Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Learning Rotation-Invariant Local Binary Descriptor.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2017-08-01

In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.

Reinforcement active learning in the vibrissae system: optimal object localization.

PubMed

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Application of Project-Based Learning on the Development of Young Local Tour Guides on Tai Phuan's Culture and Tourist Attractions in Sisatchanalai District, Sukhothai Province

ERIC Educational Resources Information Center

Kerdpol, Sakon

2016-01-01

This paper presents an investigation of a research entitled, " An Application of Project-based Learning on the Development of Young Local Tour Guides on Tai Phuan's Culture and Tourist Attractions in Sisatchanalai District, Sukhothai Province. It was intended to develop young local tour guides on Tai Phuan's culture and tourist attractions in…

A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

PubMed

Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

2015-01-15

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

PubMed Central

Gao, Yaozong; Zhan, Yiqiang

2015-01-01

Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ∼0.89) and fast (∼4 s), which satisfies the real-world clinical requirements of IGRT. PMID:24495983

Creating Synergies: Local Government Facilitating Learning and Development through Partnerships--Support Document

ERIC Educational Resources Information Center

Waterhouse, Peter; Virgona, Crina; Brown, Richard

2006-01-01

This research sought to document and better understand four evolving learning communities in Victoria. It was based upon an earlier study by the Victorian Local Governance Association (VLGA) (Snelling, 2003). The study was qualitative in nature, based on face-to-face interviews and case studies. This supporting document provides the literature…

Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

NASA Astrophysics Data System (ADS)

Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

2013-04-01

SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

Measuring Work-Based Learning for Continuous Improvement. Connecting the Classroom to Careers: The State's Role in Work-Based Learning

ERIC Educational Resources Information Center

Advance CTE: State Leaders Connecting Learning to Work, 2016

2016-01-01

Work-based learning provides a continuum of activities--from career exploration and job shadowing to internships and apprenticeships--that help students develop technical and professional skills in an authentic work environment. While many work-based learning programs are designed and operated at the local level, several states have begun building…

Living Traditions--A Teacher's Guide: Teaching Local History Using State and National Learning Standards.

ERIC Educational Resources Information Center

Skelding, Mark; Kemple, Martin; Kiefer, Joseph

This guide is designed to take teachers through a step-by-step process for developing an integrated, standards-based curriculum that focuses on the stories, history, folkways, and agrarian traditions of the local community. Such a place-based curriculum helps students to become culturally literate, makes learning relevant and engaging, draws on…

A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine †

PubMed Central

Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

2015-01-01

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. PMID:25599427

Transfer between local and global processing levels by pigeons (Columba livia) and humans (Homo sapiens) in exemplar- and rule-based categorization tasks.

PubMed

Aust, Ulrike; Braunöder, Elisabeth

2015-02-01

The present experiment investigated pigeons' and humans' processing styles-local or global-in an exemplar-based visual categorization task in which category membership of every stimulus had to be learned individually, and in a rule-based task in which category membership was defined by a perceptual rule. Group Intact was trained with the original pictures (providing both intact local and global information), Group Scrambled was trained with scrambled versions of the same pictures (impairing global information), and Group Blurred was trained with blurred versions (impairing local information). Subsequently, all subjects were tested for transfer to the 2 untrained presentation modes. Humans outperformed pigeons regarding learning speed and accuracy as well as transfer performance and showed good learning irrespective of group assignment, whereas the pigeons of Group Blurred needed longer to learn the training tasks than the pigeons of Groups Intact and Scrambled. Also, whereas humans generalized equally well to any novel presentation mode, pigeons' transfer from and to blurred stimuli was impaired. Both species showed faster learning and, for the most part, better transfer in the rule-based than in the exemplar-based task, but there was no evidence of the used processing mode depending on the type of task (exemplar- or rule-based). Whereas pigeons relied on local information throughout, humans did not show a preference for either processing level. Additional tests with grayscale versions of the training stimuli, with versions that were both blurred and scrambled, and with novel instances of the rule-based task confirmed and further extended these findings. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

NASA Technical Reports Server (NTRS)

Assad, C.; Hartmann, M.; Paulin, M. G.

2001-01-01

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

Discriminant locality preserving projections based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu; Li, Defang

2014-11-01

Conventional discriminant locality preserving projection (DLPP) is a dimensionality reduction technique based on manifold learning, which has demonstrated good performance in pattern recognition. However, because its objective function is based on the distance criterion using L2-norm, conventional DLPP is not robust to outliers which are present in many applications. This paper proposes an effective and robust DLPP version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based locality preserving between-class dispersion and the L1-norm-based locality preserving within-class dispersion. The proposed method is proven to be feasible and also robust to outliers while overcoming the small sample size problem. The experimental results on artificial datasets, Binary Alphadigits dataset, FERET face dataset and PolyU palmprint dataset have demonstrated the effectiveness of the proposed method.

Leveraging Mobile Games for Place-Based Language Learning

ERIC Educational Resources Information Center

Holden, Christopher L.; Sykes, Julie M.

2011-01-01

This paper builds on the emerging body of research aimed at exploring the educational potential of mobile technologies, specifically, how to leverage place-based, augmented reality mobile games for language learning. Mentira is the first place-based, augmented reality mobile game for learning Spanish in a local neighborhood in the Southwestern…

Using Chemistry Teaching Aids Based Local Wisdom as an Alternative Media for Chemistry Teaching and Learning

ERIC Educational Resources Information Center

Priyambodo, Erfan; Wulaningrum, Safira

2017-01-01

Students have difficulties in relating the chemistry phenomena they learned and the life around them. It is necessary to have teaching aids which can help them to relate between chemistry with the phenomena occurred in everyday life, which is chemistry's teaching aids based on local wisdom. There are 3 teaching aids which used in chemistry…

Developing Learning Model Based on Local Culture and Instrument for Mathematical Higher Order Thinking Ability

ERIC Educational Resources Information Center

Saragih, Sahat; Napitupulu, E. Elvis; Fauzi, Amin

2017-01-01

This research aims to develop a student-centered learning model based on local culture and instrument of mathematical higher order thinking of junior high school students in the frame of the 2013-Curriculum in North Sumatra, Indonesia. The subjects of the research are seventh graders which are taken proportionally random consisted of three public…

Integration of living values into physics learning based on local potentials

NASA Astrophysics Data System (ADS)

Sarah, S.; Prasetyo, Z. K.; Wilujeng, I.

2018-05-01

Living values are the principles and beliefs that influence the way of life and behavior of people in society. These values are defined to determine the individuals’ characteristics in the physical, intellectual, social-emotional, and spiritual dimensions. Such values could be acquired through physics learning. Therefore, the study concerned here was aimed at determining the difference in the living values acquired between students of the grade officially termed Grade X at a state senior high school referred to as SMAN 1 Selomerto, Central Java, Indonesia, who learned physics by using content based on local potentials and those who learned physics without using that content. A quasi-experiment with the control group pre-test post-test design was conducted to collect the data. The data were analyzed by using tests of normality, homogeneity, and different. The results indicate no difference in the living values acquired between students learning physics by using local-potential content and those learning physics without using that content.

Semi-supervised protein subcellular localization.

PubMed

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

2009-01-30

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

Hunting for Hydrothermal Vents at the Local-Scale Using AUV's and Machine-Learning Classification in the Earth's Oceans

NASA Astrophysics Data System (ADS)

White, S. M.

2018-05-01

New AUV-based mapping technology coupled with machine-learning methods for detecting individual vents and vent fields at the local-scale raise the possibility of understanding the geologic controls on hydrothermal venting.

The Engagement Tree: Arts-Based Pedagogies for Environmental Learning

ERIC Educational Resources Information Center

Davis, Susan

2018-01-01

This case study reports on an arts-based project called "Tree-Mappa," one that sought to engage primary-school children in learning about their local environment through significant trees. Pedagogical approaches featured the use of arts-based strategies as the means for activating cognitive and affective responses and learning. The frame…

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

PubMed

Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

2017-12-20

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

A Cultured Learning Environment: Implementing a Problem- and Service-Based Microbiology Capstone Course to Assess Process- and Skill-Based Learning Objectives

ERIC Educational Resources Information Center

Watson, Rachel M.; Willford, John D.; Pfeifer, Mariel A.

2018-01-01

In this study, a problem-based capstone course was designed to assess the University of Wyoming Microbiology Program's skill-based and process-based student learning objectives. Students partnered with a local farm, a community garden, and a free downtown clinic in order to conceptualize, propose, perform, and present studies addressing problems…

Computer Game-Based Learning: Perceptions and Experiences of Senior Chinese Adults

ERIC Educational Resources Information Center

Wang, Feihong; Lockee, Barbara B.; Burton, John K.

2012-01-01

The purpose of this study was to investigate senior Chinese adults' potential acceptance of computer game-based learning (CGBL) by probing their perceptions of computer game play and their perceived impacts of game play on their learning of computer skills and life satisfaction. A total of 60 senior adults from a local senior adult learning center…

Library-Based Learning in an Information Society.

ERIC Educational Resources Information Center

Breivik, Patricia Senn

1986-01-01

The average academic library has great potential for quality nonclassroom learning benefiting students, faculty, alumni, and the local business community. The major detriments are the limited perceptions about libraries and librarians among campus administrators and faculty. Library-based learning should be planned to be assimilated into overall…

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

PubMed

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

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

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

PubMed Central

Gerstner, Wulfram

2017-01-01

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

Implementation of Markerless Augmented Reality Technology Based on Android to Introduction Lontara in Marine Society

NASA Astrophysics Data System (ADS)

Jumarlis, Mila; Mirfan, Mirfan

2018-05-01

Local language learning had been leaving by people especially young people had affected technology advances so that involved lack of interest to learn culture especially local language. So required interactive and interest learning media for introduction Lontara. This research aims to design and implement augmented reality on introduction Lontara on mobile device especially android. Application of introduction Lontara based on Android was designed by Vuforia and Unity. Data collection method were observation, interview, and literature review. That data was analysed for being information. The system was designed by Unified Modeling Language (UML). The method used is a marker. The test result found that application of Augmented Reality on introduction Lontara based on Android could improve public interest for introducing local language particularly young people in learning about Lontara because of using technology. Application of introduction of Lontara based on Android used augmented reality occurred sound and how to write Lontara with animation. This application could be running without an internet connection, so that its used more efficient and could maximize from user.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

Exploring the Effect of Background Knowledge and Text Cohesion on Learning from Texts in Computer Science

ERIC Educational Resources Information Center

Gasparinatou, Alexandra; Grigoriadou, Maria

2013-01-01

In this study, we examine the effect of background knowledge and local cohesion on learning from texts. The study is based on construction-integration model. Participants were 176 undergraduate students who read a Computer Science text. Half of the participants read a text of maximum local cohesion and the other a text of minimum local cohesion.…

The Integrated Model of Sustainability Perspective in Spermatophyta Learning Based on Local Wisdom

NASA Astrophysics Data System (ADS)

Hartadiyati, E.; Rizqiyah, K.; Wiyanto; Rusilowati, A.; Prasetia, A. P. B.

2017-09-01

In present condition, culture is diminished, the change of social order toward the generation that has no policy and pro-sustainability; As well as the advancement of science and technology are often treated unwisely so as to excite local wisdom. It is therefore necessary to explore intra-curricular local wisdom in schools. This study aims to produce an integration model of sustainability perspectives based on local wisdom on spermatophyta material that is feasible and effective. This research uses define, design and develop stages to an integration model of sustainability perspectives based on local wisdom on spermatophyta material. The resulting product is an integration model of socio-cultural, economic and environmental sustainability perspective and formulated with preventive, preserve and build action on spermatophyta material consisting of identification and classification, metagenesis and the role of spermatophyta for human life. The integration model of sustainability perspective in learning spermatophyta based on local wisdom is considered proven to be effective in raising sustainability’s awareness of high school students.

Using the Storypath Approach to Make Local Government Understandable

ERIC Educational Resources Information Center

McGuire, Margit E.; Cole, Bronwyn

2008-01-01

Learning about local government seems boring and irrelevant to most young people, particularly to students from high-poverty backgrounds. The authors explore a promising approach for solving this problem, Storypath, which engages students in authentic learning and active citizenship. The Storypath approach is based on a narrative in which students…

Science learning based on local potential: Overview of the nature of science (NoS) achieved

NASA Astrophysics Data System (ADS)

Wilujeng, Insih; Zuhdan Kun, P.; Suryadarma, IGP.

2017-08-01

The research concerned here examined the effectiveness of science learning conducted with local potential as basis from the point of a review of the NoS (nature of science) achieved. It used the non equivalent control group design and took place in the regions of Magelang and Pati, Province of Central Java, and the regions of Bantul and Sleman, Province of the Special Region of Yogyakarta. The research population consisted of students of the first and second grades at each junior high school chosen with research subjects sampled by means of cluster sampling. The instruments used included: a) an observation sheet, b) a written test, and c) a questionnaire. The learning and research instruments had been declared valid and reliable according to previous developmental research. In conclusion, the science learning based on local potential was effective in terms of all the NoS aspects.

Character and Local Wisdom-Based Instructional Model of Bahasa Indonesia in Vocational High Schools

ERIC Educational Resources Information Center

Anggraini, Purwati; Kusniarti, Tuti

2017-01-01

This research aimed at establishing a character and local wisdom-based instructional model of Bahasa Indonesia. The learning model based on local wisdom literature is very important to prepared, because this model can enrich the knowledge and develop the character of students. Meanwhile, the textbook can broaden the student teachers about the…

Expanding Omani Learners' Horizons through Project-Based Learning: A Case Study

ERIC Educational Resources Information Center

Dauletova, Victoria

2014-01-01

As a relatively innovative teaching/learning approach in the Arabian Gulf region, in general, and in Oman, in particular, project-based learning requires progressive amendments and adaptations to the national culture of the learner. This article offers analysis of the current state of the approach in the local educational environment. Furthermore,…

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Credit and Attendance Alternatives for a Competency-Based Instructional Program.

ERIC Educational Resources Information Center

Georgia State Dept. of Education, Atlanta. Office of Planning and Development.

Credit alternatives allow local school systems to take advantage of community learning resources as an enrichment for, or supplement to, school learning experiences. The first part of this handbook describes credit alternatives that may be used by local school systems to provide individualized curricula for diagnostic and prescriptive instruction.…

"Public-Based-Learning": The Place of Publics in Exploring Environmental Controversies for Pedagogical Purposes

ERIC Educational Resources Information Center

Mélard, François; Denayer, Dorothée; Semal, Nathalie

2015-01-01

This article examines a 15 year-old master level seminar dedicated to the exploration of local and complex environmental issues marked by scientific or technological uncertainties. Following a pragmatic learning approach, we focus our discussion on a triadic relationship between supervisors, students and various concerned publics. A local flood…

Building Capacity through International Student Affairs Exchange

ERIC Educational Resources Information Center

Roberts, Dennis C.; Roberts, Darbi L.

2012-01-01

In order to build local capacity in an international higher education setting, the Qatar Study Tour and Young Professionals Institute (QST and YPI) was created as an inquiry-based learning experience shared among diverse participants and designed to enhance learning at both the local and international levels. The intent of the QST and YPI model…

Inquiry-Based Integrated Science Education: Implementation of Local Content “Soil Washing” Project To Improve Junior High School Students’ Environmental Literacy

NASA Astrophysics Data System (ADS)

Syifahayu

2017-02-01

The study was conducted based on teaching and learning problems led by conventional method that had been done in the process of learning science. It gave students lack opportunities to develop their competence and thinking skills. Consequently, the process of learning science was neglected. Students did not have opportunity to improve their critical attitude and creative thinking skills. To cope this problem, the study was conducted using Project-Based Learning model through inquiry-based science education about environment. The study also used an approach called Sains Lingkungan and Teknologi masyarakat - “Saling Temas” (Environmental science and Technology in Society) which promoted the local content in Lampung as a theme in integrated science teaching and learning. The study was a quasi-experimental with pretest-posttest control group design. Initially, the subjects were given a pre-test. The experimental group was given inquiry learning method while the control group was given conventional learning. After the learning process, the subjects of both groups were given post-test. Quantitative analysis was performed using the Mann-Whitney U-test and also a qualitative descriptive. Based on the result, environmental literacy skills of students who get inquiry learning strategy, with project-based learning model on the theme soil washing, showed significant differences. The experimental group is better than the control group. Data analysis showed the p-value or sig. (2-tailed) is 0.000 <α = 0.05 with the average N-gain of experimental group is 34.72 and control group is 16.40. Besides, the learning process becomes more meaningful.

Towards tailored teaching: using participatory action research to enhance the learning experience of Longitudinal Integrated Clerkship students in a South African rural district hospital.

