) rather than the affected hand or both hands (both: N = 4). Four of the 20 studies compared explicit and implicit motor learning post-stroke. Meta-analyses suggest that patients with stroke can learn implicitly with their unaffected side (mean difference (MD) = 69 ms, 95% CI[45.1, 92.9], p < .00001), but not with their affected side (standardized MD = -.11, 95% CI[-.45, .25], p = .56). Finally, implicit motor learning seemed equally effective as explicit motor learning post-stroke (SMD = -.54, 95% CI[-1.37, .29], p = .20). However, overall, the high risk of bias, small samples, and limited clinical relevance of most studies make it impossible to draw reliable conclusions regarding the effect of implicit motor learning strategies post-stroke. High quality studies with larger samples are warranted to test implicit motor learning in clinically relevant contexts. PMID:27992442

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

PubMed

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

Integrative and Deep Learning through a Learning Community: A Process View of Self

ERIC Educational Resources Information Center

Mahoney, Sandra; Schamber, Jon

2011-01-01

This study investigated deep learning produced in a community of general education courses. Student speeches on liberal education were analyzed for discovering a grounded theory of ideas about self. The study found that learning communities cultivate deep, integrative learning that makes the value of a liberal education relevant to students.…

Lifelong Learning and Learning to Learn: An Enabler of New Voices for the New Times

ERIC Educational Resources Information Center

Lee, Wing On

2014-01-01

Over the last two decades, there have been numerous attempts to review and re-examine whether conventional learning and teaching approaches are still useful or relevant. Lifelong learning institutions have grown in number and scope, and now fulfil a significant function in bridging the gap between what traditional formal education systems provide…

Learning a New Language is "Like Swiss Cheese": Learning to Learn English

ERIC Educational Resources Information Center

Larrotta, Clarena; Moon, Ji Yoon Christine; Huang, Jiuhan

2016-01-01

The purpose of the study was to understand instructors' viewpoints on the relevance of learning to learn (L2L) in the settings where they teach. Twenty-four instructors answered an online qualitative survey about their experiences teaching English to adults. Data analysis was informed by narrative analysis procedures. Study findings include…

Physiological Ranges of Matrix Rigidity Modulate Primary Mouse Hepatocyte Function In Part Through Hepatocyte Nuclear Factor 4 Alpha

PubMed Central

Desai, Seema S.; Tung, Jason C.; Zhou, Vivian X.; Grenert, James P.; Malato, Yann; Rezvani, Milad; Español-Suñer, Regina; Willenbring, Holger; Weaver, Valerie M.; Chang, Tammy T.

2016-01-01

Matrix rigidity has important effects on cell behavior and is increased during liver fibrosis; however, its effect on primary hepatocyte function is unknown. We hypothesized that increased matrix rigidity in fibrotic livers would activate mechanotransduction in hepatocytes and lead to inhibition of hepatic-specific functions. To determine the physiologically relevant ranges of matrix stiffness at the cellular level, we performed detailed atomic force microscopy analysis across liver lobules from normal and fibrotic livers. We determined that normal liver matrix stiffness was around 150Pa and increased to 1–6kPa in areas near fibrillar collagen deposition in fibrotic livers. In vitro culture of primary hepatocytes on collagen matrix of tunable rigidity demonstrated that fibrotic levels of matrix stiffness had profound effects on cytoskeletal tension and significantly inhibited hepatocyte-specific functions. Normal liver stiffness maintained functional gene regulation by hepatocyte nuclear factor 4 alpha (HNF4α) whereas fibrotic matrix stiffness inhibited the HNF4α transcriptional network. Fibrotic levels of matrix stiffness activated mechanotransduction in primary hepatocytes through focal adhesion kinase (FAK). In addition, blockade of the Rho/Rho-associated protein kinase (ROCK) pathway rescued HNF4α expression from hepatocytes cultured on stiff matrix. Conclusion Fibrotic levels of matrix stiffness significantly inhibit hepatocyte-specific functions in part by inhibiting the HNF4α transcriptional network mediated through the Rho/ROCK pathway. Increased appreciation of the role of matrix rigidity in modulating hepatocyte function will advance our understanding of the mechanisms of hepatocyte dysfunction in liver cirrhosis and spur development of novel treatments for chronic liver disease. PMID:26755329

Summative Evaluation of the Learning Initiatives Program (LIP). Final Report

ERIC Educational Resources Information Center

Human Resources and Skills Development Canada, 2005

2005-01-01

The Learning Initiatives Program (LIP), formerly the Learning Initiatives Fund (LIF), is a contribution program which was established in 1994 to encourage and support initiatives that contribute to the development of a results-oriented, accessible, relevant and accountable learning system in Canada. Through this program, Human Resources and Skills…

Legal Issues in Experiential Education. PANEL Resource Paper #3.

ERIC Educational Resources Information Center

Goldstein, Michael B.

Legal issues relevant to experiential learning are identified to help program administrators know when to seek expert assistance and advice. Much of the law of experiential learning is based on specific statutory provisions and decisions. The student involved in experiential learning may assume certain learning outcomes very different from those…

Reconceptualizing Homework as Out-of-School Learning Opportunities. Occasional Paper 135.

ERIC Educational Resources Information Center

Alleman, Janet; Brophy, Jere

This occasional paper views homework as an opportunity to exploit the potential of outside resources and environments to complement in-school learning opportunities. Out-of-school learning opportunities are considered in the context of principles for planning and implementing learning activities. These principles involve: (1) goal relevance,…

Competence-Based Knowledge Structures for Personalised Learning

ERIC Educational Resources Information Center

Heller, Jurgen; Steiner, Christina; Hockemeyer, Cord; Albert, Dietrich

2006-01-01

Competence-based extensions of Knowledge Space Theory are suggested as a formal framework for implementing key features of personalised learning in technology-enhanced learning. The approach links learning objects and assessment problems to the relevant skills that are taught or required. Various ways to derive these skills from domain ontologies…

Lifelong Learning to Empowerment: Beyond Formal Education

ERIC Educational Resources Information Center

Carr, Alexis; Balasubramanian, K.; Atieno, Rosemary; Onyango, James

2018-01-01

This paper discusses the relevance of lifelong learning vis-à-vis the Sustainable Development Goals (SDGs) and stresses the need for an approach blending formal education, non-formal and informal learning. The role of Open and Distance Learning (ODL) in moving beyond formal education and the importance of integrating pedagogy, andragogy and…

POLizied e-Learning Using Contract Management

ERIC Educational Resources Information Center

Espinosa, Espinosa David; Noguez, Julieta; Benes, Bedrich; Bueno, Abel

2005-01-01

We present an innovative way to manage online learning by administering experiential learning activities during a semester-long, web-based course that is designed with the Project Oriented Learning methodology. A consulting-style guiding thread for in-class and remote workshops is implemented using a professionally relevant project contract that…

Seeing Is as Good as Doing

ERIC Educational Resources Information Center

Osman, Magda

2008-01-01

Given the privileged status claimed for active learning in a variety of domains (visuomotor learning, causal induction, problem solving, education, skill learning), the present study examines whether action-based learning is a necessary, or a sufficient, means of acquiring the relevant skills needed to perform a task typically described as…

Semantic Learning Modifies Perceptual Face Processing

ERIC Educational Resources Information Center

Heisz, Jennifer J.; Shedden, Judith M.

2009-01-01

Face processing changes when a face is learned with personally relevant information. In a five-day learning paradigm, faces were presented with rich semantic stories that conveyed personal information about the faces. Event-related potentials were recorded before and after learning during a passive viewing task. When faces were novel, we observed…

Designing worked examples for learning tangent lines to circles

NASA Astrophysics Data System (ADS)

Retnowati, E.; Marissa

2018-03-01

Geometry is a branch of mathematics that deals with shape and space, including the circle. A difficult topic in the circle may be the tangent line to circle. This is considered a complex material since students have to simultaneously apply several principles to solve the problems, these are the property of circle, definition of the tangent, measurement and Pythagorean theorem. This paper discusses designs of worked examples for learning tangent line to circles and how to apply this design to an effective and efficient instructional activity. When students do not have sufficient prior knowledge, solving tangent problems might be clumsy, and as a consequence, the problem-solving activity hinders learning. According to a Cognitive Load Theory, learning occurs when students can construct new knowledge based on the relevant knowledge previously learned. When the relevant knowledge is unavailable, providing students with the worked example is suggested. Worked example may reduce unproductive process during learning that causes extraneous cognitive load. Nevertheless, worked examples must be created in such a way facilitate learning.

Benefits of fading in perceptual learning are driven by more than dimensional attention.

PubMed

Wisniewski, Matthew G; Radell, Milen L; Church, Barbara A; Mercado, Eduardo

2017-01-01

Individuals learn to classify percepts effectively when the task is initially easy and then gradually increases in difficulty. Some suggest that this is because easy-to-discriminate events help learners focus attention on discrimination-relevant dimensions. Here, we tested whether such attentional-spotlighting accounts are sufficient to explain easy-to-hard effects in auditory perceptual learning. In two experiments, participants were trained to discriminate periodic, frequency-modulated (FM) tones in two separate frequency ranges (300-600 Hz or 3000-6000 Hz). In one frequency range, sounds gradually increased in similarity as training progressed. In the other, stimulus similarity was constant throughout training. After training, participants showed better performance in their progressively trained frequency range, even though the discrimination-relevant dimension across ranges was the same. Learning theories that posit experience-dependent changes in stimulus representations and/or the strengthening of associations with differential responses, predict the observed specificity of easy-to-hard effects, whereas attentional-spotlighting theories do not. Calibrating the difficulty and temporal sequencing of training experiences to support more incremental representation-based learning can enhance the effectiveness of practice beyond any benefits gained from explicitly highlighting relevant dimensions.

Residents' perceptions of an integrated longitudinal curriculum: a qualitative study.

PubMed

Lubitz, Rebecca; Lee, Joseph; Hillier, Loretta M

2015-01-01

The purpose of this study was to explore family medicine residents' perceptions of a newly restructured integrated longitudinal curriculum. A purposeful sample of 16 family medicine residents participated in focus group interviews conducted from a grounded theory perspective to identify the characteristics of this training model that contribute to and that challenge learning. Eight key themes were identified: continuity of care, relevance to family medicine, autonomy, program-focused preparation, professional development as facilitated by role modeling, patient volume, clarity of expectations for learners, and logistics. Positive learning experiences were marked by high levels of autonomy, continuity, and relevance to family medicine. Less favorable learning experiences were characterized by limited opportunities for continuity of care, limited relevance to family medicine practice and unclear expectations for the resident's role. Family physician-led learning experiences contributed to residents' understanding of the full scope of family medicine practice, more so than specialist-led experiences. The logistics of implementing the integrated block were challenging and negatively impacted continuity and learning. This study suggests that an integrated longitudinalized family medicine block training model has the potential to support the principles of a longitudinal integrated competency-based curriculum to effectively prepare residents for family medicine practice.

The Existence of the Solution to One Kind of Algebraic Riccati Equation

NASA Astrophysics Data System (ADS)

Liu, Jianming

2018-03-01

The matrix equation ATX + XA + XRX + Q = O is called algebraic Riccati equation, which is very important in the fields of automatic control and other engineering applications. Many researchers have studied the solutions to various algebraic Riccati equations and most of them mainly applied the matrix methods, while few used the functional analysis theories. This paper mainly studies the existence of the solution to the following kind of algebraic Riccati equation from the functional view point: ATX + XA + XRX ‑λX + Q = O Here, X, A, R, Q ∈ n×n , Q is a symmetric matrix, and R is a positive or negative semi-definite matrix, λ is arbitrary constants. This paper uses functional approach such as fixed point theorem and contraction mapping thinking so as to provide two sufficient conditions for the solvability about this kind of Riccati equation and to arrive at some relevant conclusions.

Material properties of biofilms – key methods for understanding permeability and mechanics

PubMed Central

Billings, Nicole; Birjiniuk, Alona; Samad, Tahoura S.; Doyle, Patrick S.; Ribbeck, Katharina

2015-01-01

Microorganisms can form biofilms, which are multicellular communities surrounded by a hydrated extracellular matrix of polymers. Central properties of the biofilm are governed by this extracellular matrix, which provides mechanical stability to the three-dimensional biofilm structure, regulates the ability of the biofilm to adhere to surfaces, and determines the ability of the biofilm to adsorb gasses, solutes, and foreign cells. Despite their critical relevance for understanding and eliminating of biofilms, the materials properties of the extracellular matrix are understudied. Here, we offer the reader a guide to current technologies that can be utilized to specifically assess the permeability and mechanical properties of the biofilm matrix and its interacting components. In particular, we highlight technological advances in instrumentation and interactions between multiple disciplines that have broadened the spectrum of methods available to conduct these studies. We review pioneering work that furthers our understanding of the material properties of biofilms. PMID:25719969

Material properties of biofilms—a review of methods for understanding permeability and mechanics

NASA Astrophysics Data System (ADS)

Billings, Nicole; Birjiniuk, Alona; Samad, Tahoura S.; Doyle, Patrick S.; Ribbeck, Katharina

2015-02-01

Microorganisms can form biofilms, which are multicellular communities surrounded by a hydrated extracellular matrix of polymers. Central properties of the biofilm are governed by this extracellular matrix, which provides mechanical stability to the 3D biofilm structure, regulates the ability of the biofilm to adhere to surfaces, and determines the ability of the biofilm to adsorb gases, solutes, and foreign cells. Despite their critical relevance for understanding and eliminating of biofilms, the materials properties of the extracellular matrix are understudied. Here, we offer the reader a guide to current technologies that can be utilized to specifically assess the permeability and mechanical properties of the biofilm matrix and its interacting components. In particular, we highlight technological advances in instrumentation and interactions between multiple disciplines that have broadened the spectrum of methods available to conduct these studies. We review pioneering work that furthers our understanding of the material properties of biofilms.

Extracellular matrix structure.

PubMed

Theocharis, Achilleas D; Skandalis, Spyros S; Gialeli, Chrysostomi; Karamanos, Nikos K

2016-02-01

Extracellular matrix (ECM) is a non-cellular three-dimensional macromolecular network composed of collagens, proteoglycans/glycosaminoglycans, elastin, fibronectin, laminins, and several other glycoproteins. Matrix components bind each other as well as cell adhesion receptors forming a complex network into which cells reside in all tissues and organs. Cell surface receptors transduce signals into cells from ECM, which regulate diverse cellular functions, such as survival, growth, migration, and differentiation, and are vital for maintaining normal homeostasis. ECM is a highly dynamic structural network that continuously undergoes remodeling mediated by several matrix-degrading enzymes during normal and pathological conditions. Deregulation of ECM composition and structure is associated with the development and progression of several pathologic conditions. This article emphasizes in the complex ECM structure as to provide a better understanding of its dynamic structural and functional multipotency. Where relevant, the implication of the various families of ECM macromolecules in health and disease is also presented. Copyright © 2015 Elsevier B.V. All rights reserved.

Specialisation of extracellular matrix for function in tendons and ligaments

PubMed Central

Birch, Helen L.; Thorpe, Chavaunne T.; Rumian, Adam P.

2013-01-01

Summary Tendons and ligaments are similar structures in terms of their composition, organisation and mechanical properties. The distinction between them stems from their anatomical location; tendons form a link between muscle and bone while ligaments link bones to bones. A range of overlapping functions can be assigned to tendon and ligaments and each structure has specific mechanical properties which appear to be suited for particular in vivo function. The extracellular matrix in tendon and ligament varies in accordance with function, providing appropriate mechanical properties. The most useful framework in which to consider extracellular matrix differences therefore is that of function rather than anatomical location. In this review we discuss what is known about the relationship between functional requirements, structural properties from molecular to gross level, cellular gene expression and matrix turnover. The relevance of this information is considered by reviewing clinical aspects of tendon and ligament repair and reconstructive procedures. PMID:23885341

Scaffolding scientific discussion using socially relevant representations in networked multimedia

NASA Astrophysics Data System (ADS)

Hoadley, Christopher M.

1999-11-01

How do students make use of social cues when learning on the computer? This work examines how students in a middle-school science course learned through on-line peer discussion. Cognitive accounts of collaboration stress interacting with ideas, while socially situated accounts stress the interpersonal context. The design of electronic environments allows investigation into the interrelation of cognitive and social dimensions. I use on-line peer discussion to investigate how socially relevant representations in interfaces can aid learning. First, I identify some of the variables that affect individual participation in on-line discussion, including interface features. Individual participation is predicted by student attitudes towards learning from peers. Second, I describe the range of group outcomes for these on-line discussions. There is a large effect of discussion group on learning outcomes which is not reducible to group composition or gross measures of group process. Third, I characterize how students (individually) construct understanding from these group discussions. Learning in the on-line discussions is shown to be a result of sustained interaction over time, not merely encountering or expressing ideas. Experimental manipulations in the types of social cues available to students suggest that many students do use socially relevant representations to support their understanding of multiple viewpoints and science reasoning. Personalizing scientific disputes can afford reflection on the nature of scientific discovery and advance. While there are many individual differences in how social representations are used by students in learning, overall learning benefits for certain social representations can be shown. This work has profound implications for design of collaborative instructional methods, equitable access to science learning, design of instructional technology, and understanding of learning and cognition in social settings.

Teaching nursing research to undergraduates: a text analysis of instructors' intentions.

PubMed

Porter, Eileen J; Mansour, Tamam B

2003-04-01

Reviews of teaching strategies for undergraduate nursing research have been organized according to (a) the type of learning to be achieved, such as learning by doing, or (b) the specific teaching strategy, such as a poster session. For this text analysis, a new tack was taken to reveal the intentions of undergraduate nursing research instructors for student learning. Giorgi's (1985) descriptive phenomenological method was used to analyze 77 narrative reports of instructors about research teaching strategies. Seven intentions were identified, including desensitizing negative perceptions about research and stimulating collaborative learning about research. The intentions were contrasted in scope and relevance to frameworks organized according to learning goals or teaching techniques. The relevance of the intentions was considered in relation to critical trends influencing nursing education. Copyright 2003 Wiley Periodicals, Inc. Res Nurs Health 26:128-142, 2003

Assembly and Development of the Pseudomonas aeruginosa Biofilm Matrix

PubMed Central

Ma, Luyan; Conover, Matthew; Lu, Haiping; Parsek, Matthew R.; Bayles, Kenneth; Wozniak, Daniel J.

2009-01-01

Virtually all cells living in multicellular structures such as tissues and organs are encased in an extracellular matrix. One of the most important features of a biofilm is the extracellular polymeric substance that functions as a matrix, holding bacterial cells together. Yet very little is known about how the matrix forms or how matrix components encase bacteria during biofilm development. Pseudomonas aeruginosa forms environmentally and clinically relevant biofilms and is a paradigm organism for the study of biofilms. The extracellular polymeric substance of P. aeruginosa biofilms is an ill-defined mix of polysaccharides, nucleic acids, and proteins. Here, we directly visualize the product of the polysaccharide synthesis locus (Psl exopolysaccharide) at different stages of biofilm development. During attachment, Psl is anchored on the cell surface in a helical pattern. This promotes cell–cell interactions and assembly of a matrix, which holds bacteria in the biofilm and on the surface. Chemical dissociation of Psl from the bacterial surface disrupted the Psl matrix as well as the biofilm structure. During biofilm maturation, Psl accumulates on the periphery of 3-D-structured microcolonies, resulting in a Psl matrix-free cavity in the microcolony center. At the dispersion stage, swimming cells appear in this matrix cavity. Dead cells and extracellular DNA (eDNA) are also concentrated in the Psl matrix-free area. Deletion of genes that control cell death and autolysis affects the formation of the matrix cavity and microcolony dispersion. These data provide a mechanism for how P. aeruginosa builds a matrix and subsequently a cavity to free a portion of cells for seeding dispersal. Direct visualization reveals that Psl is a key scaffolding matrix component and opens up avenues for therapeutics of biofilm-related complications. PMID:19325879

Assembly and development of the Pseudomonas aeruginosa biofilm matrix.