PubMed

von Pressentin, Klaus B; Waggie, Firdouza; Conradie, Hoffie

2016-03-08

The introduction of Stellenbosch University's Longitudinal Integrated Clerkship (LIC) model as part of the undergraduate medical curriculum offers a unique and exciting training model to develop generalist doctors for the changing South African health landscape. At one of these LIC sites, the need for an improvement of the local learning experience became evident. This paper explores how to identify and implement a tailored teaching and learning intervention to improve workplace-based learning for LIC students. A participatory action research approach was used in a co-operative inquiry group (ten participants), consisting of the students, clinician educators and researchers, who met over a period of 5 months. Through a cyclical process of action and reflection this group identified a teaching intervention. The results demonstrate the gaps and challenges identified when implementing a LIC model of medical education. A structured learning programme for the final 6 weeks of the students' placement at the district hospital was designed by the co-operative inquiry group as an agreed intervention. The post-intervention group reflection highlighted a need to create a structured programme in the spirit of local collaboration and learning across disciplines. The results also enhance our understanding of both students and clinician educators' perceptions of this new model of workplace-based training. This paper provides practical strategies to enhance teaching and learning in a new educational context. These strategies illuminate three paradigm shifts: (1) from the traditional medical education approach towards a transformative learning approach advocated for the 21(st) century health professional; (2) from the teaching hospital context to the district hospital context; and (3) from block-based teaching towards a longitudinal integrated learning model. A programme based on balancing structured and tailored learning activities is recommended in order to address the local learning needs of students in the LIC model. We recommend that action learning sets should be developed at these LIC sites, where the relevant aspects of work-place based learning are negotiated.

Local Knowledge and Adult Learning in Environmental Adult Education: Community-Based Ecotourism in Southern Thailand

ERIC Educational Resources Information Center

Walter, Pierre

2009-01-01

This paper examines how local knowledge is employed in environmental adult education in a community-based ecotourism project in an island community in southern Thailand. The study is based on field research and analysis of project websites, media reports and documents. Situated at the intersection of global tourism and a local Thai-Malay Muslim…

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

Problem-Based Learning--Buginese Cultural Knowledge Model--Case Study: Teaching Mathematics at Junior High School

ERIC Educational Resources Information Center

Cheriani, Cheriani; Mahmud, Alimuddin; Tahmir, Suradi; Manda, Darman; Dirawan, Gufran Darma

2015-01-01

This study aims to determine the differences in learning output by using Problem Based Model combines with the "Buginese" Local Cultural Knowledge (PBL-Culture). It is also explores the students activities in learning mathematics subject by using PBL-Culture Models. This research is using Mixed Methods approach that combined quantitative…

Understanding Angle and Angle Measure: A Design-Based Research Study Using Context Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Crompton, Helen

2015-01-01

Mobile technologies are quickly becoming tools found in the educational environment. The researchers in this study use a form of mobile learning to support students in learning about angle concepts. Design-based research is used in this study to develop an empirically-substantiated local instruction theory about students' develop of angle and…

"They're Funny Bloody Cattle": Encouraging Rural Men to Learn

ERIC Educational Resources Information Center

Vallance, Soapy; Golding, Barry

2008-01-01

Our paper examines and analyses the contexts and organisations in rural and regional communities that informally and effectively encourage men to learn. It is based on a combination of local, rural adult education practice and a suite of studies in Australia and elsewhere of learning in community contexts, most recently into community-based men's…

Place in Pacific Islands Climate Education

NASA Astrophysics Data System (ADS)

Barros, C.; Koh, M. W.

2015-12-01

Understanding place, including both the environment and its people, is essential to understanding our climate, climate change, and its impacts. For us to develop a sense of our place, we need to engage in multiple ways of learning: observation, experimentation, and opportunities to apply new knowledge (Orr, 1992). This approach allows us to access different sources of knowledge and then create local solutions for local issues. It is especially powerful when we rely on experts and elders in our own community along with information from the global community.The Pacific islands Climate Education Partnership (PCEP) is a collaboration of partners—school systems, nongovernmental organizations, and government agencies—working to support learning and teaching about climate in the Pacific. Since 2009, PCEP partners have been working together to develop and implement classroom resources, curriculum standards, and teacher professional learning opportunities in which learners approach climate change and its impacts first through the lens of their own place. Such an approach to putting place central to teaching and learning about climate requires partnership and opportunities for learners to explore solutions for and with their communities. In this presentation, we will share the work unfolding in the Republic of the Marshall Islands (RMI) as one example of PCEP's approach to place-based climate education. Three weeklong K-12 teacher professional learning workshops took place during June-July 2015 in Majuro, RMI on learning gardens, climate science, and project-based learning. Each workshop was co-taught with local partners and supports educators in teaching climate-related curriculum standards through tasks that can foster sense of place through observation, experimentation, and application of new knowledge. Additionally, we will also share PCEP's next steps in place-based climate education, specifically around emerging conversations about the importance of highlighting stories of place to generate local solutions for local issues, as well as further global awareness about climate change impacts in the Pacific.

Learning Global Citizenship?: Exploring Connections between the Local and the Global

ERIC Educational Resources Information Center

Mayo, Marjorie; Gaventa, John; Rooke, Alison

2009-01-01

This article identifies historical connections between adult learning, popular education and the emergence of the public sphere in Europe, exploring potential implications for adult learning and community development, drawing upon research evaluating programmes to promote community-based learning "for" active citizenship in UK. The…

Two Frameworks for Preparing Teachers for the Shift from Local to Global Educational Environments

ERIC Educational Resources Information Center

Craig, Barbara; Stevens, Ken

2012-01-01

The research outlined in this paper is based on the convergence of two conceptual frameworks that guide the transfer of knowledge and skills from traditional teacher education, which focused on teaching in single classrooms, to open networked learning environments that include both inter-institutional teaching and learning and local and global…

A Guide to Using Student Learning Objectives as a Locally-Determined Measure of Student Growth

ERIC Educational Resources Information Center

Ohio Department of Education, 2012

2012-01-01

Over the past decade, Ohio has made important education policy advances, with a focus on student learning and achievement, standards, and accountability. Ohio is serious about its commitment to quality schools and honors this commitment by providing Local Education Agencies (LEAs) a research-based, transparent, fair teacher evaluation system…

Global Literacy in Local Learning Contexts: Connecting Home and School. Routledge Research in International and Comparative Education

ERIC Educational Resources Information Center

Mount-Cors, Mary Faith

2016-01-01

Based on qualitative research focused on literacy and health from three schools in coastal Kenya, this book examines country, school, and family contexts to develop a dual-generation maternal-child model for literacy learning and to connect local-specific phenomena with national and international policy arenas. In contrast to international…

Hydrogen Learning for Local Leaders – H2L3

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

Serfass, Patrick

The Hydrogen Learning for Local Leaders program, H2L3, elevates the knowledge about hydrogen by local government officials across the United States. The program reaches local leaders directly through “Hydrogen 101” workshops and webinar sessions; the creation and dissemination of a unique report on the hydrogen and fuel cell market in the US, covering 57 different sectors; and support of the Hydrogen Student Design Contest, a competition for interdisciplinary teams of university students to design hydrogen and fuel cell systems based on technology that’s currently commercially available.

Development of inquiry-based learning activities integrated with the local learning resource to promote learning achievement and analytical thinking ability of Mathayomsuksa 3 student

NASA Astrophysics Data System (ADS)

Sukji, Paweena; Wichaidit, Pacharee Rompayom; Wichaidit, Sittichai

2018-01-01

The objectives of this study were to: 1) compare learning achievement and analytical thinking ability of Mathayomsuksa 3 students before and after learning through inquiry-based learning activities integrated with the local learning resource, and 2) compare average post-test score of learning achievement and analytical thinking ability to its cutting score. The target of this study was 23 Mathayomsuksa 3 students who were studying in the second semester of 2016 academic year from Banchatfang School, Chainat Province. Research instruments composed of: 1) 6 lesson plans of Environment and Natural Resources, 2) the learning achievement test, and 3) analytical thinking ability test. The results showed that 1) student' learning achievement and analytical thinking ability after learning were higher than that of before at the level of .05 statistical significance, and 2) average posttest score of student' learning achievement and analytical thinking ability were higher than its cutting score at the level of .05 statistical significance. The implication of this research is for science teachers and curriculum developers to design inquiry activities that relate to student's context.

Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

NASA Astrophysics Data System (ADS)

Hannel, Mark D.; Abdulali, Aidan; O'Brien, Michael; Grier, David G.

2018-06-01

Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

Re-Engaging Marginalised Young People in Learning: The Contribution of Informal Learning and Community-Based Collaborations

ERIC Educational Resources Information Center

Hayes, Debra

2012-01-01

In the twenty-first century, a socially just system of schooling prepares all young people to adapt to new technologies, and participate in a global economy that is highly differentiated at the local level. In Australia and other countries where local markets have become heavily dependent on service economies, "working-class" families…

Learning from the coffee shop: increasing junior high school students’ self-confidence through contextual learning based on local culture of Aceh

NASA Astrophysics Data System (ADS)

Sarmini; Supriono, A.; Ridwan

2018-01-01

Teachers should be able to provide meaningful learning, create a fun learning, and encourage the self-confidence of students. The reality is learning in Junior High School still teacher-centered learning that results the level of self-confidence of students is low. Pre-action showed 30% of students do not have self-confidence. The research aims to improve the self-confidence of students through contextual learning in the course from the social studies of Aceh based on the local culture. This type of research is classroom action research that conducted in two cycles. The research focus is the students’ responses. The coffee shop is a source of learning social studies. Students Involved in the coffee shop interact with villagers who have made the coffee shop as social media. Students participate meetings to address issues of rural villagers. The coffee shop as a public share with characteristics of particularly subject as a gathering place for many people regardless of social strata, convey information, chat, and informal atmosphere that stimulates self-confidence.

Learning Culture, Spirituality and Local Knowledge: Implications for African Schooling

NASA Astrophysics Data System (ADS)

Sefa Dei, George J.

2002-09-01

(Learning, Culture, Spirituality and Local Knowedge: Implications for African Schooling) - Using a Ghanaian case study, this paper looks at the relevance and implications of local knowledge, culture and spirituality for understanding and implementing educational change in Africa. It examines how teachers, educators, and students use local cultural knowledge about self, personhood and community. Among the critical issues raised are: How do subjects understand the nature, impact and implications of spirituality for schooling and education? What is the role of spirituality, culture, language and social politics in knowledge production? What contribution does the local cultural knowledge base make to the search for genuine educational options in Africa?

The effectivenes of science domain-based science learning integrated with local potency

NASA Astrophysics Data System (ADS)

Kurniawati, Arifah Putri; Prasetyo, Zuhdan Kun; Wilujeng, Insih; Suryadarma, I. Gusti Putu

2017-08-01

This research aimed to determine the significant effect of science domain-based science learning integrated with local potency toward science process skills. The research method used was a quasi-experimental design with nonequivalent control group design. The population of this research was all students of class VII SMP Negeri 1 Muntilan. The sample of this research was selected through cluster random sampling, namely class VII B as an experiment class (24 students) and class VII C as a control class (24 students). This research used a test instrument that was adapted from Agus Dwianto's research. The aspect of science process skills in this research was observation, classification, interpretation and communication. The analysis of data used the one factor anova at 0,05 significance level and normalized gain score. The significance level result of science process skills with one factor anova is 0,000. It shows that the significance level < alpha (0,05). It means that there was significant effect of science domain-based science learning integrated with local potency toward science learning process skills. The results of analysis show that the normalized gain score are 0,29 (low category) in control class and 0,67 (medium category) in experiment class.

Sweetgrass Science

ERIC Educational Resources Information Center

Veal, William; Nagy, Steven

2012-01-01

Place-based education is a form of teaching and learning that allows the teacher to understand the cultural norms of the learners and ensure that cultural norms and local content are reproduced within the classroom so that learning is meaningful, student-centered, and applicable. The traditional definition of place-based education focused on…

Global environmental crisis: is there a connection with place-based, ecosociocultural education in rural Spain?

NASA Astrophysics Data System (ADS)

Glasson, George E.

2011-06-01

Environmental educators are challenged by how to teach children about global environmental crisis such as the Gulf oil spill, which only serves to engender children's fears and apprehensions about the negative impact of humans on ecosystems. Eduardo Dopico and Eva Garcia-Vazquez's article presents an interesting context from which to analyze and reflect on the connections between local and global environmental education issues. The authors' study involves student researchers in actively learning about place-based, sustainable agricultural practices in rural Spain that are passed down through generations. These ecofriendly, culturally mediated farming practices, referred to as "traditional" by the farmers, were contrasted to "modern" practices that are used throughout market-based globalized economy. The connection between local (traditional) and global (modern) practices became very important in the reflections and learning of the student participants about sustainability and ecojustice issues associated with traditional farming. Students learned from the local farmers a positive, non-dualistic approach to sustainable agriculture in which human activity and culture is connected to ecological sustainability. Further, the students' active research of sustainable and culturally medicated agricultural practices at the local level provided a frame of reference to understand global environmental crises.

Tracking Immanent Language Learning Behavior Over Time in Task-Based Classroom Work

ERIC Educational Resources Information Center

Kunitz, Silvia; Marian, Klara Skogmyr

2017-01-01

In this study, the authors explore how classroom tasks that are commonly used in task-based language teaching (TBLT) are achieved as observable aspects of "local educational order" (Hester & Francis, 2000) through observable and immanently social classroom behaviors. They focus specifically on students' language learning behaviors,…

Practice and Educational Effect of a Project Based Learning Program Collaborated with Local Enterprises

NASA Astrophysics Data System (ADS)

Nagashima, Masaaki; Kondo, Yasuo; Tanaka, Hisataka; Miyachika, Kouitsu; Akiyama, Masahiko; Ishibuchi, Nobutaka; Hayakawa, Motozo

The ICEE (Innovation Center for Engineering Education) was founded in April 2004 as an educational facility in the Faculty of Engineering of Tottori University. The ICEE plans the development and training of creative professionals in all fields of engineering through Project Based Learning (PBL) programs in collaboration with local enterprises. In this report, the outline and the educational effect of the education program are described. Through PBL programs, we can give problem finding and solving abilities, self-initiative and communicative skill to the students.

Students' Learning Style: A Case Study of Senior High Schools in Bengkulu

ERIC Educational Resources Information Center

Arsyad, Safnil

2018-01-01

It is widely accepted that the use of learning materials which accommodates students' schemata is much more effective than the ones outside student's present knowledge background. The objectives of this study are to describe the students' learning style distribution and their perception on local oriented and learning style-based English learning…

Design, Participation, and Social Change: What Design in Grassroots Spaces Can Teach Learning Scientists

ERIC Educational Resources Information Center

Zavala, Miguel

2016-01-01

While a science of design (and theory of learning) is certainly useful in design-based research, a participatory design research framework presents an opening for learning scientists to rethink design and learning as processes. Grounded in the autoethnographic investigation of a grassroots organization's design of a local campaign, the author…

Learning non-local dependencies.

PubMed

Kuhn, Gustav; Dienes, Zoltán

2008-01-01

This paper addresses the nature of the temporary storage buffer used in implicit or statistical learning. Kuhn and Dienes [Kuhn, G., and Dienes, Z. (2005). Implicit learning of nonlocal musical rules: implicitly learning more than chunks. Journal of Experimental Psychology-Learning Memory and Cognition, 31(6) 1417-1432] showed that people could implicitly learn a musical rule that was solely based on non-local dependencies. These results seriously challenge models of implicit learning that assume knowledge merely takes the form of linking adjacent elements (chunking). We compare two models that use a buffer to allow learning of long distance dependencies, the Simple Recurrent Network (SRN) and the memory buffer model. We argue that these models - as models of the mind - should not be evaluated simply by fitting them to human data but by determining the characteristic behaviour of each model. Simulations showed for the first time that the SRN could rapidly learn non-local dependencies. However, the characteristic performance of the memory buffer model rather than SRN more closely matched how people came to like different musical structures. We conclude that the SRN is more powerful than previous demonstrations have shown, but it's flexible learned buffer does not explain people's implicit learning (at least, the affective learning of musical structures) as well as fixed memory buffer models do.

Exploring the Characteristics of a Current Career Training Program at the United Arab Emirates University: A Study of Current Senior Students and Local Employers

ERIC Educational Resources Information Center

Alhawasin, Mohamed

2010-01-01

Collaborations between universities and businesses continue to be a vital and critical indicator of the transition in learning from school-based learning to work-based learning. Most jobs today require postsecondary education, forcing many high school students to enroll in a higher education institution in order to advance their careers. However,…

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

PubMed

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Learning French through Ethnolinguistic Activities and Individual Support

ERIC Educational Resources Information Center

Lafond, Celia; Bovey, Nadia Spang

2013-01-01

For the last six years, the university has been offering a Tutorial Programme for learning French, combining intensive courses and highly individualised learning activities. The programme is based on an ethnolinguistic approach and it is continuously monitored. It aims at rapid progress through contact with the local population, real-life…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

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

Learning from Dealing with Real World Problems

ERIC Educational Resources Information Center

Akcay, Hakan

2017-01-01

The purpose of this article is to provide an example of using real world issues as tools for science teaching and learning. Using real world issues provides students with experiences in learning in problem-based environments and encourages them to apply their content knowledge to solving current and local problems.