PubMed

Ma, Luyan; Conover, Matthew; Lu, Haiping; Parsek, Matthew R; Bayles, Kenneth; Wozniak, Daniel J

2009-03-01

Virtually all cells living in multicellular structures such as tissues and organs are encased in an extracellular matrix. One of the most important features of a biofilm is the extracellular polymeric substance that functions as a matrix, holding bacterial cells together. Yet very little is known about how the matrix forms or how matrix components encase bacteria during biofilm development. Pseudomonas aeruginosa forms environmentally and clinically relevant biofilms and is a paradigm organism for the study of biofilms. The extracellular polymeric substance of P. aeruginosa biofilms is an ill-defined mix of polysaccharides, nucleic acids, and proteins. Here, we directly visualize the product of the polysaccharide synthesis locus (Psl exopolysaccharide) at different stages of biofilm development. During attachment, Psl is anchored on the cell surface in a helical pattern. This promotes cell-cell interactions and assembly of a matrix, which holds bacteria in the biofilm and on the surface. Chemical dissociation of Psl from the bacterial surface disrupted the Psl matrix as well as the biofilm structure. During biofilm maturation, Psl accumulates on the periphery of 3-D-structured microcolonies, resulting in a Psl matrix-free cavity in the microcolony center. At the dispersion stage, swimming cells appear in this matrix cavity. Dead cells and extracellular DNA (eDNA) are also concentrated in the Psl matrix-free area. Deletion of genes that control cell death and autolysis affects the formation of the matrix cavity and microcolony dispersion. These data provide a mechanism for how P. aeruginosa builds a matrix and subsequently a cavity to free a portion of cells for seeding dispersal. Direct visualization reveals that Psl is a key scaffolding matrix component and opens up avenues for therapeutics of biofilm-related complications.

Toward the optimization of normalized graph Laplacian.

PubMed

Xie, Bo; Wang, Meng; Tao, Dacheng

2011-04-01

Normalized graph Laplacian has been widely used in many practical machine learning algorithms, e.g., spectral clustering and semisupervised learning. However, all of them use the Euclidean distance to construct the graph Laplacian, which does not necessarily reflect the inherent distribution of the data. In this brief, we propose a method to directly optimize the normalized graph Laplacian by using pairwise constraints. The learned graph is consistent with equivalence and nonequivalence pairwise relationships, and thus it can better represent similarity between samples. Meanwhile, our approach, unlike metric learning, automatically determines the scale factor during the optimization. The learned normalized Laplacian matrix can be directly applied in spectral clustering and semisupervised learning algorithms. Comprehensive experiments demonstrate the effectiveness of the proposed approach.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Better Formative Assessment

ERIC Educational Resources Information Center

Clinchot, Michael; Ngai, Courtney; Huie, Robert; Talanquer, Vicente; Lambertz, Jennifer; Banks, Gregory; Weinrich, Melissa; Lewis, Rebecca; Pelletier, Pamela; Sevian, Hannah

2017-01-01

Formative assessment has been defined as the process "to recognize and respond to student learning to enhance that learning during the learning." Formative assessment helps teachers identify strengths and weaknesses in their students' understanding, focuses students' attention on relevant information and ideas, and provides scaffolds…

Relevance: Cornerstone for Constructing Meaning

ERIC Educational Resources Information Center

Cardell, Melanie

2005-01-01

Relevance has been called the "What's In It For Me" (WIIFM) issue. If there is not something in the content that the learner really needs, then they normally do not want to be bothered with it. Relevance is learner driven, but must be teacher provided for optimal learning. Relevance is so important to the making of meaning that Eric Jensen (1996,…

Something for Everyone: Learning and Learning Technologies in a Public Library

ERIC Educational Resources Information Center

Blackburn, Fiona

2010-01-01

The nature of learning in a public library is relevant to what place e-learning and social networking technologies might have there. That a public library aims to provide something for everyone is also important. Alice Springs Public Library (ASPL) is a place of learning; it is also a crowded and composite space. The use ASPL could make of the…

A Rule-Based System for Hybrid Search and Delivery of Learning Objects to Learners

ERIC Educational Resources Information Center

Biletskiy, Yevgen; Baghi, Hamidreza; Steele, Jarrett; Vovk, Ruslan

2012-01-01

Purpose: Presently, searching the internet for learning material relevant to ones own interest continues to be a time-consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is…

How Multi-Levels of Individual and Team Learning Interact in a Public Healthcare Organisation: A Conceptual Framework

ERIC Educational Resources Information Center

Doyle, Louise; Kelliher, Felicity; Harrington, Denis

2016-01-01

The aim of this paper is to review the relevant literature on organisational learning and offer a preliminary conceptual framework as a basis to explore how the multi-levels of individual learning and team learning interact in a public healthcare organisation. The organisational learning literature highlights a need for further understanding of…

Intrinsic Motivation of Chinese Learning in Predicting Online Learning Self-Efficacy and Flow Experience Relevant to Students' Learning Progress

ERIC Educational Resources Information Center

Hong, Jon-Chao; Hwang, Ming-Yueh; Tai, Kai-Hsin; Lin, Pei-Hsin

2017-01-01

Students of Southeast Asian Heritage Learning Chinese (SSAHLC) in Taiwan have frequently demonstrated difficulty with traditional Chinese (a graphical character) radical recognition due to their limited exposure to the written language form since childhood. In this study, we designed a Chinese radical learning game (CRLG), which adopted a drill…

Spectral Approaches to Learning Predictive Representations

DTIC Science & Technology

2012-09-01

conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed...to the mean to form an initial prediction of x̂(ht). Similarly, Equation 2.3b can be interpreted as using the dynamics matrix A and error covarianceQ...in the sense of Lyapunov if its dynamics matrix A is. Thus, the Lyapunov criterion can be interpreted as holding for an LDS if, for a given covariance

Architecture studies and system demonstrations for optical parallel processor for AI and NI

NASA Astrophysics Data System (ADS)

Lee, Sing H.

1988-03-01

In solving deterministic AI problems the data search for matching the arguments of a PROLOG expression causes serious bottleneck when implemented sequentially by electronic systems. To overcome this bottleneck we have developed the concepts for an optical expert system based on matrix-algebraic formulation, which will be suitable for parallel optical implementation. The optical AI system based on matrix-algebraic formation will offer distinct advantages for parallel search, adult learning, etc.

Parsimonious extreme learning machine using recursive orthogonal least squares.

PubMed

Wang, Ning; Er, Meng Joo; Han, Min

2014-10-01

Novel constructive and destructive parsimonious extreme learning machines (CP- and DP-ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure and excellent generalization of multiinput-multioutput single hidden-layer feedforward networks (SLFNs) are obtained. The proposed ELMs are developed by innovative decomposition of the recursive orthogonal least squares procedure into sequential partial orthogonalization (SPO). The salient features of the proposed approaches are as follows: 1) Initial hidden nodes are randomly generated by the ELM methodology and recursively orthogonalized into an upper triangular matrix with dramatic reduction in matrix size; 2) the constructive SPO in the CP-ELM focuses on the partial matrix with the subcolumn of the selected regressor including nonzeros as the first column while the destructive SPO in the DP-ELM operates on the partial matrix including elements determined by the removed regressor; 3) termination criteria for CP- and DP-ELM are simplified by the additional residual error reduction method; and 4) the output weights of the SLFN need not be solved in the model selection procedure and is derived from the final upper triangular equation by backward substitution. Both single- and multi-output real-world regression data sets are used to verify the effectiveness and superiority of the CP- and DP-ELM in terms of parsimonious architecture and generalization accuracy. Innovative applications to nonlinear time-series modeling demonstrate superior identification results.

Inter-class sparsity based discriminative least square regression.

PubMed

Wen, Jie; Xu, Yong; Li, Zuoyong; Ma, Zhongli; Xu, Yuanrong

2018-06-01

Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero-one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero-one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

The State of E-Learning in Canada

ERIC Educational Resources Information Center

Canadian Council on Learning, 2009

2009-01-01

The objective of this report is to improve Canadians' understanding of e-learning--particularly of the challenges, limitations and benefits--so that Canada may move forward in appropriate and relevant ways. Levels of adoption of e-learning have been significantly slower than predicted. This report also identifies areas related to e-learning where…

Professional Learning in Higher Education: Making Good Practice Relevant

ERIC Educational Resources Information Center

Daniels, Jeannie

2017-01-01

Professionals working in a range of contexts are increasingly expected to engage in ongoing professional learning to maintain their skills and develop their practices. In this paper, I focus on professional learning in Higher Education and challenge the standardisation of professional learning that is becoming prevalent in a number of countries. I…

Adult Learning Principles and Their Application to Program Planning.

ERIC Educational Resources Information Center

Brundage, Donald H.; MacKeracher, Dorothy

This report examined adult learning principles which were developed through an analysis and synthesis of the literature in adult education, andragogy, teaching and learning, and other related fields. The report consists of six sections. The first section deals with background assumptions relevant to the field of adult education and adult learning.…

Validation of a Spanish Version of the Distance Education Learning Environments Survey (DELES) in Spain

ERIC Educational Resources Information Center

Fernández-Pascual, Maria Dolores; Ferrer-Cascales, Rosario; Reig-Ferrer, Abilio; Albaladejo-Blázquez, Natalia; Walker, Scott L.

2015-01-01

The aim of this study was to examine the validity of the Spanish version of the Distance Education Learning Environments Survey (Sp-DELES). This instrument assesses students' perceptions of virtual learning environments using six scales: Instructor Support, Student Interaction and Collaboration, Personal Relevance, Authentic Learning, Active…

Developing a Lifelong Learning System in Ethiopia: Contextual Considerations and Propositions

ERIC Educational Resources Information Center

Abiy, Dessalegn Samuel; Kabeta, Genet Gelana; Mihiretie, Dawit Mekonnen

2014-01-01

Initiated by a "Pilot workshop on developing capacity for establishing lifelong learning systems in UNESCO Member States" held at the UNESCO Institute for Lifelong Learning, the purpose of this study was to develop a Lifelong Learning system in Ethiopia. Preparations for its conceptualisation included the review of relevant national…

Connectivist Learning Objects and Learning Styles

ERIC Educational Resources Information Center

del Moral, M. Esther; Cernea, Ana; Villalustre, Lourdes

2013-01-01

The Web 2.0 brought in the use of social tools at a large scale in every area: a transformation which led to redefining the teaching-learning process. In this new context knowledge is distributed over network connections in an uncontrolled way - thus learning consists of recognizing relevant information patterns and constructing new connections.…

Student Use and Pedagogical Impact of a Mobile Learning Application

ERIC Educational Resources Information Center

Teri, Saskia; Acai, Anita; Griffith, Douglas; Mahmoud, Qusay; Ma, David W. L.; Newton, Genevieve

2014-01-01

Mobile learning (m-learning) is a relevant innovation in teaching and learning in higher education. A mobile app called NutriBiochem was developed for use in biochemistry and nutrition education for students in a second year Biochemistry and Metabolism course. NutriBiochem was accessed through smartphones, tablets, or computers. Students were…

Does a Strategy Training Foster Students' Ability to Learn from Multimedia?

ERIC Educational Resources Information Center

Scheiter, Katharina; Schubert, Carina; Gerjets, Peter; Stalbovs, Kim

2015-01-01

Despite the general effectiveness of multimedia instruction, students do not always benefit from it. This study examined whether students' learning from multimedia can be improved by teaching them relevant learning strategies. On the basis of current theories and research on multimedia learning, the authors developed a strategy training for…

Learning by Example: Designing and Developing Linked Data Application

ERIC Educational Resources Information Center

Tharani, Karim

2016-01-01

According to constructivist theory of learning, new knowledge is learned on the basis of what is already known by learners. Thus for an emerging and transformative technology such as Linked Data to be learned, the technology must be made relevant for learners and must be compatible with their skillset. Designing and developing Linked Data…

Technology in the Classroom: Using Video Links to Enable Long Distance Experiential Learning

ERIC Educational Resources Information Center

Chilton, Michael A.

2012-01-01

The experiential learning process is a method by which students learn from direct exposure to relevant applications within the discipline being taught. One way in which MIS students can benefit from experiential learning occurs when organizations in some way sponsor curricular outcomes. Sponsorship can range from classroom visits during which…

The Role of Language Learning Progressions in Improved Instruction and Assessment of English Language Learners

ERIC Educational Resources Information Center

Bailey, Alison L.; Heritage, Margaret

2014-01-01

This article addresses theoretical and empirical issues relevant for the development and evaluation of language learning progressions. The authors explore how learning progressions aligned with new content standards can form a central basis of efforts to describe the English language needed in school contexts for learning, instruction, and…

An Analytic Framework to Support E.Learning Strategy Development

ERIC Educational Resources Information Center

Marshall, Stephen J.

2012-01-01

Purpose: The purpose of this paper is to discuss and demonstrate the relevance of a new conceptual framework for leading and managing the development of learning and teaching to e.learning strategy development. Design/methodology/approach: After reviewing and discussing the research literature on e.learning in higher education institutions from…

Are Learning Styles Relevant to Virtual Reality?

ERIC Educational Resources Information Center

Chen, Chwen Jen; Toh, Seong Chong; Ismail, Wan Mohd Fauzy Wan

2005-01-01

This study aims to investigate the effects of a virtual reality (VR)-based learning environment on learners with different learning styles. The findings of the aptitude-by-treatment interaction study have shown that learners benefit most from the VR (guided exploration) mode, irrespective of their learning styles. This shows that the VR-based…

Enhancing Online Language Learning as a Tool to Boost Employability

ERIC Educational Resources Information Center

Escobar, Sol; Krauß, Susanne

2017-01-01

Online learning is a very flexible way to build and improve language knowledge alongside other work and/or study commitments whilst at the same time encouraging autonomous learning, time management, self-motivation and other skills relevant to employability. Learning on your own, however, can also be daunting. Therefore, the Languages for All…

Achieving Complex Learning Outcomes through Adoption of a Pedagogical Perspective: A Model for Computer Technology Delivered Instruction

ERIC Educational Resources Information Center

Bellard, Breshanica

2018-01-01

Professionals responsible for the delivery of education and training using technology systems and platforms can facilitate complex learning through application of relevant strategies, principles and theories that support how learners learn and that support how curriculum should be designed in a technology based learning environment. Technological…

Testing Confounds to a Developmental Theory of Children's Learning from Television.

ERIC Educational Resources Information Center

Lometti, Guy E.

Children's learning from television was studied in 343 fourth, sixth, seventh, and eighth grade students who viewed an edited version of a television program and took a posttest. It was hypothesized that children would learn more plot-relevant information (central learning material) as they moved from concrete operational to formal operational…

Identifying Special Educational Needs: Putting a New Framework for Graded Learning Support to the Test

ERIC Educational Resources Information Center

Lebeer, J.; Struyf, E.; De Maeyer, S.; Wilssens, M.; Timbremont, B.; Denys, A.; Vandeveire, H.

2010-01-01

This paper reports a field test of a new system of Graded Learning Support Classification Matrix to determine special educational needs (SEN) in a more systemic way, proposed by the Belgian Ministry of Education (Flanders Region), to put a barrier to the trend of referrals to special education schools. It is not directly determined by a child's…

What Makes a Matrix so Effective? An Empirical Test of the Relative Benefits of Signaling, Extraction, and Localization

ERIC Educational Resources Information Center

Kauffman, Douglas F.; Kiewra, Kenneth A.

2010-01-01

What type of display helps students learn the most and why? This study investigated how displays differing in terms of signaling, extraction, and localization impact learning. In Experiment 1, 72 students were assigned randomly to one cell of a 4 x 2 design. Students studied a standard text, a text with key ideas extracted, an outline that…

Developing a Community of Inquiry in a Face-to-Face Class: How an Online Learning Framework Can Enrich Traditional Classroom Practice

ERIC Educational Resources Information Center

Warner, Alfred G.

2016-01-01

Traditional classes are typically bound both in the classroom space and scheduled time. In this article, I show how applying an online learning framework called the Community of Inquiry and an organizational architecture of matrixed teams has worked in a face-to-face capstone class and extended those boundaries. These were introduced as an…

Research on the development efficiency of regional high-end talent in China: A complex network approach

PubMed Central

Zhang, Wenbin

2017-01-01

In this paper, based on the panel data of 31 provinces and cities in China from 1991 to 2016, the regional development efficiency matrix of high-end talent is obtained by DEA method, and the matrix is converted into a continuous change of complex networks through the construction of sliding window. Using a series of continuous changes in the complex network topology statistics, the characteristics of regional high-end talent development efficiency system are analyzed. And the results show that the average development efficiency of high-end talent in the western region is at a low level. After 2005, the national regional high-end talent development efficiency network has both short-range relevance and long-range relevance in the evolution process. The central region plays an important intermediary role in the national regional high-end talent development system. And the western region has high clustering characteristics. With the implementation of the high-end talent policies with regional characteristics by different provinces and cities, the relevance of high-end talent development efficiency in various provinces and cities presents a weakening trend, and the geographical characteristics of high-end talent are more and more obvious. PMID:29272286

Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B-induced local skin hypersensitization in healthy subjects: a machine-learned analysis.

PubMed

Lötsch, Jörn; Geisslinger, Gerd; Heinemann, Sarah; Lerch, Florian; Oertel, Bruno G; Ultsch, Alfred

2017-08-16

The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

The Human HPLC Column

ERIC Educational Resources Information Center

Frantz, Kyle

2007-01-01

Initiatives in education reform emphasize inquiry-based active learning and real-world relevance to increase science literacy nationwide. Active teaching and learning approaches yield rapid intellectual development and may increase interest and motivation to learn science. Incorporating the topic of drug use with neuroscience, biology, psychology,…

The Relevance of Organizational Subculture for Motivation to Transfer Learning

ERIC Educational Resources Information Center

Egan, Toby Marshall

2008-01-01

Although human resource development practitioners and researchers emphasize organizational culture as a major contributor to employee learning and development, results from this study suggest organizational subculture has greater influence on employee-related learning motivation. The relationships among organizational culture, organizational…

Posttraining transcranial magnetic stimulation of striate cortex disrupts consolidation early in visual skill learning.

PubMed

De Weerd, Peter; Reithler, Joel; van de Ven, Vincent; Been, Marin; Jacobs, Christianne; Sack, Alexander T

2012-02-08

Practice-induced improvements in skilled performance reflect "offline " consolidation processes extending beyond daily training sessions. According to visual learning theories, an early, fast learning phase driven by high-level areas is followed by a late, asymptotic learning phase driven by low-level, retinotopic areas when higher resolution is required. Thus, low-level areas would not contribute to learning and offline consolidation until late learning. Recent studies have challenged this notion, demonstrating modified responses to trained stimuli in primary visual cortex (V1) and offline activity after very limited training. However, the behavioral relevance of modified V1 activity for offline consolidation of visual skill memory in V1 after early training sessions remains unclear. Here, we used neuronavigated transcranial magnetic stimulation (TMS) directed to a trained retinotopic V1 location to test for behaviorally relevant consolidation in human low-level visual cortex. Applying TMS to the trained V1 location within 45 min of the first or second training session strongly interfered with learning, as measured by impaired performance the next day. The interference was conditional on task context and occurred only when training in the location targeted by TMS was followed by training in a second location before TMS. In this condition, high-level areas may become coupled to the second location and uncoupled from the previously trained low-level representation, thereby rendering consolidation vulnerable to interference. Our data show that, during the earliest phases of skill learning in the lowest-level visual areas, a behaviorally relevant form of consolidation exists of which the robustness is controlled by high-level, contextual factors.

Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones

PubMed Central

Hansen, R K; Bissell, M J

2010-01-01

The changes in tissue architecture that accompany the development of breast cancer have been the focus of investigations aimed at developing new cancer therapeutics. As we learn more about the normal mammary gland, we have begun to understand the complex signaling pathways underlying the dramatic shifts in the structure and function of breast tissue. Integrin-, growth factor-, and steroid hormone-signaling pathways all play an important part in maintaining tissue architecture; disruption of the delicate balance of signaling results in dramatic changes in the way cells interact with each other and with the extracellular matrix, leading to breast cancer. The extracellular matrix itself plays a central role in coordinating these signaling processes. In this review, we consider the interrelationships between the extracellular matrix, integrins, growth factors, and steroid hormones in mammary gland development and function. PMID:10903527

Mitochondrial matrix pH controls oxidative phosphorylation and metabolism-secretion coupling in INS-1E clonal beta cells.