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

PubMed

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

PubMed Central

Clune, Jeff

2017-01-01

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting. PMID:29145413

Place-based Learning About Climate with Elementary GLOBE

NASA Astrophysics Data System (ADS)

Hatheway, B.; Gardiner, L. S.; Harte, T.; Stanitski, D.; Taylor, J.

2017-12-01

Place-based education - helping students make connections between themselves, their community, and their local environment - is an important tool to help young learners understand their regional climate and start to learn about climate and environmental change. Elementary GLOBE storybooks and learning activities allow opportunities for place-based education instructional strategies about climate. In particular, two modules in the Elementary GLOBE unit - Seasons and Climate - provide opportunities for students to explore their local climate and environment. The storybooks and activities also make connections to other parts of elementary curriculum, such as arts, geography, and math. Over the long term, place-based education can also encourage students to be stewards of their local environment. A strong sense of place may help students to see themselves as stakeholders in their community and its resilience. In places that are particularly vulnerable to the impacts of climate and environmental change and the economic, social, and environmental tradeoffs of community decisions, helping young students developing a sense of place and to see the connection between Earth science, local community, and their lives can have a lasting impact on how a community evolves for decades to come. Elementary GLOBE was designed to help elementary teachers (i.e., grades K-4) integrate Earth system science topics into their curriculum as they teach literacy skills to students. This suite of instructional materials includes seven modules. Each module contains a science-based storybook and learning activities that support the science content addressed in the storybooks. Elementary GLOBE modules feature air quality, climate, clouds, Earth system, seasons, soil, and water. New eBooks allow students to read stories on computers or tablets, with the option of listening to each story with an audio recording. A new Elementary GLOBE Teacher Implementation Guide, published in 2017, provides educators with information and strategies how Elementary GLOBE modules can be effectively applied in classrooms, how Elementary GLOBE modules are aligned with national standards, and how student literacy and science inquiry skills can be strengthened while learning about the Earth system.

Teaching, Learning, and Leading with Schools and Communities: Preparing Sophisticated and Resilient Elementary STEM Educators

ERIC Educational Resources Information Center

Smetana, Lara K.; Coleman, Elizabeth R.; Ryan, Ann Marie; Tocci, Charles

2013-01-01

Loyola University Chicago's Teaching, Learning, and Leading With Schools and Communities (TLLSC) program is an ambitious break from traditional university-based teacher preparation models. This clinically based initial teacher preparation program, fully embedded in local schools and community organizations, takes an ecological perspective on the…

Engaged Institutions: Impacting the Lives of Vulnerable Youth through Place-Based Learning.

ERIC Educational Resources Information Center

Rural School and Community Trust, Washington, DC.

Six case studies examine the connections between higher education institutions and schools that have chosen place-based education as a framework for student learning and community growth. Through such partnerships, Lubec (Maine) high school has established a vocational aquaculture program in an effort to revitalize the struggling local fishing…

Preservice Teachers' Observations of Children's Learning during Family Math Night

ERIC Educational Resources Information Center

Kurz, Terri L.; Kokic, Ivana Batarelo

2011-01-01

Family math night can easily be implemented into mathematics methodology courses providing an opportunity for field-based learning. Preservice teachers were asked to develop and implement an inquiry-based activity at a family math night event held at a local school with personnel, elementary children and their parents in attendance. This action…

From Malaysia to America: Community-Based Character Education for Children and Youth

ERIC Educational Resources Information Center

Haslip, Meishi Lim; Haslip, Michael J.

2013-01-01

This article shares lessons learned from the implementation of a community-based character education program in Malaysia. The program at Jenjarom Learning Center is directed toward the transformation and empowerment of local children and youth through moral and character education. The stated purpose of the program has been to awaken the…

Habituation based synaptic plasticity and organismic learning in a quantum perovskite.

PubMed

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; Li, Jiarui; Kang, Mingu; Mazzoli, Claudio; Zhou, Hua; Barbour, Andi; Wilkins, Stuart; Narayanan, Badri; Cherukara, Mathew; Zhang, Zhen; Sankaranarayanan, Subramanian K R S; Comin, Riccardo; Rabe, Karin M; Roy, Kaushik; Ramanathan, Shriram

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO 3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen-induced electron localization.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

EarthShapes: Potential for Place-Based Teacher Learning between the Virtual and the Actual

ERIC Educational Resources Information Center

Triggs, Valerie

2009-01-01

This contribution investigates a recent research project involving in-service teacher learning as experienced through an online/offline art studio in which common experiences of relationships to particular local landforms generate imaginative and collaborative processes and practices of teaching and learning. EarthShapes Studio is both a…

Predictable Locations Aid Early Object Name Learning

ERIC Educational Resources Information Center

Benitez, Viridiana L.; Smith, Linda B.

2012-01-01

Expectancy-based localized attention has been shown to promote the formation and retrieval of multisensory memories in adults. Three experiments show that these processes also characterize attention and learning in 16- to 18-month old infants and, moreover, that these processes may play a critical role in supporting early object name learning. The…

Attending Globally or Locally: Incidental Learning of Optimal Visual Attention Allocation

ERIC Educational Resources Information Center

Beck, Melissa R.; Goldstein, Rebecca R.; van Lamsweerde, Amanda E.; Ericson, Justin M.

2018-01-01

Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF)…

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

Fractal dimension based damage identification incorporating multi-task sparse Bayesian learning

NASA Astrophysics Data System (ADS)

Huang, Yong; Li, Hui; Wu, Stephen; Yang, Yongchao

2018-07-01

Sensitivity to damage and robustness to noise are critical requirements for the effectiveness of structural damage detection. In this study, a two-stage damage identification method based on the fractal dimension analysis and multi-task Bayesian learning is presented. The Higuchi’s fractal dimension (HFD) based damage index is first proposed, directly examining the time-frequency characteristic of local free vibration data of structures based on the irregularity sensitivity and noise robustness analysis of HFD. Katz’s fractal dimension is then presented to analyze the abrupt irregularity change of the spatial curve of the displacement mode shape along the structure. At the second stage, the multi-task sparse Bayesian learning technique is employed to infer the final damage localization vector, which borrow the dependent strength of the two fractal dimension based damage indication information and also incorporate the prior knowledge that structural damage occurs at a limited number of locations in a structure in the absence of its collapse. To validate the capability of the proposed method, a steel beam and a bridge, named Yonghe Bridge, are analyzed as illustrative examples. The damage identification results demonstrate that the proposed method is capable of localizing single and multiple damages regardless of its severity, and show superior robustness under heavy noise as well.

An e-learning application on electrochemotherapy

PubMed Central

Corovic, Selma; Bester, Janez; Miklavcic, Damijan

2009-01-01

Background Electrochemotherapy is an effective approach in local tumour treatment employing locally applied high-voltage electric pulses in combination with chemotherapeutic drugs. In planning and performing electrochemotherapy a multidisciplinary expertise is required and collaboration, knowledge and experience exchange among the experts from different scientific fields such as medicine, biology and biomedical engineering is needed. The objective of this study was to develop an e-learning application in order to provide the educational content on electrochemotherapy and its underlying principles and to support collaboration, knowledge and experience exchange among the experts involved in the research and clinics. Methods The educational content on electrochemotherapy and cell and tissue electroporation was based on previously published studies from molecular dynamics, lipid bilayers, single cell level and simplified tissue models to complex biological tissues and research and clinical results of electrochemotherapy treatment. We used computer graphics such as model-based visualization (i.e. 3D numerical modelling using finite element method) and 3D computer animations and graphical illustrations to facilitate the representation of complex biological and physical aspects in electrochemotherapy. The e-learning application is integrated into an interactive e-learning environment developed at our institution, enabling collaboration and knowledge exchange among the users. We evaluated the designed e-learning application at the International Scientific workshop and postgraduate course (Electroporation Based Technologies and Treatments). The evaluation was carried out by testing the pedagogical efficiency of the presented educational content and by performing the usability study of the application. Results The e-learning content presents three different levels of knowledge on cell and tissue electroporation. In the first part of the e-learning application we explain basic principles of electroporation process. The second part provides educational content about importance of modelling and visualization of local electric field in electroporation-based treatments. In the third part we developed an interactive module for visualization of local electric field distribution in 3D tissue models of cutaneous tumors for different parameters such as voltage applied, distance between electrodes, electrode dimension and shape, tissue geometry and electric conductivity. The pedagogical efficiency assessment showed that the participants improved their level of knowledge. The results of usability evaluation revealed that participants found the application simple to learn, use and navigate. The participants also found the information provided by the application easy to understand. Conclusion The e-learning application we present in this article provides educational material on electrochemotherapy and its underlying principles such as cell and tissue electroporation. The e-learning application is developed to provide an interactive educational content in order to simulate the "hands-on" learning approach about the parameters being important for successful therapy. The e-learning application together with the interactive e-learning environment is available to the users to provide collaborative and flexible learning in order to facilitate knowledge exchange among the experts from different scientific fields that are involved in electrochemotherapy. The modular structure of the application allows for upgrade with new educational content collected from the clinics and research, and can be easily adapted to serve as a collaborative e-learning tool also in other electroporation-based treatments such as gene electrotransfer, gene vaccination, irreversible tissue ablation and transdermal gene and drug delivery. The presented e-learning application provides an easy and rapid approach for information, knowledge and experience exchange among the experts from different scientific fields, which can facilitate development and optimisation of electroporation-based treatments. PMID:19843322

An e-learning application on electrochemotherapy.

PubMed

Corovic, Selma; Bester, Janez; Miklavcic, Damijan

2009-10-20

Electrochemotherapy is an effective approach in local tumour treatment employing locally applied high-voltage electric pulses in combination with chemotherapeutic drugs. In planning and performing electrochemotherapy a multidisciplinary expertise is required and collaboration, knowledge and experience exchange among the experts from different scientific fields such as medicine, biology and biomedical engineering is needed. The objective of this study was to develop an e-learning application in order to provide the educational content on electrochemotherapy and its underlying principles and to support collaboration, knowledge and experience exchange among the experts involved in the research and clinics. The educational content on electrochemotherapy and cell and tissue electroporation was based on previously published studies from molecular dynamics, lipid bilayers, single cell level and simplified tissue models to complex biological tissues and research and clinical results of electrochemotherapy treatment. We used computer graphics such as model-based visualization (i.e. 3D numerical modelling using finite element method) and 3D computer animations and graphical illustrations to facilitate the representation of complex biological and physical aspects in electrochemotherapy. The e-learning application is integrated into an interactive e-learning environment developed at our institution, enabling collaboration and knowledge exchange among the users. We evaluated the designed e-learning application at the International Scientific workshop and postgraduate course (Electroporation Based Technologies and Treatments). The evaluation was carried out by testing the pedagogical efficiency of the presented educational content and by performing the usability study of the application. The e-learning content presents three different levels of knowledge on cell and tissue electroporation. In the first part of the e-learning application we explain basic principles of electroporation process. The second part provides educational content about importance of modelling and visualization of local electric field in electroporation-based treatments. In the third part we developed an interactive module for visualization of local electric field distribution in 3D tissue models of cutaneous tumors for different parameters such as voltage applied, distance between electrodes, electrode dimension and shape, tissue geometry and electric conductivity. The pedagogical efficiency assessment showed that the participants improved their level of knowledge. The results of usability evaluation revealed that participants found the application simple to learn, use and navigate. The participants also found the information provided by the application easy to understand. The e-learning application we present in this article provides educational material on electrochemotherapy and its underlying principles such as cell and tissue electroporation. The e-learning application is developed to provide an interactive educational content in order to simulate the "hands-on" learning approach about the parameters being important for successful therapy. The e-learning application together with the interactive e-learning environment is available to the users to provide collaborative and flexible learning in order to facilitate knowledge exchange among the experts from different scientific fields that are involved in electrochemotherapy. The modular structure of the application allows for upgrade with new educational content collected from the clinics and research, and can be easily adapted to serve as a collaborative e-learning tool also in other electroporation-based treatments such as gene electrotransfer, gene vaccination, irreversible tissue ablation and transdermal gene and drug delivery. The presented e-learning application provides an easy and rapid approach for information, knowledge and experience exchange among the experts from different scientific fields, which can facilitate development and optimisation of electroporation-based treatments.

A Critical Reflection on the Multiple Roles Required to Facilitate Mutual Learning during Service-Learning in Creative Arts Education

ERIC Educational Resources Information Center

Meyer, Merna; Wood, Lesley

2017-01-01

In this article, I critically reflect on my own learning during a community-based, service-learning pilot project, highlighting the multiple roles that were required of me as facilitator. I provided opportunity for student teachers in a Creative Arts module to engage with youth from a local township community. The purpose of the participatory…

Place-Based Science Teaching and Learning: 40 Activities for K-8 Classrooms

ERIC Educational Resources Information Center

Buxton, Cory A.; Provenzo, Eugene F., Jr.

2011-01-01

Grounded in theory and best-practices research, this practical text provides elementary and middle school teachers with 40 place-based activities that will help them to make science learning relevant to their students. This text provides teachers with both a rationale and a set of strategies and activities for teaching science in a local context…

Blocking Spatial Navigation Across Environments That Have a Different Shape

PubMed Central

2015-01-01

According to the geometric module hypothesis, organisms encode a global representation of the space in which they navigate, and this representation is not prone to interference from other cues. A number of studies, however, have shown that both human and non-human animals can navigate on the basis of local geometric cues provided by the shape of an environment. According to the model of spatial learning proposed by Miller and Shettleworth (2007, 2008), geometric cues compete for associative strength in the same manner as non-geometric cues do. The experiments reported here were designed to test if humans learn about local geometric cues in a manner consistent with the Miller-Shettleworth model. Experiment 1 replicated previous findings that humans transfer navigational behavior, based on local geometric cues, from a rectangle-shaped environment to a kite-shaped environment, and vice versa. In Experiments 2 and 3, it was observed that learning about non-geometric cues blocked, and were blocked by, learning about local geometric cues. The reciprocal blocking observed is consistent with associative theories of spatial learning; however, it is difficult to explain the observed effects with theories of global-shape encoding in their current form. PMID:26569017

Development of a Mobile Learning System Based on a Collaborative Problem-Posing Strategy

ERIC Educational Resources Information Center

Sung, Han-Yu; Hwang, Gwo-Jen; Chang, Ya-Chi

2016-01-01

In this study, a problem-posing strategy is proposed for supporting collaborative mobile learning activities. Accordingly, a mobile learning environment has been developed, and an experiment on a local culture course has been conducted to evaluate the effectiveness of the proposed approach. Three classes of an elementary school in southern Taiwan…

Distance Learning as a Tool for Poverty Reduction and Economic Development: A Focus on China and Mexico

ERIC Educational Resources Information Center

Larson, Richard C.; Murray, M. Elizabeth

2008-01-01

This paper uses case studies to focus on distance learning in developing countries as an enabler for economic development and poverty reduction. To provide perspective, we first review the history of telecottages, local technology-equipped facilities to foster community-based learning, which have evolved into "telecenters" or…

Uncertainty Management and Sensemaking as Precursors to Transformative Learning in an International Immersion Service-Learning Experience

ERIC Educational Resources Information Center

Larson, Jan M.; Fay, Martha

2016-01-01

This study is based on an international immersion service-learning/research experience in a remote village in Moldova that provided faculty and students an opportunity to teach journalism and help local students and community representatives create their own online news outlet. Students' existing conceptions were challenged, they experienced…

Measuring Up: How to Track and Evaluate Local Sustainability Projects

EPA Pesticide Factsheets

Learn about two new federal resources to help you measure, track, and report progress, based directly on the experiences of local governments across the country, and hear from one case study taking place in northwest Washington.

Learning about and Practice of Designing Local Data Bases as an Harmonizing Factor.

ERIC Educational Resources Information Center

Neelameghan, A.

This paper provides information workers with some practical approaches to the design, development, and use of local databases that form components of information storage and retrieval systems (ISR) and of automated library operations. Topics discussed include: (1) course objectives for the design and development of local databases for library and…

Incremental social learning in particle swarms.

PubMed

de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco

2011-04-01

Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations.