PubMed

Akhmedov, Dmitry; Braun, Matthias; Mataki, Chikage; Park, Kyu-Sang; Pozzan, Tullio; Schoonjans, Kristina; Rorsman, Patrik; Wollheim, Claes B; Wiederkehr, Andreas

2010-11-01

Glucose-evoked mitochondrial signals augment ATP synthesis in the pancreatic β cell. This activation of energy metabolism increases the cytosolic ATP/ADP ratio, which stimulates plasma membrane electrical activity and insulin granule exocytosis. We have recently demonstrated that matrix pH increases during nutrient stimulation of the pancreatic β cell. Here, we have tested whether mitochondrial matrix pH controls oxidative phosphorylation and metabolism-secretion coupling in the rat β-cell line INS-1E. Acidification of the mitochondrial matrix pH by nigericin blunted nutrient-dependent respiratory and ATP responses (continuously monitored in intact cells). Using electrophysiology and single cell imaging, we find that the associated defects in energy metabolism suppress glucose-stimulated plasma membrane electrical activity and cytosolic calcium transients. The same parameters were unaffected after direct stimulation of electrical activity with tolbutamide, which bypasses mitochondrial function. Furthermore, lowered matrix pH strongly inhibited sustained, but not first-phase, insulin secretion. Our results demonstrate that the matrix pH exerts a control function on oxidative phosphorylation in intact cells and that this mode of regulation is of physiological relevance for the generation of downstream signals leading to insulin granule exocytosis. We propose that matrix pH serves a novel signaling role in sustained cell activation.

An Analysis of the Use of Social Software and Its Impact on Organizational Processes

NASA Astrophysics Data System (ADS)

Pascual-Miguel, Félix; Chaparro-Peláez, Julián; Hernández-García, Ángel

This article proposes a study on the implementation rate of the most relevant 2.0 tools and technologies in Spanish enterprises, and their impact on 12 important aspects of business processes. In order to characterize the grade of implementation and the perceived improvements on the processes two indexes, Implementation Index and Impact Rate, have been created and displayed in a matrix called "2.0 Success Matrix". Data has been analyzed from a survey taken to directors and executives of large companies and small and medium businesses.

Current status of matrix-assisted laser desorption ionisation-time of flight mass spectrometry in the clinical microbiology laboratory.

PubMed

Kok, Jen; Chen, Sharon C A; Dwyer, Dominic E; Iredell, Jonathan R

2013-01-01

The integration of matrix-assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF MS) into many clinical microbiology laboratories has revolutionised routine pathogen identification. MALDI-TOF MS complements and has good potential to replace existing phenotypic identification methods. Results are available in a more clinically relevant timeframe, particularly in bacteraemic septic shock. Novel applications include strain typing and the detection of antimicrobial resistance, but these are not widely used. This review discusses the technical aspects, current applications, and limitations of MALDI-TOF MS.

Dressed Hard States and Black Hole Soft Hair.

PubMed

Mirbabayi, Mehrdad; Porrati, Massimo

2016-11-18

A recent, intriguing Letter by Hawking, Perry, and Strominger suggests that soft photons and gravitons can be regarded as black hole hair and may be relevant to the black hole information paradox. In this Letter we make use of factorization theorems for infrared divergences of the S matrix to argue that by appropriately dressing in and out hard states, the soft-quanta-dependent part of the S matrix becomes essentially trivial. The information paradox can be fully formulated in terms of dressed hard states, which do not depend on soft quanta.

Recognition and defect detection of dot-matrix text via variation-model based learning

NASA Astrophysics Data System (ADS)

Ohyama, Wataru; Suzuki, Koushi; Wakabayashi, Tetsushi

2017-03-01

An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68 %.

Perceptual advantage for category-relevant perceptual dimensions: the case of shape and motion.

PubMed

Folstein, Jonathan R; Palmeri, Thomas J; Gauthier, Isabel

2014-01-01

Category learning facilitates perception along relevant stimulus dimensions, even when tested in a discrimination task that does not require categorization. While this general phenomenon has been demonstrated previously, perceptual facilitation along dimensions has been documented by measuring different specific phenomena in different studies using different kinds of objects. Across several object domains, there is support for acquired distinctiveness, the stretching of a perceptual dimension relevant to learned categories. Studies using faces and studies using simple separable visual dimensions have also found evidence of acquired equivalence, the shrinking of a perceptual dimension irrelevant to learned categories, and categorical perception, the local stretching across the category boundary. These later two effects are rarely observed with complex non-face objects. Failures to find these effects with complex non-face objects may have been because the dimensions tested previously were perceptually integrated. Here we tested effects of category learning with non-face objects categorized along dimensions that have been found to be processed by different areas of the brain, shape and motion. While we replicated acquired distinctiveness, we found no evidence for acquired equivalence or categorical perception.

Relevance Judging, Evaluation, and Decision Making in Virtual Libraries: A Descriptive Study.

ERIC Educational Resources Information Center

Fitzgerald, Mary Ann; Galloway, Chad

2001-01-01

Describes a study that investigated the cognitive processes undergraduates used to select information while using a virtual library, GALILEO (Georgia Library Learning Online). Discusses higher order thinking processes, relevance judging, evaluation (critical thinking), decision making, reasoning involving documents, relevance-related reasoning,…

Application of mathematical modeling in sustained release delivery systems.

PubMed

Grassi, Mario; Grassi, Gabriele

2014-08-01

This review, presenting as starting point the concept of the mathematical modeling, is aimed at the physical and mathematical description of the most important mechanisms regulating drug delivery from matrix systems. The precise knowledge of the delivery mechanisms allows us to set up powerful mathematical models which, in turn, are essential for the design and optimization of appropriate drug delivery systems. The fundamental mechanisms for drug delivery from matrices are represented by drug diffusion, matrix swelling, matrix erosion, drug dissolution with possible recrystallization (e.g., as in the case of amorphous and nanocrystalline drugs), initial drug distribution inside the matrix, matrix geometry, matrix size distribution (in the case of spherical matrices of different diameter) and osmotic pressure. Depending on matrix characteristics, the above-reported variables may play a different role in drug delivery; thus the mathematical model needs to be built solely on the most relevant mechanisms of the particular matrix considered. Despite the somewhat diffident behavior of the industrial world, in the light of the most recent findings, we believe that mathematical modeling may have a tremendous potential impact in the pharmaceutical field. We do believe that mathematical modeling will be more and more important in the future especially in the light of the rapid advent of personalized medicine, a novel therapeutic approach intended to treat each single patient instead of the 'average' patient.

Physiological ranges of matrix rigidity modulate primary mouse hepatocyte function in part through hepatocyte nuclear factor 4 alpha.

PubMed

Desai, Seema S; Tung, Jason C; Zhou, Vivian X; Grenert, James P; Malato, Yann; Rezvani, Milad; Español-Suñer, Regina; Willenbring, Holger; Weaver, Valerie M; Chang, Tammy T

2016-07-01

Matrix rigidity has important effects on cell behavior and is increased during liver fibrosis; however, its effect on primary hepatocyte function is unknown. We hypothesized that increased matrix rigidity in fibrotic livers would activate mechanotransduction in hepatocytes and lead to inhibition of liver-specific functions. To determine the physiologically relevant ranges of matrix stiffness at the cellular level, we performed detailed atomic force microscopy analysis across liver lobules from normal and fibrotic livers. We determined that normal liver matrix stiffness was around 150 Pa and increased to 1-6 kPa in areas near fibrillar collagen deposition in fibrotic livers. In vitro culture of primary hepatocytes on collagen matrix of tunable rigidity demonstrated that fibrotic levels of matrix stiffness had profound effects on cytoskeletal tension and significantly inhibited hepatocyte-specific functions. Normal liver stiffness maintained functional gene regulation by hepatocyte nuclear factor 4 alpha (HNF4α), whereas fibrotic matrix stiffness inhibited the HNF4α transcriptional network. Fibrotic levels of matrix stiffness activated mechanotransduction in primary hepatocytes through focal adhesion kinase. In addition, blockade of the Rho/Rho-associated protein kinase pathway rescued HNF4α expression from hepatocytes cultured on stiff matrix. Fibrotic levels of matrix stiffness significantly inhibit hepatocyte-specific functions in part by inhibiting the HNF4α transcriptional network mediated through the Rho/Rho-associated protein kinase pathway. Increased appreciation of the role of matrix rigidity in modulating hepatocyte function will advance our understanding of the mechanisms of hepatocyte dysfunction in liver cirrhosis and spur development of novel treatments for chronic liver disease. (Hepatology 2016;64:261-275). © 2016 by the American Association for the Study of Liver Diseases.

Learning feature representations with a cost-relevant sparse autoencoder.

PubMed

Längkvist, Martin; Loutfi, Amy

2015-02-01

There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capacity in the autoencoder is used to minimize the reconstruction error for these noisy inputs. This paper proposes a method that improves the feature learning process by focusing on the task relevant information in the data. This selective attention is achieved by weighting the reconstruction error and reducing the influence of noisy inputs during the learning process. The proposed model is trained on a number of publicly available image data sets and the test error rate is compared to a standard sparse autoencoder and other methods, such as the denoising autoencoder and contractive autoencoder.

Prior familiarity with components enhances unconscious learning of relations.

PubMed

Scott, Ryan B; Dienes, Zoltan

2010-03-01

The influence of prior familiarity with components on the implicit learning of relations was examined using artificial grammar learning. Prior to training on grammar strings, participants were familiarized with either the novel symbols used to construct the strings or with irrelevant geometric shapes. Participants familiarized with the relevant symbols showed greater accuracy when judging the correctness of new grammar strings. Familiarity with elemental components did not increase conscious awareness of the basis for discriminations (structural knowledge) but increased accuracy even in its absence. The subjective familiarity of test strings predicted grammaticality judgments. However, prior exposure to relevant symbols did not increase overall test string familiarity or reliance on familiarity when making grammaticality judgments. Familiarity with the symbols increased the learning of relations between them (bigrams and trigrams) thus resulting in greater familiarity for grammatical versus ungrammatical strings. The results have important implications for models of implicit learning.

Artificial Intelligence in Cardiology.