Ethics and Undergraduate Research in the Study of Religion: Place-Based Pedagogy and Reciprocal Research Relations

ERIC Educational Resources Information Center

Prideaux, Mel

2016-01-01

In the undergraduate religious studies classroom at the University of Leeds students are introduced to the complexity of religion in locality. One of the most engaging ways to do this is through a place-based pedagogy utilizing independent fieldwork as part of the learning process. However, undergraduates, like seasoned researchers, must learn to…

Laying the Foundation for Competency Education: A Policy Guide for the Next Generation Educator Workforce

ERIC Educational Resources Information Center

Pace, Lillian; Worthen, Maria

2014-01-01

This paper provides a vision and set of policy recommendations to help federal, state, and local leaders develop the workforce necessary to support teaching and learning in a competency-based K-12 education system. Part One, Pre-service and Credentialing for K-12 Competency-Based Learning Environments, provides policymakers with a framework and…

Predictable Locations Aid Early Object Name Learning

PubMed Central

Benitez, Viridiana L.; Smith, Linda B.

2012-01-01

Expectancy-based localized attention has been shown to promote the formation and retrieval of multisensory memories in adults. Three experiments show that these processes also characterize attention and learning in 16- to 18- month old infants and, moreover, that these processes may play a critical role in supporting early object name learning. The three experiments show that infants learn names for objects when those objects have predictable rather than varied locations, that infants who anticipate the location of named objects better learn those object names, and that infants integrate experiences that are separated in time but share a common location. Taken together, these results suggest that localized attention, cued attention, and spatial indexing are an inter-related set of processes in young children that aid in the early building of coherent object representations. The relevance of the experimental results and spatial attention for everyday word learning are discussed. PMID:22989872

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Aboriginal health learning in the forest and cultivated gardens: building a nutritious and sustainable food system.

PubMed

Stroink, Mirella L; Nelson, Connie H

2009-01-01

Sustainable food systems are those in which diverse foods are produced in close proximity to a market. A dynamic, adaptive knowledge base that is grounded in local culture and geography and connected to outside knowledge resources is essential for such food systems to thrive. Sustainable food systems are particularly important to remote and Aboriginal communities, where extensive transportation makes food expensive and of poorer nutritional value. The Learning Garden program was developed and run with two First Nation communities in northwestern Ontario. With this program, the team adopted a holistic and experiential model of learning to begin rebuilding a knowledge base that would support a sustainable local food system. The program involved a series of workshops held in each community and facilitated by a community-based coordinator. Topics included cultivated gardening and forest foods. Results of survey data collected from 20 Aboriginal workshop participants are presented, revealing a moderate to low level of baseline knowledge of the traditional food system, and a reliance on the mainstream food system that is supported by food values that place convenience, ease, and price above the localness or cultural connectedness of the food. Preliminary findings from qualitative data are also presented on the process of learning that occurred in the program and some of the insights we have gained that are relevant to future adaptations of this program.

Local Use-Dependent Sleep in Wakefulness Links Performance Errors to Learning

PubMed Central

Quercia, Angelica; Zappasodi, Filippo; Committeri, Giorgia; Ferrara, Michele

2018-01-01

Sleep and wakefulness are no longer to be considered as discrete states. During wakefulness brain regions can enter a sleep-like state (off-periods) in response to a prolonged period of activity (local use-dependent sleep). Similarly, during nonREM sleep the slow-wave activity, the hallmark of sleep plasticity, increases locally in brain regions previously involved in a learning task. Recent studies have demonstrated that behavioral performance may be impaired by off-periods in wake in task-related regions. However, the relation between off-periods in wake, related performance errors and learning is still untested in humans. Here, by employing high density electroencephalographic (hd-EEG) recordings, we investigated local use-dependent sleep in wake, asking participants to repeat continuously two intensive spatial navigation tasks. Critically, one task relied on previous map learning (Wayfinding) while the other did not (Control). Behaviorally awake participants, who were not sleep deprived, showed progressive increments of delta activity only during the learning-based spatial navigation task. As shown by source localization, delta activity was mainly localized in the left parietal and bilateral frontal cortices, all regions known to be engaged in spatial navigation tasks. Moreover, during the Wayfinding task, these increments of delta power were specifically associated with errors, whose probability of occurrence was significantly higher compared to the Control task. Unlike the Wayfinding task, during the Control task neither delta activity nor the number of errors increased progressively. Furthermore, during the Wayfinding task, both the number and the amplitude of individual delta waves, as indexes of neuronal silence in wake (off-periods), were significantly higher during errors than hits. Finally, a path analysis linked the use of the spatial navigation circuits undergone to learning plasticity to off periods in wake. In conclusion, local sleep regulation in wakefulness, associated with performance failures, could be functionally linked to learning-related cortical plasticity. PMID:29666574

Local wisdom of Ngata Toro community in utilizing forest resources as a learning source of biology

NASA Astrophysics Data System (ADS)

Yuliana, Sriyati, Siti; Sanjaya, Yayan

2017-08-01

Indonesian society is a pluralistic society with different cultures and local potencies that exist in each region. Some of local community still adherethe tradition from generation to generation in managing natural resources wisely. The application of the values of local wisdom is necessary to teach back to student to be more respect the culture and local potentials in the region. There are many ways developing student character by exploring local wisdom and implementing them as a learning resources. This study aims at revealing the values of local wisdom Ngata Toro indigenous people of Central Sulawesi Province in managing forest as a source of learning biology. This research was conducted by in-depth interviews, participant non-observation, documentation studies, and field notes. The data were analyzed with triangulation techniques by using a qualitative interaction analysis that is data collection, data reduction, and data display. Ngata Toro local community manage forest by dividing the forest into several zones, those arewana ngkiki, wana, pangale, pahawa pongko, oma, and balingkea accompanied by rules in the management of result-based forest conservation and sustainable utilization. By identifying the purpose of zonation and regulation of the forest, such values as the value of environmental conservation, balance value, sustainable value, and the value of mutual cooperation. These values are implemented as a biological learning resource which derived from the competences standard of analyze the utilization and conservation of the environment.

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

New pathways to physics instruction: Blending a MOOC and in-person discussion to train physics graduate students and postdocs in evidence-based teaching

NASA Astrophysics Data System (ADS)

Goldberg, Bennett

A challenge facing physics education is how to encourage and support the adoption of evidence-based instructional practices that decades of physics education research has shown to be effective. Like many STEM departments, physics departments struggle to overcome the barriers of faculty knowledge, motivation and time; institutional cultures and reward systems; and disciplinary traditions. Research has demonstrated successful transformation of department-level approaches to instruction through local learning communities, in-house expertise, and department administrative support. In this talk, I will discuss how physics and other STEM departments can use a MOOC on evidence-based instruction together with in-person seminar discussions to create a learning community of graduate students and postdocs, and how such communities can affect departmental change in teaching and learning. Four university members of the 21-university network working to prepare future faculty to be both excellent researchers and excellent teachers collaborated on an NSF WIDER project to develop and deliver two massive open online courses (MOOCs) in evidence-based STEM instruction. A key innovation is a new blended mode of delivery where groups of participants engaged with the online content and then meet weekly in local learning communities to discuss content, communicate current experiences, and delve deeper into particular techniques of local interest. The MOOC team supported these so-called MOOC-Centered Learning Communities, or MCLCs, with detailed facilitator guides complete with synopses of online content, learning goals and suggested activities for in-person meetings, as well as virtual MCLC communities for sharing and feedback. In the initial run of the first MOOC, 40 MCLCs were created; in the second run this past fall, more than 80 MCLCs formed. Further, target audiences of STEM graduate students and postdocs completed at a 40-50% rate, indicating the value they place in building their knowledge in evidence-based instruction. We will present data on the impact of being in an MCLC on completion and learning outcomes, as well as data on departmental change in physics supported by MCLCs. Work supported by NSF DUE-1347605.

A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

E-Learning and Instructional Management System Based on Local Computer Networks and Internet

ERIC Educational Resources Information Center

Khazaal, Hasan F.; Abbas, Riyadh A.; Abdulridha, Basim M.; Karam, Marc; Aglan, Heshmat

2014-01-01

This article describes the educational efforts invested at Wasit University (WU), in Wasit, Iraq, in order to make WU the first university in that country to implement campus-wide e-learning, which is essential for any country aiming for progress through the essential goal of "Education For All"; e-learning being economic, far-reaching,…

Learn Global; Serve Local: Student Outcomes from a Community-Based Learning Pedagogy

ERIC Educational Resources Information Center

Pedersen, Paula J.; Meyer, Jenice M.; Hargrave, Michelle

2015-01-01

Although research suggests that service learning (SL) is being incorporated into the college classroom at an increasing rate, faculty often report that they are not convinced of its educational values. There is a lack of research on, and awareness about, what SL is, how it can be incorporated into the curriculum, and the outcomes on student…

LoColms: an innovative approach of enhancing traditional classroom form of education by promoting web-based distance learning in the poorer countries.

PubMed

Ngarambe, Donart; Pan, Yun-he; Chen, De-ren

2003-01-01

There have been numerous attempts recently to promote technology based education (Shrestha, 1997) in the poorer third world countries, but so far all these have not provided a sustainable solution as they are either centered and controlled from abroad and relying solely on foreign donors for their sustenance or they are not web-based, which make distribution problematic, and some are not affordable by most of the local population in these places. In this paper we discuss an application, the Local College Learning Management System (LoColms), which we are developing, that is both sustainable and economical to suit the situation in these countries. The application is a web-based system, and aims at improving the traditional form of education by empowering the local universities. Its economy comes from the fact that it is supported by traditional communication technology, the public switching telephone network system, PSTN, which eliminates the need for packet switched or dedicated private virtual networks (PVN) usually required in similar situations. At a later stage, we shall incorporate ontology and paging tools to improve resource sharing and storage optimization in the Proxy Caches (ProCa) and LoColms servers. The system is based on the client/server paradigm and its infrastructure consists of the PSTN, ProCa, with the learning centers accessing the universities by means of point-to-point protocol (PPP).

APOLLO: a quality assessment service for single and multiple protein models.

PubMed

Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

2011-06-15

We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Place Based STEM: Leveraging Local Resources to Engage K-12 Teachers in Teaching Integrated STEM and for Addressing the Local STEM Pipeline

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

Dr. Louis Nadelson; Anne Louise Seifert; Meagan McKinney

Business, industry, parks, nature settings, government infrastructure, and people, can be invaluable resources for connecting STEM curriculum within context which results in conditions ideal for promoting purposeful learning of authentic STEM content. Thus, community-based STEM resources offer ideal context for teaching STEM content. A benefit of focusing teacher attention on these contextual, content aligned resources is that they are in every community; making place-based STEM education a possibility, regardless of the location of STEM teaching and learning. Further, associating STEM teaching and learning with local resources addresses workforce development and the STEM pipeline by exposing students to STEM careers andmore » applications in their local communities. The desire to align STEM teaching and learning with local STEM related resources guided the design of our week-long integrated STEM K-12 teacher professional development (PD) program, i-STEM. We have completed four years of our i-STEM PD program and have made place-based STEM a major emphasis of our curriculum. This report focuses on the data collected in the fourth year of our program. Our week-long i-STEM PD served over 425 educators last summer (2013), providing them with in depth theme-based integrated STEM short courses which were limited to an average of 15 participants and whole group plenary sessions focused around placed based integrated STEM, inquiry, engineering design, standards and practices of Common Core and 21st Century skills. This state wide PD was distributed in five Idaho community colleges and took place over two weeks. The STEM short courses included topics on engineering for sustainability, using engineering to spark interest in STEM, municipal water systems, health, agriculture, food safety, mining, forestry, energy, and others. Integral to these short courses were field trips designed to connect the K-12 educators to the resources in their local communities that could be leveraged for teaching integrated STEM and provide a relevant context for teaching STEM content. Workplace presentations made by place-based STEM experts and provided teachers field trips to place-base STEM industries and business such as manufacturing plants, waste water treatment systems, mines, nature parks, food processing plants, research, hospitals, and laboratory facilities. We researched the 425 participants’ conceptions of place-based STEM prior to and after their taking part in the summer institutes, which included fieldtrips. Our findings revealed substantial increase in our participants’ knowledge, interest, and plans to use place-based resources for teaching integrated STEM. We detail the data analysis and provide a theoretical foundation and justification for the importance of place-based STEM to address the STEM pipeline for the future workforce.« less

100 Days of Learning in Place: How a Small School Utilized "Place-Based" Learning To Master State Academic Standards.

ERIC Educational Resources Information Center

Lewicki, James

This report describes the development and implementation of a place-based curriculum for a small charter high school of 25 students in Wisconsin. The curriculum involved 100 days of field studies in local places such as historical archives, a restored wetland, a river valley, and a senior citizen community center. The students worked with 60…

An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System.

PubMed

Yin, Shen; Xie, Xiaochen; Lam, James; Cheung, Kie Chung; Gao, Huijun

2016-12-01

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Motion Recognition and Modifying Motion Generation for Imitation Robot Based on Motion Knowledge Formation

NASA Astrophysics Data System (ADS)

Okuzawa, Yuki; Kato, Shohei; Kanoh, Masayoshi; Itoh, Hidenori

A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and modification are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the continuous hidden markov model. When the motion is recognized as being unfamiliar, the second part learns it using locally weighted regression and acquires a knowledge of the motion. When a robot recognizes the instructed motion as familiar or judges that its acquired knowledge is applicable to the motion generation, the third part imitates the instructed motion by modifying a learned motion. This paper reports some performance results: the motion imitation of several radio gymnastics motions.

Evaluating Reactions to Community Bridge Initiative Pilot Classes

ERIC Educational Resources Information Center

Koldewyn, Julie; Brain, Roslynn; Stephens, Kate

2017-01-01

Does participating in an integrated service-learning project aimed at improving local sustainability issues result in significant professional real-world application for students? This study aimed to answer that question by evaluating student reactions to pilot classes featuring a sustainability-based service-learning program, Community Bridge…

Taking the Lead

ERIC Educational Resources Information Center

Sedgmore, Lynne

2007-01-01

Adult and community learning (ACL) covers a wide range of educational provision, much of which is based locally within the community. Teaching staff often come from the same community and have empathy and understanding for learners. There is a strong focus on widening participation, flexible learning and learner-centred provision. It sometimes…

Policy learning for flood mitigation: a longitudinal assessment of the community rating system in Florida.

PubMed

Brody, Samuel D; Zahran, Sammy; Highfield, Wesley E; Bernhardt, Sarah P; Vedlitz, Arnold

2009-06-01

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the degree to which local jurisdictions learn from flood risks and under what circumstances, little if any empirical, longitudinal research has been conducted along these lines. This article addresses the research gap by examining the change in local flood mitigation policies in Florida from 1999 to 2005. We track 18 different mitigation activities organized into four series of activities under the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS) for every local jurisdiction in Florida participating in the FEMA program on a yearly time step. We then identify the major factors contributing to policy changes based on CRS scores over the seven-year study period. Using multivariate statistical models to analyze both natural and social science data, we isolate the effects of several variables categorized into the following groups: hydrologic conditions, flood disaster history, socioeconomic and human capital controls. Results indicate that local jurisdictions do in fact learn from histories of flood risk and this process is expedited under specific conditions.

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

PubMed

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-02-11

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

PubMed Central

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

Local Service Learning in Teacher Preparation Program

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2016-01-01

The local knowledge is simply integrated in education and learning process. This study aims to promote local knowledge in school through service learning. The learning process is employed herbal plants to reinforce students learn how to sustain local knowledge with modern life and 21st century classroom. Participants consisted of 42 pre-service…

Neural network-based multiple robot simultaneous localization and mapping.

PubMed

Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

2011-12-01

In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

Local linear regression for function learning: an analysis based on sample discrepancy.

PubMed

Cervellera, Cristiano; Macciò, Danilo

2014-11-01

Local linear regression models, a kind of nonparametric structures that locally perform a linear estimation of the target function, are analyzed in the context of empirical risk minimization (ERM) for function learning. The analysis is carried out with emphasis on geometric properties of the available data. In particular, the discrepancy of the observation points used both to build the local regression models and compute the empirical risk is considered. This allows to treat indifferently the case in which the samples come from a random external source and the one in which the input space can be freely explored. Both consistency of the ERM procedure and approximating capabilities of the estimator are analyzed, proving conditions to ensure convergence. Since the theoretical analysis shows that the estimation improves as the discrepancy of the observation points becomes smaller, low-discrepancy sequences, a family of sampling methods commonly employed for efficient numerical integration, are also analyzed. Simulation results involving two different examples of function learning are provided.

Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

NASA Astrophysics Data System (ADS)

Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

2017-01-01

Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

Discriminative structural approaches for enzyme active-site prediction.