PubMed

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Structural studies on carbon materials for advanced space technology. Part 1: Structure and oxidation behavior of some carbon/carbon composite materials

NASA Technical Reports Server (NTRS)

Fischbach, D. B.; Uptegrove, D. R.; Srinivasagopalan, S.

1974-01-01

The microstructure and some microstructural effects of oxidation have been investigated for laminar carbon fiber cloth/cloth binder matrix composite materials. It was found that cloth wave is important in determining the macrostructure of the composites X-ray diffraction analysis showed that the composites were more graphitic than the constituent fiber phases, indicating a graphitic binder matrix phase. Various tests which were conducted to investigate specific properties of the material are described. It was learned that under the moderate temperature and oxidant flow conditions studied, C-700, 730 materials exhibit superior oxidation resistance primarily because of the inhibiting influence of the graphitized binder matrix.

Identifying relevant data for a biological database: handcrafted rules versus machine learning.

PubMed

Sehgal, Aditya Kumar; Das, Sanmay; Noto, Keith; Saier, Milton H; Elkan, Charles

2011-01-01

With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases.

Working for the Common Good: Concepts and Models for Service-Learning in Management. AAHE's Series on Service-Learning in the Disciplines.

ERIC Educational Resources Information Center

Godfrey, Paul C., Ed.; Grasso, Edward T., Ed.

The articles in this volume, 15th in a series of monographs on service learning and the academic disciplines, show how student learning can be enhanced by joining management theory with experience and management analysis with action. Service learning prepares business students to see new dimensions of relevance in their coursework, and it provides…

Two-photon imaging in mice shows striosomes and matrix have overlapping but differential reinforcement-related responses

PubMed Central

Sur, Mriganka

2017-01-01

Striosomes were discovered several decades ago as neurochemically identified zones in the striatum, yet technical hurdles have hampered the study of the functions of these striatal compartments. Here we used 2-photon calcium imaging in neuronal birthdate-labeled Mash1-CreER;Ai14 mice to image simultaneously the activity of striosomal and matrix neurons as mice performed an auditory conditioning task. With this method, we identified circumscribed zones of tdTomato-labeled neuropil that correspond to striosomes as verified immunohistochemically. Neurons in both striosomes and matrix responded to reward-predicting cues and were active during or after consummatory licking. However, we found quantitative differences in response strength: striosomal neurons fired more to reward-predicting cues and encoded more information about expected outcome as mice learned the task, whereas matrix neurons were more strongly modulated by recent reward history. These findings open the possibility of harnessing in vivo imaging to determine the contributions of striosomes and matrix to striatal circuit function. PMID:29251596

The Genus Corynebacterium and Other Medically Relevant Coryneform-Like Bacteria

PubMed Central

2012-01-01

Catalase-positive Gram-positive bacilli, commonly called “diphtheroids” or “coryneform” bacteria were historically nearly always dismissed as contaminants when recovered from patients, but increasingly have been implicated as the cause of significant infections. These taxa have been underreported, and the taxa were taxonomically confusing. The mechanisms of pathogenesis, especially for newly described taxa, were rarely studied. Antibiotic susceptibility data were relatively scant. In this minireview, clinical relevance, phenotypic and genetic identification methods, matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) evaluations, and antimicrobial susceptibility testing involving species in the genus Corynebacterium and other medically relevant Gram-positive rods, collectively called coryneforms, are described. PMID:22837327

Energy-free machine learning force field for aluminum.

PubMed

Kruglov, Ivan; Sergeev, Oleg; Yanilkin, Alexey; Oganov, Artem R

2017-08-17

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space.

PubMed

Giorelli, M; Renda, F; Calisti, M; Arienti, A; Ferri, G; Laschi, C

2015-05-13

This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.

Subspace Clustering via Learning an Adaptive Low-Rank Graph.

PubMed

Yin, Ming; Xie, Shengli; Wu, Zongze; Zhang, Yun; Gao, Junbin

2018-08-01

By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.

Developmental emergence of fear/threat learning: neurobiology, associations and timing

PubMed Central

Tallot, L.; Doyère, V.; Sullivan, R. M.

2016-01-01

Pavlovian fear or threat conditioning, where a neutral stimulus takes on aversive properties through pairing with an aversive stimulus, has been an important tool for exploring the neurobiology of learning. In the past decades, this neurobehavioral approach has been expanded to include the developing infant. Indeed, protracted postnatal brain development permits the exploration of how incorporating the amygdala, prefrontal cortex and hippocampus into this learning system impacts the acquisition and expression of aversive conditioning. Here, we review the developmental trajectory of these key brain areas involved in aversive conditioning and relate it to pups’ transition to independence through weaning. Overall, the data suggests that adult-like features of threat learning emerge as the relevant brain areas become incorporated into this learning. Specifically, the developmental emergence of the amygdala permits cue learning and the emergence of the hippocampus permits context learning. We also describe unique features of learning in early life that block threat learning and enhance interaction with the mother or exploration of the environment. Finally, we describe the development of a sense of time within this learning and its involvement in creating associations. Together these data suggest that the development of threat learning is a useful tool for dissecting adult-like functioning of brain circuits, as well as providing unique insights into ecologically relevant developmental changes. PMID:26534899

Psychological Principles in Materials Selection.

ERIC Educational Resources Information Center

Colvin, Cynthia M.

Those psychological principles which might aid the teacher in the selection of instructional materials are examined. Since learning is a process which builds sequentially on past learning, beginning reading materials should include words that have personal relevance for the individual child. Meaningful material is learned more quickly than…

Implementing Service Learning: From Nutrition Education into Community Action

ERIC Educational Resources Information Center

Zinger, Lana; Sinclair, Alicia

2008-01-01

Service learning integrates academic learning and relevant community service with classroom instruction, focusing on critical, reflective thinking and personal civic responsibility. Through a grant, community college students were provided with grocery store vouchers to purchase unfamiliar, healthy foods. Students were taken on an educational…

Adaptative Peer to Peer Data Sharing for Technology Enhanced Learning

NASA Astrophysics Data System (ADS)

Angelaccio, Michele; Buttarazzi, Berta

Starting from the hypothesis that P2P Data Sharing in a direct teaching scenario (e.g.: a classroom lesson) may lead to relevant benefits, this paper explores the features of EduSHARE a Collaborative Learning System useful for Enhanced Learning Process.

Basic Visual Processes and Learning Disability.

ERIC Educational Resources Information Center

Leisman, Gerald

Representatives of a variety of disciplines concerned with either clinical or research problems in vision and learning disabilities present reviews and reports of relevant research and clinical approaches. Contributions are organized into four broad sections: basic processes, specific disorders, diagnosis of visually based problems in learning,…

Contingent Learning for Creative Music Technologists

ERIC Educational Resources Information Center

King, Andrew

2009-01-01

This article will review educational literature relevant to the design and implementation of a learning technology interface (LTI) into an undergraduate music technology curriculum. It also explores through empirical enquiry some of the advantages and disadvantages of using learning technology. This case study adopted a social-constructivist…

Overcoming Learned Helplessness in Community College Students.

ERIC Educational Resources Information Center

Roueche, John E.; Mink, Oscar G.

1982-01-01

Reviews research on the effects of repeated experiences of helplessness and on locus of control. Identifies conditions necessary for overcoming learned helplessness; i.e., the potential for learning to occur; consistent reinforcement; relevant, valued reinforcers; and favorable psychological situation. Recommends eight ways for teachers to…

Successful Web Learning Environments: New Design Guidelines.

ERIC Educational Resources Information Center

Martinez, Margaret

The Web offers the perfect technology and environment for precision learning because learners can be uniquely identified, relevant content can be specifically personalized, and subsequent response and progress can be monitored, supported, and assessed. Technologically, researchers are making rapid progress realizing the personalized learning dream…

Semantic and visual memory codes in learning disabled readers.

PubMed

Swanson, H L

1984-02-01

Two experiments investigated whether learning disabled readers' impaired recall is due to multiple coding deficiencies. In Experiment 1, learning disabled and skilled readers viewed nonsense pictures without names or with either relevant or irrelevant names with respect to the distinctive characteristics of the picture. Both types of names improved recall of nondisabled readers, while learning disabled readers exhibited better recall for unnamed pictures. No significant difference in recall was found between name training (relevant, irrelevant) conditions within reading groups. In Experiment 2, both reading groups participated in recall training for complex visual forms labeled with unrelated words, hierarchically related words, or without labels. A subsequent reproduction transfer task showed a facilitation in performance in skilled readers due to labeling, with learning disabled readers exhibiting better reproduction for unnamed pictures. Measures of output organization (clustering) indicated that recall is related to the development of superordinate categories. The results suggest that learning disabled children's reading difficulties are due to an inability to activate a semantic representation that interconnects visual and verbal codes.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data.

PubMed

Tomazzoli, Maíra M; Pai Neto, Remi D; Moresco, Rodolfo; Westphal, Larissa; Zeggio, Amelia R S; Specht, Leandro; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-12-01

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( λ= 280-400 ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data.

PubMed

Tomazzoli, Maíra Maciel; Pai Neto, Remi Dal; Moresco, Rodolfo; Westphal, Larissa; Zeggio, Amélia Regina Somensi; Specht, Leandro; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo

2015-10-21

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant's resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis' chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds (λ = 280-400ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

A Framework for Culturally Relevant Online Learning: Lessons from Alaska's Tribal Health Workers.

PubMed

Cueva, Katie; Cueva, Melany; Revels, Laura; Lanier, Anne P; Dignan, Mark; Viswanath, K; Fung, Teresa T; Geller, Alan C

2018-03-22

Culturally relevant health promotion is an opportunity to reduce health inequities in diseases with modifiable risks, such as cancer. Alaska Native people bear a disproportionate cancer burden, and Alaska's rural tribal health workers consequently requested cancer education accessible online. In response, the Alaska Native Tribal Health Consortium cancer education team sought to create a framework for culturally relevant online learning to inform the creation of distance-delivered cancer education. Guided by the principles of community-based participatory action research and grounded in empowerment theory, the project team conducted a focus group with 10 Alaska Native education experts, 12 culturally diverse key informant interviews, a key stakeholder survey of 62 Alaska Native tribal health workers and their instructors/supervisors, and a literature review on distance-delivered education with Alaska Native or American Indian people. Qualitative findings were analyzed in Atlas.ti, with common themes presented in this article as a framework for culturally relevant online education. This proposed framework includes four principles: collaborative development, interactive content delivery, contextualizing learning, and creating connection. As an Alaskan tribal health worker shared "we're all in this together. All about conversations, relationships. Always learn from you/with you, together what we know and understand from the center of our experience, our ways of knowing, being, caring." The proposed framework has been applied to support cancer education and promote cancer control with Alaska Native people and has motivated health behavior change to reduce cancer risk. This framework may be adaptable to other populations to guide effective and culturally relevant online interventions.