PubMed

Kato, Tsuyoshi; Nagano, Nozomi

2011-02-15

Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

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

NASA Astrophysics Data System (ADS)

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

2004-12-01

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

Procuring a Sustainable Future: An Action Learning Approach to the Development and Modelling of Ethical and Sustainable Procurement Practices

ERIC Educational Resources Information Center

Boak, George; Watt, Peter; Gold, Jeff; Devins, David; Garvey, Robert

2016-01-01

This paper contributes to an understanding of the processes by which organisational actors learn how to affect positive and sustainable social change in their local region through action learning, action research and appreciative inquiry. The paper is based on a critically reflective account of key findings from an ongoing action research project,…

On adaptive learning rate that guarantees convergence in feedforward networks.

PubMed

Behera, Laxmidhar; Kumar, Swagat; Patnaik, Awhan

2006-09-01

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

NASA Astrophysics Data System (ADS)

Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

2018-01-01

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Research on cardiovascular disease prediction based on distance metric learning

NASA Astrophysics Data System (ADS)

Ni, Zhuang; Liu, Kui; Kang, Guixia

2018-04-01

Distance metric learning algorithm has been widely applied to medical diagnosis and exhibited its strengths in classification problems. The k-nearest neighbour (KNN) is an efficient method which treats each feature equally. The large margin nearest neighbour classification (LMNN) improves the accuracy of KNN by learning a global distance metric, which did not consider the locality of data distributions. In this paper, we propose a new distance metric algorithm adopting cosine metric and LMNN named COS-SUBLMNN which takes more care about local feature of data to overcome the shortage of LMNN and improve the classification accuracy. The proposed methodology is verified on CVDs patient vector derived from real-world medical data. The Experimental results show that our method provides higher accuracy than KNN and LMNN did, which demonstrates the effectiveness of the Risk predictive model of CVDs based on COS-SUBLMNN.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

PubMed Central

Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-01-01

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. PMID:29267252

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

PubMed

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Using Learning Labs for Culturally Responsive Positive Behavioral Interventions and Supports

ERIC Educational Resources Information Center

Bal, Aydin; Schrader, Elizabeth M.; Afacan, Kemal; Mawene, Dian

2016-01-01

Culturally responsive positive behavioral interventions and supports (CRPBIS) is a statewide research project designed to renovate behavioral support systems to become more inclusive, adaptive, and supportive for all. The CRPBIS methodology, called "learning lab," provides a research-based process to bring together local stakeholders and…

A Questionnaire-Based Study on Chinese University Students' Demotivation to Learn English

ERIC Educational Resources Information Center

Li, Chili; Zhou, Ting

2017-01-01

This paper, adopting questionnaire survey method, investigated 367 non-key local university English as a Foreign Language (EFL) students' demotivation to learn English. The collected data revealed that there were two main categories of demotivators: internal factors ("lack of intrinsic interest," "experience of failure and lack of…

A Learning Progression for Water in Socio-Ecological Systems

ERIC Educational Resources Information Center

Gunckel, Kristin L.; Covitt, Beth A.; Salinas, Ivan; Anderson, Charles W.

2012-01-01

Providing model-based accounts (explanations and predictions) of water and substances in water moving through environmental systems is an important practice for environmental science literacy and necessary for citizens confronting global and local water quantity and quality issues. In this article we present a learning progression for water in…

Place-Based Learning and Mobile Technology

ERIC Educational Resources Information Center

LaBelle, Chris

2011-01-01

When delivered on a mobile device, interpretive tours of a locale afford powerful learning experiences. As mobile devices become more powerful, content for these devices that is individualized and location-specific has become more common. In light of this trend, Oregon State University Extension developed a GPS-enabled iPhone tree tour…

Lessons learned in applying ecosystem goods and services to community decision making

EPA Science Inventory

This report is intended to describe lessons learned from the application of FEGS-based research in a series of PBS conducted by EPA’s Office of Research and Development (ORD) and make this information available and useful for planning future research into local decision sup...

Evaluation Report on the Community Learning Center.

ERIC Educational Resources Information Center

Fried, Robert L.

The Community Learning Center (CLC) evaluation is based on on-site visits and interviews with staff and students of widely differing ethnic backgrounds. Teaching resources are varied. The Model Cities program is the basic source for CLC funding; the Cambridge Public Library is the center's local sponsor. The external bureaucratic framework needs…

Alternate Learning Center. Abstracts of Inservice Training Programs.

ERIC Educational Resources Information Center

Rhode Island State Dept. of Education, Providence. Div. of Development and Operations.

This booklet is a collection of abstracts describing the 18 programs offered at the Alternate Learning Center of the Rhode Island Teacher Center which has as its Primary function school based inservice training for local teachers and administrators. Each project is described in detail, including course goals, specific objectives, training…

Constraint-based Temporal Reasoning with Preferences

NASA Technical Reports Server (NTRS)

Khatib, Lina; Morris, Paul; Morris, Robert; Rossi, Francesca; Sperduti, Alessandro; Venable, K. Brent

2005-01-01

Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Learning and coordinating in a multilayer network

PubMed Central

Lugo, Haydée; Miguel, Maxi San

2015-01-01

We introduce a two layer network model for social coordination incorporating two relevant ingredients: a) different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based both on social and strategic motivations. Two populations of agents are distributed in two layers with intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of being the riskier one. PMID:25585934

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PubMed Central

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images. PMID:24989402

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

PubMed

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

Learning locality preserving graph from data.

PubMed

Zhang, Yan-Ming; Huang, Kaizhu; Hou, Xinwen; Liu, Cheng-Lin

2014-11-01

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

PubMed

Taylor, Jonathan Christopher; Fenner, John Wesley

2017-11-29

Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

platform (HPC) was developed, called the HPC-Acoustic Data Accelerator, or HPC-ADA for short. The HPC-ADA was designed based on fielded systems [1-4...software (Detection cLassificaiton for MAchine learning - High Peformance Computing). The software package was designed to utilize parallel and...Sedna [7] and is designed using a parallel architecture2, allowing existing algorithms to distribute to the various processing nodes with minimal changes

Strategy for integration of coastal culture in learning process of mathematics in junior high school

NASA Astrophysics Data System (ADS)

Suyitno, H.; Zaenuri; Florentinus, T. S.; Zakaria, E.

2018-03-01

Traditional life in the fishing family is part of the local culture. Many School-age children in the fishing family drop-outs due to lack of parents motivation and the environment was less supportive. The problems were: (1) How the strategy of integration of local culture in learning process of mathematics in Junior High School (JHS)? (2) How to prepare the Mathematics Student’s Book for grade 7 of JHS that based on coastal culture, that has an ISBN, has international level, applicable, and in accordance with the current curriculum? The purposes of this research were: (1) to obtain the strategy of integration of local culture in learning process of mathematics in JHS, through FGD between UNNES and UKM; (2) to obtain the experts validation, through Focus Group Discussion (FGD) between UNNES and UKM toward the draft of the Mathematics Student’s Book for grade 7 of JHS that based on coastal culture; (3) produces Mathematics Student’s Book for grade 7 SMP which based on coastal culture and has an ISBN, international, applicable, and in accordance with the curriculum. The research activity was a qualitative research, so that the research methods include: (1) data reduction, (2) display data, (3) data interpretation, and (4) conclusion/verification. The main activities of this research: drafting the Mathematics Student’s Book of Grade 7 which based on coastal culture; get the validation from international partners;conducting FGD at Education Faculty of Universiti Kebangsaan Malaysia through the program of visiting lecturers for getting the Mathematics Student’s Book of grade 7 which based on coastal culture, registering for ISBN, and publishing the reasearch results in International seminar and International Journals. The results of this research were as follows. (1) Getting a good strategy for integration of local culture in learning process of mathematics in JHS. (2) Getting the Mathematics Student’s Book for grade 7 of JHS that based on coastal culture, that has an ISBN, international level, applicable, and in accordance with the current curriculum.

Learning from Public Television and the Web: Positioning Continuing Education as a Knowledge Portal.

ERIC Educational Resources Information Center

Vedro, Steven R.

1999-01-01

Digital convergence--the merging of television and computing--challenges localized monopolies of public television and continuing education. Continuing educators can reposition themselves in the electronic marketplace by serving as an educational portal, bringing their strengths of "brand recognition," local customer base, and access to…

Engaging Employers in Apprenticeship Opportunities: Making It Happen Locally

ERIC Educational Resources Information Center

OECD Publishing, 2017

2017-01-01

This joint OECD-ILO publication provides guidance on how local and regional governments can foster business-education partnerships in apprenticeship programmes and other types of work-based learning, drawing on case studies across nine countries. There has been increasing interest in apprenticeships which combine on the job training with…

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

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

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation.

PubMed

Soltoggio, Andrea; Stanley, Kenneth O

2012-10-01

Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Envisioning Science Environment Technology and Society

NASA Astrophysics Data System (ADS)

Maknun, J.; Busono, T.; Surasetja, I.

2018-02-01

Science Environment Technology and Society (SETS) approach helps students to connect science concept with the other aspects. This allows them to achieve a clearer depiction of how each concept is linked with the other concepts in SETS. Taking SETS into account will guide students to utilize science as a productive concept in inventing and developing technology, while minimizing its negative impacts on the environment and society. This article discusses the implementation of Sundanese local wisdoms, that can be found in the local stilt house (rumah panggung), in the Building Construction subject in vocational high school on Building Drawing Technique expertise. The stilt house structural system employs ties, pupurus joints, and wedges on its floor, wall, and truss frames, as well as its beams. This local knowledge was incorporated into the Building Construction learning program and applied on the following basic competences: applying wood’s specification and characteristics for building construction, managing wood’s specification and characteristics for building construction, analyzing building structure’s type and function based on their characteristics, reasoning building structure’s type and function based on their characteristics, categorizing wood construction works, and reasoning wood construction works. The research result is the Sundanese traditional-local-wisdom-based learning design of the Building Construction subject.

Increasing Parent Engagement in Student Learning Using an Intelligent Tutoring System

ERIC Educational Resources Information Center

Broderick, Zachary; O'Connor, Christine; Mulcahy, Courtney; Heffernan, Neil; Heffernan, Christina

2011-01-01

This study demonstrates the ability of an Intelligent Tutoring System (ITS) to increase parental engagement in student learning. A parent notification feature was developed for the web-based ASSISTment ITS that allows parents to log into their own accounts and access detailed data about their students' performance. Parents from a local middle…

Science Education and Sustainability: A Case-Study in Discussion-Based Learning.

ERIC Educational Resources Information Center

Gayford, Chris

1995-01-01

Explores links between science education and current developments in environmental education that take account of the concept of sustainability and the impact of local action on global issues. Uses the greenhouse effect as a case-study example with 16-year-old students. Reports that students learned more effectively using a discussion-based…

Learning Processes and Social Mobilization in a Swedish Metropolitan Hip-Hop Collective

ERIC Educational Resources Information Center

Beach, Dennis; Sernhede, Ove

2012-01-01

Based on ethnographic research on the encounter between local culture and schools in multicultural suburban areas, this article explores possibilities suggested by autonomous learning activities in a hip-hop collective that may have a potential to break urban segregation patterns. The collective's artistic production raises questions that have not…

Lifelong Learning and the New Economy: Rhetoric or Reality?

ERIC Educational Resources Information Center

Cruikshank, Jane

2007-01-01

Historically, lifelong learning (under the name adult education) in Canada had a broad base and covered a wide variety of purposes and activities. Many programs included social, community and social justice visions and worked to strengthen local communities. However, with the advent of the so-called New Economy, this has changed. Canadian…

Using Quality Schemes in Adult and Community Learning: A Guide for Managers.

ERIC Educational Resources Information Center

Ewens, David; Watters, Kate

This document examines adult and community learning (ACL) and quality programs across England. The difficulties faced by local education agencies' ACL services in delivering quality are noted, along with ways quality improvement has been supported. Quality programs--whether internal or external, based on awards, or used as diagnostic tools--are…

Community Education. AONTAS Policy Series.

ERIC Educational Resources Information Center

Irish National Association of Adult Education, Dublin.

Ireland's economic and social problems in the 1980s spawned a new kind of community education. Key characteristics of the new community education are as follows: (1) it is a learning environment and located in the community; (2) it provides learning programs based on identified needs; (3) its control remains in the local community's hands; (4) its…

Supporting students' learning in the domain of computer science

NASA Astrophysics Data System (ADS)

Gasparinatou, Alexandra; Grigoriadou, Maria

2011-03-01

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas high-knowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n = 65) read one of four versions of a text concerning Local Network Topologies, orthogonally varying local and global cohesion. Participants' comprehension was examined through free-recall measure, text-based, bridging-inference, elaborative-inference, problem-solving questions and a sorting task. The results indicated that high-knowledge readers benefited from the low-cohesion text. The interaction of text cohesion and knowledge was reliable for the sorting activity, for elaborative-inference and for problem-solving questions. Although high-knowledge readers performed better in text-based and in bridging-inference questions with the low-cohesion text, the interaction of text cohesion and knowledge was not reliable. The results suggest a more complex view of when and for whom textual cohesion affects comprehension and consequently learning in computer science.

Problem-Based Learning in a Local Church: A Grounded Theory Study of the Korean Sunday School Teachers' Perception of the Effects of Problem-Based Learning in Teacher Education

ERIC Educational Resources Information Center

Lee, Sooin Tim

2012-01-01

There is a hunger for effective teacher equipping programs for adult volunteer teachers in the educational ministry of today's churches. In addition, these programs for volunteer teachers need to be well-suited for adult learners and relevant to their real-life situations. The purpose of this qualitative study is to explore the effects of…

Investigating engagement, thinking, and learning among culturally diverse, urban sixth graders experiencing an inquiry-based science curriculum, contextualized in the local environment

NASA Astrophysics Data System (ADS)

Kelley, Sybil Schantz

This mixed-methods study combined pragmatism, sociocultural perspectives, and systems thinking concepts to investigate students' engagement, thinking, and learning in science in an urban, K-8 arts, science, and technology magnet school. A grant-funded school-university partnership supported the implementation of an inquiry-based science curriculum, contextualized in the local environment through field experiences. The researcher worked as co-teacher of 3 sixth-grade science classes and was deeply involved in the daily routines of the school. The purposes of the study were to build a deeper understanding of the complex interactions that take place in an urban science classroom, including challenges related to implementing culturally-relevant instruction; and to offer insight into the role educational systems play in supporting teaching and learning. The central hypothesis was that connecting learning to meaningful experiences in the local environment can provide culturally accessible points of engagement from which to build science learning. Descriptive measures provided an assessment of students' engagement in science activities, as well as their levels of thinking and learning throughout the school year. Combined with analyses of students' work files and focus group responses, these findings provided strong evidence of engagement attributable to the inquiry-based curriculum. In some instances, degree of engagement was found to be affected by student "reluctance" and "resistance," terms defined but needing further examination. A confounding result showed marked increases in thinking levels coupled with stasis or decrease in learning. Congruent with past studies, data indicated the presence of tension between the diverse cultures of students and the mainstream cultures of school and science. Findings were synthesized with existing literature to generate the study's principal product, a grounded theory model representing the complex, interacting factors involved in teaching and learning. The model shows that to support learning and to overcome cultural tensions, there must be alignment among three main forces or "causal factors": students, teaching, and school climate. Conclusions emphasize system-level changes to support science learning, including individualized support for students in the form of differentiated instruction; focus on excellence in teaching, particularly through career-spanning professional support for teachers; and attention to identifying key leverage points for implementing effective change.

Effective collaborative learning in biomedical education using a web-based infrastructure.

PubMed

Wu, Yunfeng; Zheng, Fang; Cai, Suxian; Xiang, Ning; Zhong, Zhangting; He, Jia; Xu, Fang

2012-01-01

This paper presents a feature-rich web-based system used for biomedical education at the undergraduate level. With the powerful groupware features provided by the wiki system, the instructors are able to establish a community-centered mentoring environment that capitalizes on local expertise to create a sense of online collaborative learning among students. The web-based infrastructure can help the instructors effectively organize and coordinate student research projects, and the groupware features may support the interactive activities, such as interpersonal communications and data sharing. The groupware features also provide the web-based system with a wide range of additional ways of organizing collaboratively developed materials, which makes it become an effective tool for online active learning. Students are able to learn the ability to work effectively in teams, with an improvement of project management, design collaboration, and technical writing skills. With the fruitful outcomes in recent years, it is positively thought that the web-based collaborative learning environment can perform an excellent shift away from the conventional instructor-centered teaching to community- centered collaborative learning in the undergraduate education.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

ESD and lifelong learning: a case study of the Shangri-la Institute's current engagement with the Bazhu community in Diqing, China

NASA Astrophysics Data System (ADS)

Liu, Yunhua; Constable, Alicia

2010-06-01

This article argues that ESD should be integrated into lifelong learning and provides an example of how this might be done. It draws on a case study of a joint project between the Shangri-la Institute and the Bazhu community in Diqing, southwest China, to analyse a community-based approach to Education for Sustainable Development and assess its implications for lifelong learning. The article examines the different knowledge, skills and values needed for ESD across the life span and asserts the need for these competencies to be informed by the local context. The importance of linking ESD with local culture and indigenous knowledge is emphasised. The article goes on to propose methods for integrating ESD into lifelong learning and underscore the need for learning at the individual, institutional and societal levels in formal, non-formal and informal learning settings. It calls for institutional changes that link formal, non-formal and informal learning through the common theme of ESD, and establish platforms to share experiences, reflect on these and thereby continually improve ESD.