A Computational Model of How Cholinergic Interneurons Protect Striatal-Dependent Learning

ERIC Educational Resources Information Center

Ashby, F. Gregory; Crossley, Matthew J.

2011-01-01

An essential component of skill acquisition is learning the environmental conditions in which that skill is relevant. This article proposes and tests a neurobiologically detailed theory of how such learning is mediated. The theory assumes that a key component of this learning is provided by the cholinergic interneurons in the striatum known as…

Competency--and Process-Driven e-Learning--A Model-Based Approach

ERIC Educational Resources Information Center

Leyking, Katrina; Chikova, Pavlina; Loos, Peter

2007-01-01

As a matter of fact e-Learning still has not really caught on for corporate training purposes. Investigations on the reasons reveal that e-Learning modules like WBTs often miss any relevance for the tasks to be accomplished in the day-to-day workplace settings. The very learning needs both from an organizational and individual perspective are…

Organizing Blended Learning for Students on the Basis of Learning Roadmaps

ERIC Educational Resources Information Center

Andreeva, Nadezhda M.; Artyukhov, Ivan P.; Myagkova, Elena G.; Pak, Nikolay I.; Akkasynova, Zhamilya K.

2018-01-01

The relevance of the problem of organizing blended learning for students is related to the sharpening contradiction between the high potential of this educational technology and the poor methodological elaboration of its use in actual learning practice. With regard to this, the paper is aimed at providing grounds for the methodological system of…

Theory and Practice: How Filming "Learning in the Real World" Helps Students Make the Connection

ERIC Educational Resources Information Center

Commander, Nannette Evans; Ward, Teresa E.; Zabrucky, Karen M.

2012-01-01

This article describes an assignment, titled "Learning in the Real World," designed for graduate students in a learning theory course. Students work in small groups to create high quality audio-visual films that present "real learning" through interviews and/or observations of learners. Students select topics relevant to theories we are discussing…

Why Increased Social Presence through Web Videoconferencing Does Not Automatically Lead to Improved Learning

ERIC Educational Resources Information Center

Giesbers, Bas; Rienties, Bart; Tempelaar, Dirk T.; Gijselaers, Wim

2014-01-01

The Community of Inquiry (CoI) model provides a well-researched theoretical framework to understand how learners and teachers interact and learn together in computer-supported collaborative learning (CSCL). Most CoI research focuses on asynchronous learning. However, with the arrival of easy-to-use synchronous communication tools the relevance of…

Relevance of IT Integration into Teaching to Learning Satisfaction and Learning Effectiveness

ERIC Educational Resources Information Center

Huang, Shiuab-Ying

2014-01-01

The main purpose of this study is to verify and understand the effects of IT integration into teaching by colleges and vocational schools in Taiwan on learning effectiveness, with learning satisfaction as a mediator. This paper adopts stratified sampling on the administrative supervisors and teachers (i.e. population) in Taiwanese colleges and…

Faculty Experiences, Perceptions, and the Factors That Influence the Use of E-Learning Technologies in the Classroom

ERIC Educational Resources Information Center

Burgos, Rosalina

2014-01-01

The rapid growth of e-learning technologies in higher education challenges university faculty to make their teaching relevant in these new contexts. As e-learning technologies are widely available, faculty members integrated them to their teaching repertoire. Several researchers discussed the impact of e-learning technologies on teaching and…

The Use of E-Learning in Pre-Service Teacher Education

ERIC Educational Resources Information Center

Li, Baomin

2009-01-01

Purpose: E-learning has been applied in pre-service teacher training for many years. The purpose of this paper is to present the use of e-learning in a pre-service teacher training course and discuss the relevant issues involved. Design/methodology/approach: The article reviews literature related to instruction design, and e-learning concepts and…

Lifelong Learning as a Goal--Do Autonomy and Self-Regulation in School Result in Well Prepared Pupils?

ERIC Educational Resources Information Center

Luftenegger, Marko; Schober, Barbara; van de Schoot, Rens; Wagner, Petra; Finsterwald, Monika; Spiel, Christiane

2012-01-01

Fostering lifelong learning (LLL) is a topic of high relevance for current educational policy. School lays the cornerstone for the key components of LLL, specifically persistent motivation to learn and self-regulated learning behavior. The present study investigated the impact of classroom instruction variables on concrete determinants for these…

Factors Influencing Self-Regulation in E-Learning 2.0: Confirmatory Factor Model

ERIC Educational Resources Information Center

Zhao, Hong

2016-01-01

The importance of self-regulation in e-learning has been well noted in research. Relevant studies have shown a consistent positive correlation between learners' self-regulation and their success rate in e-learning. Increasing attention has been paid to developing learners' self-regulated abilities in e-learning. For students, what and how to learn…

Learner Characteristics Predict Performance and Confidence in E-Learning: An Analysis of User Behavior and Self-Evaluation

ERIC Educational Resources Information Center

Jeske, Debora; Roßnagell, Christian Stamov; Backhaus, Joy

2014-01-01

We examined the role of learner characteristics as predictors of four aspects of e-learning performance, including knowledge test performance, learning confidence, learning efficiency, and navigational effectiveness. We used both self reports and log file records to compute the relevant statistics. Regression analyses showed that both need for…

Elucidating Usage of e-Government Learning: A Perspective of the Extended Technology Acceptance Model

ERIC Educational Resources Information Center

Shyu, Stacy Huey-Pyng; Huang, Jen-Hung

2011-01-01

Learning is critical to both economic prosperity and social cohesion. E-government learning, which refers to the government's use of web-based technologies to facilitate learning about subjects that are useful to citizens, is relatively new, relevant, and potentially cost-effective. This work proposes and verifies that the technology acceptance…

How Cognitive Styles Affect the Learning Behaviors of Online Problem-Solving Based Discussion Activity: A Lag Sequential Analysis

ERIC Educational Resources Information Center

Wu, Sheng-Yi; Hou, Huei-Tse

2015-01-01

Cognitive styles play an important role in influencing the learning process, but to date no relevant study has been conducted using lag sequential analysis to assess knowledge construction learning patterns based on different cognitive styles in computer-supported collaborative learning activities in online collaborative discussions. This study…

Designing Collaborative E-Learning Environments Based upon Semantic Wiki: From Design Models to Application Scenarios

ERIC Educational Resources Information Center

Li, Yanyan; Dong, Mingkai; Huang, Ronghuai

2011-01-01

The knowledge society requires life-long learning and flexible learning environment that enables fast, just-in-time and relevant learning, aiding the development of communities of knowledge, linking learners and practitioners with experts. Based upon semantic wiki, a combination of wiki and Semantic Web technology, this paper designs and develops…

Developing an Entrepreneurship Module by Using Product-Based Learning Approach in Vocational Education

ERIC Educational Resources Information Center

Yulastri, Asmar; Hidayat, Hendra; Ganefri; Islami, Syaiful; Edya, Fuji

2017-01-01

Boring lecturers and irrelevant learning materials needed by students caused the students are less motivated to learn entrepreneurship course in vocational education. It is caused by the learning materials that will be delivered by the lectures can be predicted by the students. Thus, a relevant and supplementary support is much needed which can be…

Relationships between Hong Kong Students' Perceptions of the Learning Environment and Their Learning Patterns in Post-Secondary Education

ERIC Educational Resources Information Center

Law, Dennis C. S.; Meyer, Jan H. F.

2011-01-01

The present study aims to analyse the complex relationships between the relevant constructs of students' demographic background, perceptions, learning patterns and (proxy measures of) learning outcomes in order to delineate the possible direct, indirect, or spurious effects among them. The analytical methodology is substantively framed against the…

Troublesome Knowledge, Troubling Experience: An Inquiry into Faculty Learning in Service-Learning

ERIC Educational Resources Information Center

Harrison, Barbara; Clayton, Patti H.; Tilley-Lubbs, Gresilda A.

2014-01-01

In this article we share the theoretical framework of threshold concepts--concepts on which deep understanding of a field of practice and inquiry hinges and which, once understood and internalized, open a doorway to otherwise inaccessible ways of thinking--and explore its relevance to learning how to teach, learn, serve, partner, and generate…

Making Science Relevant

ERIC Educational Resources Information Center

Eick, Charles; Deutsch, Bill; Fuller, Jennifer; Scott, Fletcher

2008-01-01

Science teachers are always looking for ways to demonstrate the relevance of science to students. By connecting science learning to important societal issues, teachers can motivate students to both enjoy and engage in relevant science (Bennet, Lubben, and Hogarth 2007). To develop that connection, teachers can help students take an active role in…

Culture, Relevance, and Schooling: Exploring Uncommon Ground

ERIC Educational Resources Information Center

Scherff, Lisa, Ed.; Spector, Karen, Ed.

2011-01-01

In "Culture, Relevance, and Schooling: Exploring Uncommon Ground," Lisa Scherff, Karen Spector, and the contributing authors conceive of culturally relevant and critically minded pedagogies in terms of opening up new spatial, discursive, and/or embodied learning terrains. Readers will traverse multiple landscapes and look into a variety of spaces…

Toward a Factor Analytic Definition of Academic Relevance

ERIC Educational Resources Information Center

Permut, Steven E.

1974-01-01

Underlying factor structure of 10 concepts rated by 67 students in an introductory advertising course was examined. Dimensions of relevance were found to conform to results reported by Menges and Trumpeter (1971) suggesting the potential for a university of basic dimensions of educational relevance across diverse fields of learning. (Author/RC)

Examining Hip-Hop as Culturally Relevant Pedagogy

ERIC Educational Resources Information Center

Kim, Jung; Pulido, Isaura

2015-01-01

Culturally relevant pedagogy is a framework that conceptualizes the process of student learning as contingent upon educators' deep understanding of students' cultural backgrounds to co-construct knowledge and develop academic skills. Concurrently, there are a growing number of studies that explore hip-hop as a culturally relevant curriculum for…

Relevant ESL for the Teenager.