Ground-based cloud classification by learning stable local binary patterns

NASA Astrophysics Data System (ADS)

Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua

2018-07-01

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Joint deep shape and appearance learning: application to optic pathway glioma segmentation

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Li, Ien; Packer, Roger J.; Avery, Robert A.; Linguraru, Marius George

2017-03-01

Automated tissue characterization is one of the major applications of computer-aided diagnosis systems. Deep learning techniques have recently demonstrated impressive performance for the image patch-based tissue characterization. However, existing patch-based tissue classification techniques struggle to exploit the useful shape information. Local and global shape knowledge such as the regional boundary changes, diameter, and volumetrics can be useful in classifying the tissues especially in scenarios where the appearance signature does not provide significant classification information. In this work, we present a deep neural network-based method for the automated segmentation of the tumors referred to as optic pathway gliomas (OPG) located within the anterior visual pathway (AVP; optic nerve, chiasm or tracts) using joint shape and appearance learning. Voxel intensity values of commonly used MRI sequences are generally not indicative of OPG. To be considered an OPG, current clinical practice dictates that some portion of AVP must demonstrate shape enlargement. The method proposed in this work integrates multiple sequence magnetic resonance image (T1, T2, and FLAIR) along with local boundary changes to train a deep neural network. For training and evaluation purposes, we used a dataset of multiple sequence MRI obtained from 20 subjects (10 controls, 10 NF1+OPG). To our best knowledge, this is the first deep representation learning-based approach designed to merge shape and multi-channel appearance data for the glioma detection. In our experiments, mean misclassification errors of 2:39% and 0:48% were observed respectively for glioma and control patches extracted from the AVP. Moreover, an overall dice similarity coefficient of 0:87+/-0:13 (0:93+/-0:06 for healthy tissue, 0:78+/-0:18 for glioma tissue) demonstrates the potential of the proposed method in the accurate localization and early detection of OPG.

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

PubMed Central

Zamli, Kamal Z.; Din, Fakhrud; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level. PMID:29771918

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem.

PubMed

Zamli, Kamal Z; Din, Fakhrud; Ahmed, Bestoun S; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Paths through interpretive territory: Two teachers' enactment of a technology-rich, inquiry-fostering science curriculum

NASA Astrophysics Data System (ADS)

McDonald, Scott Powell

New understandings about how people learn and constructivist pedagogy pose challenges for teachers. Science teachers face an additional challenge of developing inquiry-based pedagogy to foster complex reasoning skills. Theory provides only fuzzy guidance as to how constructivist or inquiry pedagogy can be accomplished in a wide variety of contexts and local constraints. This study contributes to the understanding of the development of constructivist, inquiry-based pedagogy by addressing the question: How do teachers interpret and enact a technology-rich, inquiry fostering science curricula for fifth grade students' biodiversity learning? This research is a case study of two teachers chosen as critical contrasting cases and represent differences across multiple criteria including: urban I suburban, teaching philosophy, and content preparation. The two fifth grade teachers each enacted BioKIDS: Kids' Inquiry in Diverse Species, an eight week curriculum focused on biodiversity. BioKIDS incorporates multiple learning technologies to support student learning including handheld computer software designed to help students collect field data, and a web-based resource for data on local animal species. The results of this study indicate there are tensions teachers must struggle with when setting goals during enactment of inquiry science curricula. They must find a balance between an emphasis on authentic learning and authentic science, and between natural history and natural science. Authentic learning focuses on students' interests and lives; Authentic science focuses on students working with the tools and processes of science. Natural history focuses on the foundational skills in science of observation and classification. Natural science focuses on analytical science drawing on data to develop claims about the world. These two key tensions in teachers' goal setting were critical in defining and understanding differences in how teachers interpreted a curriculum to meet local context and constraints. This study also examined how teachers used technology and scientific inscriptions to support their goals. Implications for research in science education as well as design of curricula and technology are discussed.

Learning to recognize objects on the fly: a neurally based dynamic field approach.

PubMed

Faubel, Christian; Schöner, Gregor

2008-05-01

Autonomous robots interacting with human users need to build and continuously update scene representations. This entails the problem of rapidly learning to recognize new objects under user guidance. Based on analogies with human visual working memory, we propose a dynamical field architecture, in which localized peaks of activation represent objects over a small number of simple feature dimensions. Learning consists of laying down memory traces of such peaks. We implement the dynamical field model on a service robot and demonstrate how it learns 30 objects from a very small number of views (about 5 per object are sufficient). We also illustrate how properties of feature binding emerge from this framework.

Collaborating Fuzzy Reinforcement Learning Agents

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

Earlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.

Adaptive local linear regression with application to printer color management.

PubMed

Gupta, Maya R; Garcia, Eric K; Chin, Erika

2008-06-01

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

L1-norm locally linear representation regularization multi-source adaptation learning.

PubMed

Tao, Jianwen; Wen, Shiting; Hu, Wenjun

2015-09-01

In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. Copyright © 2015 Elsevier Ltd. All rights reserved.

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

EpiAssist: Service-learning in public health education.

PubMed

Horney, Jennifer A; Bamrara, Sanjana; Macik, Maria Lazo; Shehane, Melissa

2016-01-01

Although public health degree programs typically require practica and other field experiences, service-learning courses, with a focus on civic engagement and the application of classroom learning in real world settings, can go beyond these requirements and provide benefits to students and community-based practice partners. The goal of this paper is to assess potential benefits of service-learning programs for both graduate-level public health students and state and local public health agency partners. EpiAssist is a new service-learning program developed at the School of Public Health of the Texas A and M University Health Science Center, USA, in January 2015. EpiAssist was integrated into a new course, Methods in Field Epidemiology. The integration of service-learning was guided by a partnership with the Texas A and M Center for Teaching Excellence. State, regional, and local public health partners requested EpiAssist via email or telephone. A listserv was used to recruit student volunteers to meet requests. 54 of 86 registered EpiAssist students (63%) participated in at least one of ten service-learning and three training activities between January and June, 2015. Service-learning activities included questionnaire development, in-person and telephone data collection, and data analysis. Training topics for students included the Epi Info™ software, community assessment and communicable disease reporting. Students and partner organizations provided generally positive assessments of this service learning program through an online evaluation. Service-learning provides students with enhanced classroom learning through applied public health experience in state, regional and local health departments. These experiences provide both needed surge capacity to public health departments and valuable hands-on field experience to students.

Engaging with Employers in Work-Based Learning: A Foundation Degree in Applied Technology

ERIC Educational Resources Information Center

Benefer, Richard

2007-01-01

Purpose: This paper aims to describe the work of Staffordshire University in engaging with local employers and local further education colleges in the development of a Foundation Degree in Applied Technology. Design/methodology/approach: Following an outline of current government policy in employer engagement, the paper identifies--from the…

Becoming an Evidence-Based Practitioner: A Framework for Teacher-Researchers.

ERIC Educational Resources Information Center

McNamara, Olwen, Ed.

This book presents case studies of classroom research into the teaching and learning of English, mathematics, and sciences, drawing on the experiences of teacher researchers who, in partnership with their local education agencies and local universities, set out to intervene in key areas of the primary curriculum. After "Introduction: Inviting…

Local Studies for the Slow Learner: The "Our Town" Experience.

ERIC Educational Resources Information Center

Evans, Stanley R.

Considering that the lower ability student in the seventh and eighth grades faces problems of emotion, identity, and self-motivation, there is a need for a more activity-based, exploratory curriculum that gives students responsibility for their learning and living. An interdisciplinary local studies approach was adopted for this six-week unit…

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Context-aware and locality-constrained coding for image categorization.

PubMed

Xiao, Wenhua; Wang, Bin; Liu, Yu; Bao, Weidong; Zhang, Maojun

2014-01-01

Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts.

Transcranial magnetic stimulation assisted by neuronavigation of magnetic resonance images

NASA Astrophysics Data System (ADS)

Viesca, N. Angeline; Alcauter, S. Sarael; Barrios, A. Fernando; González, O. Jorge J.; Márquez, F. Jorge A.

2012-10-01

Technological advance has improved the way scientists and doctors can learn about the brain and treat different disorders. A non-invasive method used for this is Transcranial Magnetic Stimulation (TMS) based on neuron excitation by electromagnetic induction. Combining this method with functional Magnetic Resonance Images (fMRI), it is intended to improve the localization technique of cortical brain structures by designing an extracranial localization system, based on Alcauter et al. work.

The Development of Biology Teaching Material Based on the Local Wisdom of Timorese to Improve Students Knowledge and Attitude of Environment in Caring the Preservation of Environment

ERIC Educational Resources Information Center

Ardan, Andam S.

2016-01-01

The purposes of this study were (1) to describe the biology learning such as lesson plans, teaching materials, media and worksheets for the tenth grade of High School on the topic of Biodiversity and Basic Classification, Ecosystems and Environment Issues based on local wisdom of Timorese; (2) to analyze the improvement of the environmental…

Experiential Learning in Education for Sustainable Development: Experiences from a Czech-Kazakh Social Learning Programme

ERIC Educational Resources Information Center

Cincera, Jan

2013-01-01

The article presents experience from a joint Czech-Kazakh project based on experiential education. The goal of the project was to develop trust and cooperation between various stakeholders to promote effective public participation in local sustainable development issues in Kazakhstan. The article describes the methodology of the programme and its…

Mediating Effects of Trust, Communication, and Collaboration on Teacher Professional Learning in Hong Kong Primary Schools

ERIC Educational Resources Information Center

Li, Lijuan; Hallinger, Philip; Kennedy, Kerry John; Walker, Allan

2017-01-01

This study tests mediated principal leadership effects on teacher professional learning through collegial trust, communication and collaboration in Hong Kong primary schools. It is based on a series of single mediator studies, and uses the same convenience sample of 970 teachers from 32 local primary schools. It also adopts regression-based…

Learning Places and "Little Volunteers": An Assessment of Place- and Community-Based Education in China

ERIC Educational Resources Information Center

Efird, Rob

2015-01-01

In 2003, China's Ministry of Education mandated environmental education in all subjects at all levels in Chinese public schools and explicitly encouraged teachers to engage their students in hands-on learning in their local communities. However, a number of obstacles--including an intense preoccupation with test scores and student safety--make…

Teaching Students in Place: The Languages of Third Space Learning

ERIC Educational Resources Information Center

Morawski, Cynthia M.

2017-01-01

With a perceptive eye cast on geoscience pedagogy for students labeled as disabled, Martinez-Álvarez makes important contributions to the existing conversation on placed-based learning. It is in our local backyards, from the corner basketball court, to the mud bank of a city lake, to the adjacent field where rocky outcrops spill down to a…

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

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

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integratemore » the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.« less

Contour Tracking in Echocardiographic Sequences via Sparse Representation and Dictionary Learning

PubMed Central

Huang, Xiaojie; Dione, Donald P.; Compas, Colin B.; Papademetris, Xenophon; Lin, Ben A.; Bregasi, Alda; Sinusas, Albert J.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets. PMID:24292554

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

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

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Climate Change Education in the U.S. Affiliated Pacific Islands

NASA Astrophysics Data System (ADS)

Sussman, A.; Fletcher, C. H.; Sachs, J. P.

2013-12-01

The Pacific Islands Climate Education Partnership (PCEP) serves the U.S. Affiliated Pacific Island (USAPI) Region. The international entities served by PCEP are the state of Hawai';i (USA); three Freely Associated States (the Federated States of Micronesia, the Republic of the Marshall Islands, and the Republic of Palau), and three Territories (Guam, Commonwealth of Northern Mariana Islands, and American Samoa). These Pacific Islands spread across 4.9 million square miles and include diverse indigenous cultures and languages. Many USAPI students live considerably below the poverty line. The Pacific Island region is projected to experience some of the most profound negative impacts considerably sooner than other regions. Funded by NSF, the PCEP aims to educate the region's students and citizens in ways that exemplify modern science and indigenous environmental knowledge, address the urgency of climate change impacts, and honor indigenous cultures. Students and citizens within the region will have the knowledge and skills to advance their and our understanding of climate change, and to adapt to its impacts. The PCEP Strategic Plan incorporates a range of interconnected strategic goals grouped into four priority education areas: Climate Education Framework --Implement a next-generation Climate Education Framework that focuses on the content and skills necessary for understanding the science of global and Pacific island climates, as well as the adaptation to climate impacts in the USAPI region. Indigenous Knowledge and Practices --Gather appropriate local indigenous knowledge based on the cultural stories and traditional practices related to environmental stewardship, climate, and local climate adaptation strategies. Learning and Teaching--Enhance conditions for learning about climate change in K-14 classrooms with the CEF through college-based, credentialed climate education programs; professional learning opportunities for teachers; and increased teacher implementation of locally-relevant climate science and adaptation curricula. Community-School Partnership --Connect schools (K-14) and community climate adaptation partners through locally relevant projects to implement effective and sustainable climate education. Explore and build awareness of resources that the community, colleges, and K-12 schools have to offer each other, and initiate partnering activities to support project-based learning activities. Current PCEP activities include: revising state and national science education standards to better incorporate climate change; contextualizing curricula to wide variety of climate and education contexts; gathering local indigenous knowledge and practices related to climate education and adaptation; providing professional development appropriate to these very diverse locations; supporting local professional learning communities in each international location; and developing a regional climate education certificate program. A key PCEP challenge is to maintain a coherent regional identity while contextualizing education activities to very diverse locations. PCEP staff have a high priority to learn, share and communicate across these locations, and to broadly benefit from lessons learned in each of the locations. Another strong connector is the overlap in climate changes, impacts and adaptation strategies across this international region.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

PubMed

Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

PubMed

Peng, Yong; Lu, Bao-Liang; Wang, Suhang

2015-05-01

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. Copyright © 2015 Elsevier Ltd. All rights reserved.

Smoothing of cost function leads to faster convergence of neural network learning

NASA Astrophysics Data System (ADS)

Xu, Li-Qun; Hall, Trevor J.

1994-03-01

One of the major problems in supervised learning of neural networks is the inevitable local minima inherent in the cost function f(W,D). This often makes classic gradient-descent-based learning algorithms that calculate the weight updates for each iteration according to (Delta) W(t) equals -(eta) (DOT)$DELwf(W,D) powerless. In this paper we describe a new strategy to solve this problem, which, adaptively, changes the learning rate and manipulates the gradient estimator simultaneously. The idea is to implicitly convert the local- minima-laden cost function f((DOT)) into a sequence of its smoothed versions {f(beta t)}Ttequals1, which, subject to the parameter (beta) t, bears less details at time t equals 1 and gradually more later on, the learning is actually performed on this sequence of functionals. The corresponding smoothed global minima obtained in this way, {Wt}Ttequals1, thus progressively approximate W-the desired global minimum. Experimental results on a nonconvex function minimization problem and a typical neural network learning task are given, analyses and discussions of some important issues are provided.

Interdisciplinary problem-based learning as a method to prepare Micronesia for public health emergencies.

PubMed

Yamada, Seiji; Durand, A Mark; Chen, Tai-Ho; Maskarinec, Gregory G

2007-03-01

The University of Hawai'i Pacific Basin Bioterrorism Curriculum Development Project has developed a problem-based learning (PBL) curriculum for teaching health professionals and health professional students about bioterrorism and other public health emergencies. These PBL cases have been incorporated into interdisciplinary training settings in community-based settings, such as in the small island districts of the U.S.-Affiliated Pacific Islands. Quantitative and qualitative methods have been utilized in the evaluation of the PBL cases, PBL tutorials, and the accomplishment of learning objectives. Evaluation of the PBL tutorials demonstrates that PBL is an educational and training modality appropriate for such settings. Participants found it helpful to learn in interdisciplinary groups. The educational process was modified in accordance with local culture. PBL is a useful educational modality for settings where healthcare staffing and available resources are limited.

A Telescopic Binary Learning Machine for Training Neural Networks.

PubMed

Brunato, Mauro; Battiti, Roberto

2017-03-01

This paper proposes a new algorithm based on multiscale stochastic local search with binary representation for training neural networks [binary learning machine (BLM)]. We study the effects of neighborhood evaluation strategies, the effect of the number of bits per weight and that of the maximum weight range used for mapping binary strings to real values. Following this preliminary investigation, we propose a telescopic multiscale version of local search, where the number of bits is increased in an adaptive manner, leading to a faster search and to local minima of better quality. An analysis related to adapting the number of bits in a dynamic way is presented. The control on the number of bits, which happens in a natural manner in the proposed method, is effective to increase the generalization performance. The learning dynamics are discussed and validated on a highly nonlinear artificial problem and on real-world tasks in many application domains; BLM is finally applied to a problem requiring either feedforward or recurrent architectures for feedback control.

Teen Advocates for Community and Environmental Sustainability

NASA Astrophysics Data System (ADS)

Wunar, B.

2017-12-01

The Museum of Science and Industry, Chicago (MSI) is in the early stages of a NOAA supported Environmental Literacy Grant project that aims to engage high school age youth in the exploration of climate and Earth systems science. Participating youth are positioned as teen advocates for establishing resilient communities in the Midwest. The project utilizes a variety of resources, including NOAA Science On a Sphere® (SOS) technology and datasets, Great Lakes and local climate assets, and local municipal resiliency planning guides to develop museum-based youth programming. Teen participants in the project will share their learning through regular facilitated interactions with public visitors in the Museum and will bring learning experiences to Chicago Public Library sites throughout the city's neighborhoods. Project content will also be adapted for use in 100+ after-school science clubs to engage younger students from diverse communities across the Chicago area. Current strategies for supporting teen facilitation of public experiences, linkages to out of school time and summer learning programs, and connections to local resiliency planning agencies will be explored.

Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model.

PubMed

Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming; Kakadiaris, Ioannis A

2011-10-01

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Data-Driven Learning of Total and Local Energies in Elemental Boron

NASA Astrophysics Data System (ADS)

Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor

2018-04-01

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β -rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

Data-Driven Learning of Total and Local Energies in Elemental Boron.

PubMed

Deringer, Volker L; Pickard, Chris J; Csányi, Gábor

2018-04-13

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

PubMed Central

Xu, Yang; Luo, Xiong; Wang, Weiping; Zhao, Wenbing

2017-01-01

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes. PMID:28085084

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

PubMed

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Memristive device based learning for navigation in robots.

PubMed

Sarim, Mohammad; Kumar, Manish; Jha, Rashmi; Minai, Ali A

2017-11-08

Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.

A case study analysing the process of analogy-based learning in a teaching unit about simple electric circuits

NASA Astrophysics Data System (ADS)

Paatz, Roland; Ryder, James; Schwedes, Hannelore; Scott, Philip

2004-09-01

The purpose of this case study is to analyse the learning processes of a 16-year-old student as she learns about simple electric circuits in response to an analogy-based teaching sequence. Analogical thinking processes are modelled by a sequence of four steps according to Gentner's structure mapping theory (activate base domain, postulate local matches, connect them to a global match, draw candidate inferences). We consider whether Gentner's theory can be used to account for the details of this specific teaching/learning context. The case study involved video-taping teaching and learning activities in a 10th-grade high school course in Germany. Teaching used water flow through pipes as an analogy for electrical circuits. Using Gentner's theory, relational nets were created from the student's statements at different stages of her learning. Overall, these nets reflect the four steps outlined earlier. We also consider to what extent the learning processes revealed by this case study are different from previous analyses of contexts in which no analogical knowledge is available.

Children's Literature as a Springboard to Place-Based Embodied Learning

ERIC Educational Resources Information Center

Wason-Ellam, Linda

2010-01-01

Globalization makes living in the world more complex. Many children live as social cyborgs attached to the digital spaces of the virtual play worlds of television, video and computer games rather than connected to their own local places. The impact of this change may well be that children lack acquaintance with their local places and may never…

Values of Local Wisdom: A Potential to Develop an Assessment and Remedial

ERIC Educational Resources Information Center

Toharudin, Uus; Kurniawan, Iwan Setia

2017-01-01

Development assessment and remedial needs to be done because it is an important part of a learning process. This study aimed to describe the ability of student teachers of biology in developing assessment and remedial based on local wisdom. using a quasi-experimental research methods with quantitative descriptive analysis techniques. The research…

Connecting Indigenous Stories with Geology: Inquiry-Based Learning in a Middle Years Classroom

ERIC Educational Resources Information Center

Larkin, Damian; King, Donna; Kidman, Gillian

2012-01-01

One way to integrate indigenous perspectives in junior science is through links between indigenous stories of the local area and science concepts. Using local indigenous stories about landforms, a teacher of Year 8 students designed a unit on geology that catered for the diverse student population in his class. This paper reports on the…

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

Successful Teaching, Learning, and Use of Digital Mapping Technology in Mazvihwa, Rural Zimbabwe

NASA Astrophysics Data System (ADS)

Eitzel Solera, M. V.; Madzoro, S.; Solera, J.; Mhike Hove, E.; Changarara, A.; Ndlovu, D.; Chirindira, A.; Ndlovu, A.; Gwatipedza, S.; Mhizha, M.; Ndlovu, M.

2016-12-01

Participatory mapping is now a staple of community-based work around the world. Particularly for indigenous and rural peoples, it can represent a new avenue for environmental justice and can be a tool for culturally appropriate management of local ecosystems. We present a successful example of teaching and learning digital mapping technology in rural Zimbabwe. Our digital mapping project is part of the long-term community-based participatory research of The Muonde Trust in Mazvihwa, Zimbabwe. By gathering and distributing local knowledge and also bringing in visitors to share knowledge, Muonde has been able to spread relevant information among rural farmers. The authors were all members of Muonde or were Muonde's visitors, and were mentors and learners of digital mapping technologies at different times. Key successful characteristics of participants included patience, compassion, openness, perseverance, respect, and humility. Important mentoring strategies included: 1) instruction in Shona and in English, 2) locally relevant examples, assignments, and analogies motivated by real needs, 3) using a variety of teaching methods for different learning modalities, 4) building on and modifying familiar teaching methods, and 5) paying attention to the social and relational aspects of teaching and learning. The Muonde mapping team has used their new skills for a wide variety of purposes, including: identifying, discussing, and acting on emerging needs; using digital mapping for land-use and agropastoral planning; and using mapping as a tool for recording and telling important historical and cultural stories. Digital mapping has built self-confidence as well as providing employable skills and giving Muonde more visibility to other local and national non-governmental organizations, utility companies, and educational institutions. Digital mapping, as taught in a bottom-up, collaborative way, has proven to be both accessible and of enormous practical use to rural Zimbabweans.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Teaching Mathematics Bilingually for Kindergarten Students with Teaching Aids Based on Local Wisdom

ERIC Educational Resources Information Center

Ambarini, Ririn; Setyaji, Arso; Suneki, Sri

2018-01-01

Language and Mathematics are both skills and knowledge that need to master well so that it can be the provision for students' future life when mingling with the community or society. Because of that the integration of teaching both language and Mathematics in bilingual Math learning will give many benefits to the students. They will learn not only…

Public Scholarship within an Urban School District: A Community and University Partnership Approach to Service-Learning

ERIC Educational Resources Information Center

Booker, Angela; Montgomery-Block, Kindra; Scott, Zenae; Reyes, bel; Onyewuenyi, Adaurennaya

2011-01-01

This article reports on a collaborative partnership, based in principles of public scholarship and designed to serve local, at-risk or high-risk youth. The program is a six-week summer service-learning initiative in the Sacramento, California, area developed for transitioning 9th grade students through a multi-agency partnership. The project…

The Rainforest Still Needs Us: The Forman School's 20 Years in the Mountains of Costa Rica

ERIC Educational Resources Information Center

Lawson, Leesa

2013-01-01

The search for solutions to protect the rainforest, while offering local farmers a sustainable means of making a living, started at The Forman School as a search to fully engage its students in learning. The Forman School is an independent college preparatory school for students with language-based learning differences (LD). This article discusses…

Cross-Cultural Interface Design and the Classroom-Learning Environment in Taiwan

ERIC Educational Resources Information Center

Chang, Chia-Lin; Su, Yelin

2012-01-01

This study examined whether using localized interface designs would make a difference in users' learning results and their perceptions of the interface design in a classroom learning environment. This study also sought to learn more about users' attitudes toward the localized interface features. To assess the impact of using localized interfaces…

Preparing public health nurses for pandemic influenza through distance learning.

PubMed

Macario, Everly; Benton, Lisa D; Yuen, Janet; Torres, Mara; Macias-Reynolds, Violet; Holsclaw, Patricia; Nakahara, Natalie; Jones, Marcy Connell

2007-01-01

As a global influenza pandemic appears imminent with the spread of avian influenza, the California Department of Health Services (CDHS) and the California Distance Learning Health Network (CDLHN) presented a live 90-min satellite broadcast and subsequent 2-hr small group problem-solving tabletop exercise to practice interventions needed to minimize the consequences of a pandemic event. Public health nurses (PHNs), managers, and other staff in laboratories, clinical care, veterinary medicine, environmental health, public information and safety, emergency management, and transportation down linked the program, broadcast by satellite from the CDHS Richmond Laboratory Campus, to view on-site locally. PHNs represented the professional category with the highest number of participants for those conducting the program outside of California. For those in California, PHNs represented the professional category with the second highest number of participants. Participants and distance-learning facilitators completed a training evaluation survey. Continuing education credits were provided by the Centers for Disease Control and Prevention to participants who completed the satellite broadcast evaluation. This distance-learning-by-satellite method of education paired with an activities-based tabletop exercise, and a focus on local rather than State-based responsibility, marks an innovative method of training PHNs and other staff in emergency preparedness response.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Evaluating the online platform of a blended-learning pharmacist continuing education degree program.

PubMed

Wilbur, Kerry

2016-01-01

Background Distance-based continuing education opportunities are increasingly embraced by health professionals worldwide. Methods To evaluate the online component of a blended-learning degree program for pharmacists, we conducted a structured self-assessment and peer review using an instrument systematically devised according to Moore's principles of transactional distance. The web-based platform for 14 courses was reviewed by both local and external faculty, followed by shared reflection of individual and aggregate results. Results Findings indicated a number of course elements for modification to enhance the structure, dialog, and autonomy of the student learning experience. Conclusion Our process was an important exercise in quality assurance and is worthwhile for other health disciplines developing and delivering distance-based content to pursue.

Evaluating the online platform of a blended-learning pharmacist continuing education degree program

PubMed Central

Wilbur, Kerry

2016-01-01

Background Distance-based continuing education opportunities are increasingly embraced by health professionals worldwide. Methods To evaluate the online component of a blended-learning degree program for pharmacists, we conducted a structured self-assessment and peer review using an instrument systematically devised according to Moore's principles of transactional distance. The web-based platform for 14 courses was reviewed by both local and external faculty, followed by shared reflection of individual and aggregate results. Results Findings indicated a number of course elements for modification to enhance the structure, dialog, and autonomy of the student learning experience. Conclusion Our process was an important exercise in quality assurance and is worthwhile for other health disciplines developing and delivering distance-based content to pursue. PMID:27282277

Learning-based controller for biotechnology processing, and method of using

DOEpatents

Johnson, John A.; Stoner, Daphne L.; Larsen, Eric D.; Miller, Karen S.; Tolle, Charles R.

2004-09-14

The present invention relates to process control where some of the controllable parameters are difficult or impossible to characterize. The present invention relates to process control in biotechnology of such systems, but not limited to. Additionally, the present invention relates to process control in biotechnology minerals processing. In the inventive method, an application of the present invention manipulates a minerals bioprocess to find local exterma (maxima or minima) for selected output variables/process goals by using a learning-based controller for bioprocess oxidation of minerals during hydrometallurgical processing. The learning-based controller operates with or without human supervision and works to find processor optima without previously defined optima due to the non-characterized nature of the process being manipulated.

Evaluating the online platform of a blended-learning pharmacist continuing education degree program.

PubMed

Wilbur, Kerry

2016-01-01

Distance-based continuing education opportunities are increasingly embraced by health professionals worldwide. To evaluate the online component of a blended-learning degree program for pharmacists, we conducted a structured self-assessment and peer review using an instrument systematically devised according to Moore's principles of transactional distance. The web-based platform for 14 courses was reviewed by both local and external faculty, followed by shared reflection of individual and aggregate results. Findings indicated a number of course elements for modification to enhance the structure, dialog, and autonomy of the student learning experience. Our process was an important exercise in quality assurance and is worthwhile for other health disciplines developing and delivering distance-based content to pursue.

Census of the Local Universe (CLU): Classification of Galaxy Candidates in Narrowband Images Using Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Chaoran; Van Sistine, Anglea; Kaplan, David; Brady, Patrick; Cook, David O.; Kasliwal, Mansi

2018-01-01

A complete catalog of galaxies in the local universe is critical for efficient electromagnetic follow-up of gravitational wave events (EMGW). The Census of the Local Universe (CLU; Cook et al. 2017, in preparation) aims to provide a galaxy catalog out to 200 Mpc that is as complete as possible. CLU has recently completed an Hα survey of ~3π of the sky with the goal of cataloging those galaxies that are likely hosts of EMGW events. Here, we present a tool we developed using machine learning technology to classify sources extracted from the Hα narrowband images within 200Mpc. In this analysis we find we are able to recover more galaxies compared to selections based on Hα colors alone.

Social reinforcement can regulate localized brain activity.

PubMed

Mathiak, Krystyna A; Koush, Yury; Dyck, Miriam; Gaber, Tilman J; Alawi, Eliza; Zepf, Florian D; Zvyagintsev, Mikhail; Mathiak, Klaus

2010-11-01

Social learning is essential for adaptive behavior in humans. Neurofeedback based on functional magnetic resonance imaging (fMRI) trains control over localized brain activity. It can disentangle learning processes at the neural level and thus investigate the mechanisms of operant conditioning with explicit social reinforcers. In a pilot study, a computer-generated face provided a positive feedback (smiling) when activity in the anterior cingulate cortex (ACC) increased and gradually returned to a neutral expression when the activity dropped. One female volunteer without previous experience in fMRI underwent training based on a social reinforcer. Directly before and after the neurofeedback runs, neural responses to a cognitive interference task (Simon task) were recorded. We observed a significant increase in activity within ACC during the neurofeedback blocks, correspondent with the a-priori defined anatomical region of interest. In the course of the neurofeedback training, the subject learned to regulate ACC activity and could maintain the control even without direct feedback. Moreover, ACC was activated significantly stronger during Simon task after the neurofeedback training when compared to before. Localized brain activity can be controlled by social reward. The increased ACC activity transferred to a cognitive task with the potential to reduce cognitive interference. Systematic studies are required to explore long-term effects on social behavior and clinical applications.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

Participatory monitoring to connect local and global priorities for forest restoration.

PubMed

Evans, Kristen; Guariguata, Manuel R; Brancalion, Pedro H S

2018-06-01

New global initiatives to restore forest landscapes present an unparalleled opportunity to reverse deforestation and forest degradation. Participatory monitoring could play a crucial role in providing accountability, generating local buy in, and catalyzing learning in monitoring systems that need scalability and adaptability to a range of local sites. We synthesized current knowledge from literature searches and interviews to provide lessons for the development of a scalable, multisite participatory monitoring system. Studies show that local people can collect accurate data on forest change, drivers of change, threats to reforestation, and biophysical and socioeconomic impacts that remote sensing cannot. They can do this at one-third the cost of professionals. Successful participatory monitoring systems collect information on a few simple indicators, respond to local priorities, provide appropriate incentives for participation, and catalyze learning and decision making based on frequent analyses and multilevel interactions with other stakeholders. Participatory monitoring could provide a framework for linking global, national, and local needs, aspirations, and capacities for forest restoration. © 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Telemedicine in Western Africa: lessons learned from a pilot project in Mali, perspectives and recommendations.

PubMed

Geissbuhler, Antoine; Ly, Ousmane; Lovis, Christian; L'Haire, Jean-François

2003-01-01

to evaluate the feasibility, potential and risks of an internet-based telemedicine network in developing countries of Western Africa. a project for the development of a national telemedicine network in Mali was initiated in 2001, using internet-based technologies for distance learning and teleconsultations. the telemedicine network has been in productive use for 12 months and has enabled various collaboration channels, including North-South, South-South, and South-North distance learning and teleconsultations. It also unveiled a set of potential problems: a) limited pertinence of North-South collaborations when there are major differences in available resources or socio-cultural contexts between the collaborating parties; b) risk of induced digital divide if the periphery of the health system is not involved in the development of the network, and c) need for the development of local medical contents management skills. the identified risks must be taken into account when designing large-scale telemedicine projects in developing countries and can be mitigated by the fostering of South-South collaboration channels, the use of satellite-based internet connectivity in remote areas, and the valorization of local knowledge and its publication on-line.