ERIC Educational Resources Information Center

Wheatley, Iris Alicia Velez

This guide was prepared for the ESL teacher to help bilingual students learn the English reading and writing skills necessary to acquire a summer job. These lessons are relevant to students' needs, an important factor in generating interest and motivation. General objectives are: to design a relevant ESL program for teenagers; to help monolingual…

Adaptive Memory: Survival Processing Enhances Retention

ERIC Educational Resources Information Center

Nairne, James S.; Thompson, Sarah R.; Pandeirada, Josefa N. S.

2007-01-01

The authors investigated the idea that memory systems might have evolved to help us remember fitness-relevant information--specifically, information relevant to survival. In 4 incidental learning experiments, people were asked to rate common nouns for their survival relevance (e.g., in securing food, water, or protection from predators); in…

Improved lattice computation of proton decay matrix elements

NASA Astrophysics Data System (ADS)

Aoki, Yasumichi; Izubuchi, Taku; Shintani, Eigo; Soni, Amarjit

2017-07-01

We present an improved result for the lattice computation of the proton decay matrix elements in Nf=2 +1 QCD. In this study, by adopting the error reduction technique of all-mode-averaging, a significant improvement of the statistical accuracy is achieved for the relevant form factor of proton (and also neutron) decay on the gauge ensemble of Nf=2 +1 domain-wall fermions with mπ=0.34 - 0.69 GeV on a 2.7 fm3 lattice, as used in our previous work [1]. We improve the total accuracy of matrix elements to 10-15% from 30-40% for p →π e+ or from 20-40% for p →K ν ¯. The accuracy of the low-energy constants α and β in the leading-order baryon chiral perturbation theory (BChPT) of proton decay are also improved. The relevant form factors of p →π estimated through the "direct" lattice calculation from the three-point function appear to be 1.4 times smaller than those from the "indirect" method using BChPT with α and β . It turns out that the utilization of our result will provide a factor 2-3 larger proton partial lifetime than that obtained using BChPT. We also discuss the use of these parameters in a dark matter model.

Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security.

PubMed

Zufiria, Pedro J; Pastor-Escuredo, David; Úbeda-Medina, Luis; Hernandez-Medina, Miguel A; Barriales-Valbuena, Iker; Morales, Alfredo J; Jacques, Damien C; Nkwambi, Wilfred; Diop, M Bamba; Quinn, John; Hidalgo-Sanchís, Paula; Luengo-Oroz, Miguel

2018-01-01

We propose a framework for the systematic analysis of mobile phone data to identify relevant mobility profiles in a population. The proposed framework allows finding distinct human mobility profiles based on the digital trace of mobile phone users characterized by a Matrix of Individual Trajectories (IT-Matrix). This matrix gathers a consistent and regularized description of individual trajectories that enables multi-scale representations along time and space, which can be used to extract aggregated indicators such as a dynamic multi-scale population count. Unsupervised clustering of individual trajectories generates mobility profiles (clusters of similar individual trajectories) which characterize relevant group behaviors preserving optimal aggregation levels for detailed and privacy-secured mobility characterization. The application of the proposed framework is illustrated by analyzing fully anonymized data on human mobility from mobile phones in Senegal at the arrondissement level over a calendar year. The analysis of monthly mobility patterns at the livelihood zone resolution resulted in the discovery and characterization of seasonal mobility profiles related with economic activities, agricultural calendars and rainfalls. The use of these mobility profiles could support the timely identification of mobility changes in vulnerable populations in response to external shocks (such as natural disasters, civil conflicts or sudden increases of food prices) to monitor food security.

[Tissue engineering of urinary bladder using acellular matrix].

PubMed

Glybochko, P V; Olefir, Yu V; Alyaev, Yu G; Butnaru, D V; Bezrukov, E A; Chaplenko, A A; Zharikova, T M

2017-04-01

Tissue engineering has become a new promising strategy for repairing damaged organs of the urinary system, including the bladder. The basic idea of tissue engineering is to integrate cellular technology and advanced bio-compatible materials to replace or repair tissues and organs. of the study is the objective reflection of the current trends and advances in tissue engineering of the bladder using acellular matrix through a systematic search of preclinical and clinical studies of interest. Relevant studies, including those on methods of tissue engineering of urinary bladder, was retrieved from multiple databases, including Scopus, Web of Science, PubMed, Embase. The reference lists of the retrieved review articles were analyzed for the presence of the missing relevant publications. In addition, a manual search for registered clinical trials was conducted in clinicaltrials.gov. Following the above search strategy, a total of 77 eligible studies were selected for further analysis. Studies differed in the types of animal models, supporting structures, cells and growth factors. Among those, studies using cell-free matrix were selected for a more detailed analysis. Partial restoration of urothelium layer was observed in most studies where acellular grafts were used for cystoplasty, but no the growth of the muscle layer was observed. This is the main reason why cellular structures are more commonly used in clinical practice.

Relational Learning via Collective Matrix Factorization

DTIC Science & Technology

2008-06-01

well-known example of such a schema is pLSI- pHITS [13], which models document-word counts and document-document citations: E1 = words and E2 = E3...relational co- clustering include pLSI, pLSI- pHITS , the symmetric block models of Long et. al. [23, 24, 25], and Bregman tensor clustering [5] (which can...to pLSI- pHITS In this section we provide an example where the additional flexibility of collective matrix factorization leads to better results; and

Biopreservation of Myoglobin in Crowded Environment: A Comparison between Gelatin and Trehalose Matrixes.

PubMed

Semeraro, Enrico F; Giuffrida, Sergio; Cottone, Grazia; Cupane, Antonio

2017-09-21

Biopreservation by sugar and/or polymeric matrixes is a thoroughly studied research topic with wide technological relevance. Ternary amorphous systems containing both saccharides and proteins are extensively exploited to model the in vivo biopreservation process. With the aim of disentangling the effect of saccharides and polypeptidic crowders (such as gelatin) on the preservation of a model protein, we present here a combined differential scanning calorimetry and UV-vis spectrophotometry study on samples of myoglobin embedded in amorphous gelatin and trehalose + gelatin matrixes at different hydrations, and compare them with amorphous myoglobin-only and myoglobin-trehalose samples. The results point out the different effects of gelatin, which acts mainly as a crowding agent, and trehalose, which acts mainly by direct interaction. Gelatin is able to improve effectively the protein thermal stability at very low hydration; however, it has small effects at medium to high hydration. Consistently, gelatin appears to be more effective than trehalose against massive denaturation in the long time range, while the mixed trehalose + collagen matrix is most effective in preserving protein functionality, outdoing both gelatin-only and trehalose-only matrixes.

ILIAD Testing; and a Kalman Filter for 3-D Pose Estimation

NASA Technical Reports Server (NTRS)

Richardson, A. O.

1996-01-01

This report presents the results of a two-part project. The first part presents results of performance assessment tests on an Internet Library Information Assembly Data Base (ILIAD). It was found that ILLAD performed best when queries were short (one-to-three keywords), and were made up of rare, unambiguous words. In such cases as many as 64% of the typically 25 returned documents were found to be relevant. It was also found that a query format that was not so rigid with respect to spelling errors and punctuation marks would be more user-friendly. The second part of the report shows the design of a Kalman Filter for estimating motion parameters of a three dimensional object from sequences of noisy data derived from two-dimensional pictures. Given six measured deviation values represendng X, Y, Z, pitch, yaw, and roll, twelve parameters were estimated comprising the six deviations and their time rate of change. Values for the state transiton matrix, the observation matrix, the system noise covariance matrix, and the observation noise covariance matrix were determined. A simple way of initilizing the error covariance matrix was pointed out.

Metal Cluster Models for Heterogeneous Catalysis: A Matrix-Isolation Perspective.

PubMed

Hübner, Olaf; Himmel, Hans-Jörg

2018-02-19

Metal cluster models are of high relevance for establishing new mechanistic concepts for heterogeneous catalysis. The high reactivity and particular selectivity of metal clusters is caused by the wealth of low-lying electronically excited states that are often thermally populated. Thereby the metal clusters are flexible with regard to their electronic structure and can adjust their states to be appropriate for the reaction with a particular substrate. The matrix isolation technique is ideally suited for studying excited state reactivity. The low matrix temperatures (generally 4-40 K) of the noble gas matrix host guarantee that all clusters are in their electronic ground-state (with only a very few exceptions). Electronically excited states can then be selectively populated and their reactivity probed. Unfortunately, a systematic research in this direction has not been made up to date. The purpose of this review is to provide the grounds for a directed approach to understand cluster reactivity through matrix-isolation studies combined with quantum chemical calculations. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

E-learning in medical education in resource constrained low- and middle-income countries.

PubMed

Frehywot, Seble; Vovides, Yianna; Talib, Zohray; Mikhail, Nadia; Ross, Heather; Wohltjen, Hannah; Bedada, Selam; Korhumel, Kristine; Koumare, Abdel Karim; Scott, James

2013-02-04

In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators "AND" and "OR" to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. E-learning in medical education is a means to an end, rather than the end in itself. Utilizing e-learning can result in greater educational opportunities for students while simultaneously enhancing faculty effectiveness and efficiency. However, this potential of e-learning assumes a certain level of institutional readiness in human and infrastructural resources that is not always present in LMICs. Institutional readiness for e-learning adoption ensures the alignment of new tools to the educational and economic context.

E-learning in medical education in resource constrained low- and middle-income countries

PubMed Central

2013-01-01

Background In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Methods Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators “AND” and “OR” to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Results Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. Conclusions E-learning in medical education is a means to an end, rather than the end in itself. Utilizing e-learning can result in greater educational opportunities for students while simultaneously enhancing faculty effectiveness and efficiency. However, this potential of e-learning assumes a certain level of institutional readiness in human and infrastructural resources that is not always present in LMICs. Institutional readiness for e-learning adoption ensures the alignment of new tools to the educational and economic context. PMID:23379467