Age and gender classification in the wild with unsupervised feature learning

NASA Astrophysics Data System (ADS)

Wan, Lihong; Huo, Hong; Fang, Tao

2017-03-01

Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

"Bringing Learning Closer to Home": The Value and Impact of the Lisbon Strategy for Strengthening the Role of Local Learning Centres and Partnerships in South-Eastern Europe

ERIC Educational Resources Information Center

Zarifis, George K.

2008-01-01

This paper presents a comparative examination of four local learning centres that provide learning opportunities throughout life in Bulgaria, Cyprus, Greece and Turkey. The paper aims to assess some of the strengths and weaknesses of different types of local learning centres and partnerships in South-Eastern Europe--in line with the value and…

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Streaming Media Technology: Laying the Foundations for Educational Change.

ERIC Educational Resources Information Center

Sircar, Jayanta

2000-01-01

Discussion of the delivery of multimedia using streaming technology focuses on its use in engineering education. Highlights include engineering education and instructional technology, including learning approaches based on cognitive development; differences between local and distance education; economic factors; and roles of Web-based streaming,…

Rank preserving sparse learning for Kinect based scene classification.

PubMed

Tao, Dapeng; Jin, Lianwen; Yang, Zhao; Li, Xuelong

2013-10-01

With the rapid development of the RGB-D sensors and the promptly growing population of the low-cost Microsoft Kinect sensor, scene classification, which is a hard, yet important, problem in computer vision, has gained a resurgence of interest recently. That is because the depth of information provided by the Kinect sensor opens an effective and innovative way for scene classification. In this paper, we propose a new scheme for scene classification, which applies locality-constrained linear coding (LLC) to local SIFT features for representing the RGB-D samples and classifies scenes through the cooperation between a new rank preserving sparse learning (RPSL) based dimension reduction and a simple classification method. RPSL considers four aspects: 1) it preserves the rank order information of the within-class samples in a local patch; 2) it maximizes the margin between the between-class samples on the local patch; 3) the L1-norm penalty is introduced to obtain the parsimony property; and 4) it models the classification error minimization by utilizing the least-squares error minimization. Experiments are conducted on the NYU Depth V1 dataset and demonstrate the robustness and effectiveness of RPSL for scene classification.

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

PubMed

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Metric Learning for Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

Research-based recommendations for implementing international service-learning.

PubMed

Amerson, Roxanne

2014-01-01

An increasing number of schools of nursing are incorporating international service-learning and/or immersion experiences into their curriculum to promote cultural competence. The purpose of this paper is to identify research-based recommendations for implementing an international service-learning program. A review of literature was conducted in the Cumulative Index of Nursing and Allied Health Literature database using the keywords international, immersion, cultural competence, nursing, and international service-learning. Additional references were located from the reference lists of related articles. Planning of international or immersion experiences requires consideration of the type of country, the length of time, and design of the program; the use of a service-learning framework; opportunities that require the student to live and work in the community, provide hands-on care, participate in unstructured activities, and make home visits; and a method of reflection. Increasing cultural competence does not require foreign travel, but it does necessitate that students are challenged to move outside their comfort zone and work directly with diverse populations. These research-based recommendations may be used either internationally or locally to promote the most effective service-learning opportunities for nursing students. © 2014.

Reinforcement learning of periodical gaits in locomotion robots

NASA Astrophysics Data System (ADS)

Svinin, Mikhail; Yamada, Kazuyaki; Ushio, S.; Ueda, Kanji

1999-08-01

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance- based reinforcement learning scheme, is used for sensory- motor control of an eight-legged mobile robot. Important feature of the classifier system is its ability to work with the continuous sensor space. The robot does not have a prior knowledge of the environment, its own internal model, and the goal coordinates. It is only assumed that the robot can acquire stable gaits by learning how to reach a light source. During the learning process the control system, is self-organized by reinforcement signals. Reaching the light source defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. Feasibility of the proposed self-organized system is tested under simulation and experiment. The control actions are specified at the leg level. It is shown that, as learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns.

Local Instruction Theory (LIT) on spherical geometry for enhancement students’ strategic competence

NASA Astrophysics Data System (ADS)

Nuraida, I.; Kusumah, Y. S.; Kartasasmita, B. G.

2018-03-01

This research focused on the analysis of the materials spherical geometry of the wake in an attempt to enhancemet the strategic competence of students and to produce learning trajectory. That is because the materials that are used less catchy concept gives students. Learning materials with Local Instructional Theory (LIT) can enhancemet the strategic competence of the students. This research aims to study the difference of achievement and improving the strategic competence of the students who got the Realistics Mathematics Education (RME) and (LIT) with conventional learning. This research is the Design Research with two cycles. This research has three phases i.e. 1) preparing for the experiment/preliminary; 2) teaching eksperiment; 3) retrospective analysis. The population of the research was the whole IX group junior high school 1 Rajapolah with samples of IXg and IXj group. Results of the analysis of the data shows that students based on Mathematical Prior Knowledge (MPK) acquire learning achievement have RME and LIT and enhancement strategic competence of the mathematical that are higher than those of students who obtain the conventional learning.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

PubMed Central

Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

2014-01-01

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

Problem-based learning on quantitative analytical chemistry course

NASA Astrophysics Data System (ADS)

Fitri, Noor

2017-12-01

This research applies problem-based learning method on chemical quantitative analytical chemistry, so called as "Analytical Chemistry II" course, especially related to essential oil analysis. The learning outcomes of this course include aspects of understanding of lectures, the skills of applying course materials, and the ability to identify, formulate and solve chemical analysis problems. The role of study groups is quite important in improving students' learning ability and in completing independent tasks and group tasks. Thus, students are not only aware of the basic concepts of Analytical Chemistry II, but also able to understand and apply analytical concepts that have been studied to solve given analytical chemistry problems, and have the attitude and ability to work together to solve the problems. Based on the learning outcome, it can be concluded that the problem-based learning method in Analytical Chemistry II course has been proven to improve students' knowledge, skill, ability and attitude. Students are not only skilled at solving problems in analytical chemistry especially in essential oil analysis in accordance with local genius of Chemistry Department, Universitas Islam Indonesia, but also have skilled work with computer program and able to understand material and problem in English.

The Adolescent Mentalization-based Integrative Treatment (AMBIT) approach to outcome evaluation and manualization: adopting a learning organization approach.

PubMed

Fuggle, Peter; Bevington, Dickon; Cracknell, Liz; Hanley, James; Hare, Suzanne; Lincoln, John; Richardson, Garry; Stevens, Nina; Tovey, Heather; Zlotowitz, Sally

2015-07-01

AMBIT (Adolescent Mentalization-Based Integrative Treatment) is a developing team approach to working with hard-to-reach adolescents. The approach applies the principle of mentalization to relationships with clients, team relationships and working across agencies. It places a high priority on the need for locally developed evidence-based practice, and proposes that outcome evaluation needs to be explicitly linked with processes of team learning using a learning organization framework. A number of innovative methods of team learning are incorporated into the AMBIT approach, particularly a system of web-based wiki-formatted AMBIT manuals individualized for each participating team. The paper describes early development work of the model and illustrates ways of establishing explicit links between outcome evaluation, team learning and manualization by describing these methods as applied to two AMBIT-trained teams; one team working with young people on the edge of care (AMASS - the Adolescent Multi-Agency Support Service) and another working with substance use (CASUS - Child and Adolescent Substance Use Service in Cambridgeshire). Measurement of the primary outcomes for each team (which were generally very positive) facilitated team learning and adaptations of methods of practice that were consolidated through manualization. © The Author(s) 2014.

Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.

Self-directed e-learning at a tertiary hospital in Malawi--a qualitative evaluation and lessons learnt.

PubMed

Barteit, Sandra; Hoepffner, Philip; Huwendiek, Sören; Karamagi, Angela; Munthali, Charles; Theurer, Antje; Neuhann, Florian

2015-01-01

Malawi faces a severe lack of health workers. Despite initiatives to address this problem, a critical shortage of health care staff remains. This lack challenges the education and training of junior medical staff, especially medical interns in their final and crucial training year before they independently work as medical doctors. We have introduced an e-learning platform in the medical department of the Kamuzu Central Hospital (KCH) in Malawi. With the support of computer-assisted instruction, we aimed to improve the quality of medical training and education, as well as access to current medical materials, in particular for interns. From March to April 2012, we conducted a qualitative evaluation to assess relevance and appropriateness of the e-learning platform. Data was collected via face-to-face interviews, a guided group discussion and a checklist based observation log. Evaluation data was recorded and coded using content analysis, interviewees were chosen via purposive sampling. E-learning proved to be technically feasible in this setting. Users considered the e-learning platform to be relevant and appropriate. Concerns were raised about sustainability, accessibility and technical infrastructure, as well as limited involvement and responsibilities of Malawian partners. Interest in e-learning was high, yet, awareness of and knowledge about the e-learning platform among potential users was low. Evaluation results indicated that further adaptions to local needs are necessary to increase usage and accessibility. Interview results and our project experiences showed that, in the given setting, e-learning requires commitment from local stakeholders, adequate technical infrastructure, identification and assignation of responsibilities, as well as specific adaption to local needs.

Self-directed e-learning at a tertiary hospital in Malawi – A qualitative Evaluation and Lessons learnt

PubMed Central

Barteit, Sandra; Hoepffner, Philip; Huwendiek, Sören; Karamagi, Angela; Munthali, Charles; Theurer, Antje; Neuhann, Florian

2015-01-01

Background: Malawi faces a severe lack of health workers. Despite initiatives to address this problem, a critical shortage of health care staff remains. This lack challenges the education and training of junior medical staff, especially medical interns in their final and crucial training year before they independently work as medical doctors. Project description: We have introduced an e-learning platform in the medical department of the Kamuzu Central Hospital (KCH) in Malawi. With the support of computer-assisted instruction, we aimed to improve the quality of medical training and education, as well as access to current medical materials, in particular for interns. Method: From March to April 2012, we conducted a qualitative evaluation to assess relevance and appropriateness of the e-learning platform. Data was collected via face-to-face interviews, a guided group discussion and a checklist based observation log. Evaluation data was recorded and coded using content analysis, interviewees were chosen via purposive sampling. Results: E-learning proved to be technically feasible in this setting. Users considered the e-learning platform to be relevant and appropriate. Concerns were raised about sustainability, accessibility and technical infrastructure, as well as limited involvement and responsibilities of Malawian partners. Interest in e-learning was high, yet, awareness of and knowledge about the e-learning platform among potential users was low. Evaluation results indicated that further adaptions to local needs are necessary to increase usage and accessibility. Conclusions: Interview results and our project experiences showed that, in the given setting, e-learning requires commitment from local stakeholders, adequate technical infrastructure, identification and assignation of responsibilities, as well as specific adaption to local needs. PMID:25699110

Learning that Makes Sense in the Big Society

ERIC Educational Resources Information Center

Lamb, Penny

2010-01-01

Eric Pickles, Secretary of State for Communities and Local Government, has said his three priorities in office will be "localism, localism and localism". What does localism mean for local democracy and, with swingeing funding cuts in the offing, what can one do to ensure the relevance of adult learning to the localism agenda is…

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

NASA Astrophysics Data System (ADS)

Li, Jinsha; Liu, Sanyang; Li, Junmin

2017-10-01

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Community-based shared values as a 'Heart-ware' driver for integrated watershed management: Japan-Malaysia policy learning perspective

NASA Astrophysics Data System (ADS)

Mohamad, Zeeda Fatimah; Nasaruddin, Affan; Abd Kadir, Siti Norasiah; Musa, Mohd Noor; Ong, Benjamin; Sakai, Nobumitsu

2015-11-01

This paper explores the case for using ;community-based shared values; as a potential driver for the ;Heartware; aspects of governance in Integrated Watershed Management (IWM) - from a Japan-Malaysia policy learning perspective. This policy approach was originally inspired by the Japanese experience, and the paper investigates whether a similar strategy can be adapted in the Malaysian context-based on a qualitative exploratory case study of a local downstream watershed community. The community-based shared values are categorized into six functional values that can be placed on a watershed: industry, ecosystem, lifestyle, landscape, water resource and spirituality. The study confirmed the availability of a range of community-based shared values in each category that are promising to drive the heartware for integrated watershed management in the local Malaysian context. However, most of these shared values are either declining in its appreciation or nostalgic in nature. The paper ends with findings on key differences and similarities between the Malaysian and Japanese contexts, and concludes with lessons for international transfer of IWM heartware policy strategies between the two countries.

Place-Based Education in Geoscience: Theory, Research, Practice, and Assessment

ERIC Educational Resources Information Center

Semken, Steven; Ward, Emily Geraghty; Moosavi, Sadredin; Chinn, Pauline W. U.

2017-01-01

Place-based education (PBE) is a situated, context-rich, transdisciplinary teaching and learning modality distinguished by its unequivocal relationship to place, which is any locality that people have imbued with meanings and personal attachments through actual or vicarious experiences. As an observational and historical science, geoscience is…

Making the Tacit Explicit: Rethinking Culturally Inclusive Pedagogy in International Student Academic Adaptation

ERIC Educational Resources Information Center

Blasco, Maribel

2015-01-01

The article proposes an approach, broadly inspired by culturally inclusive pedagogy, to facilitate international student academic adaptation based on rendering tacit aspects of local learning cultures explicit to international full degree students, rather than adapting them. Preliminary findings are presented from a focus group-based exploratory…

The Effect of Professional Learning Activities on Implementation of California's Quality Professional Learning Standards in Alignment with the Local Control Funding Formula Priority 2

ERIC Educational Resources Information Center

Pinotti, Sadie

2017-01-01

The purpose of this Delphi study was to identify the professional learning activities that experts perceive are necessary for local education agencies (LEAs) to effectively implement California's Quality Professional Learning Standards (QPLS) in alignment with the Local Control Funding Formula (LCFF) Priority 2. The study also examined the degree…

The community comes to campus: the Patient and Community Fair.

PubMed

Towle, Angela; Godolphin, William; Kline, Cathy

2015-08-01

Community-based learning connects students with local communities so that they learn about the broad context in which health and social care is provided; however, students usually interact with only one or a few organisations that serve a particular population. One example of a community-based learning activity is the health fair in which students provide health promotion and screening for local communities. We adapted the health fair concept to develop a multi-professional educational event at which, instead of providing service, students learn from and about the expertise and resources of not-for-profit organisations. The fair is an annual 1-day event that students can attend between, or in place of, classes. Each community organisation has a booth to display information. One-hour 'patient panels' are held on a variety of topics throughout the day. Evaluation methods include questionnaires, exit interviews and visitor tracking sheets. Over 5 years (2009-2013), the fair increased in size with respect to estimated attendance, number of participating organisations, number of patient panels and number of students for whom the fair is a required curriculum component. Students learn about a range of patient experiences and community resources, and information about specific diseases or conditions. The fair is an efficient way for students to learn about a range of community organisations. It fosters university-community engagement through continuing connections between students, faculty members and community organisations. Lessons learned include the need for community organisations to have techniques to engage students, and ways to overcome challenges of evaluating an informal 'drop-in' event. The fair is an efficient way for students to learn about a range of community organisations. © 2015 John Wiley & Sons Ltd.

How collaborative governance can facilitate quality learning for sustainability in cities: A comparative case study of Bristol, Kitakyushu and Tongyeong

NASA Astrophysics Data System (ADS)

Ofei-Manu, Paul; Didham, Robert J.; Byun, Won Jung; Phillips, Rebecca; Dickella Gamaralalage, Premakumara Jagath; Rees, Sian

2017-09-01

Quality learning for sustainability can have a transformative effect in terms of promoting empowerment, leadership and wise investments in individual and collective lives and regenerating the local economies of cities, making them more inclusive, safe, resilient and sustainable. It can also help cities move towards achieving the United Nations Sustainable Development Goals (SDGs). Effecting the transformation of cities into Learning Cities, however, requires changes in the structure of governance. Drawing on interviews with key informants as well as secondary data, this article examines how collaborative governance has facilitated quality learning for sustainability in Bristol (United Kingdom), Kitakyushu (Japan) and Tongyeong (Republic of Korea). Focusing on a conceptual framework and practical application of learning initiatives, this comparative study reveals how these cities' governance mechanisms and institutional structures supported initiatives premised on cooperative learning relationships. While recognising differences in the scope and depth of the learning initiatives and the need for further improvements, the authors found evidence of general support for the governance structures and mechanisms for learning in these cities. The authors conclude by recommending that (1) to implement the Learning Cities concept based on UNESCO's Key Features of Learning Cities, recognition should be given to existing sustainability-related learning initiatives in cities; (2) collaborative governance of the Learning Cities concept at both local and international levels should be streamlined; and (3) UNESCO's Global Network of Learning Cities could serve as a hub for sharing education/learning resources and experiences for other international city-related programmes as an important contribution to the implementation of the SDGs.

Compressed multi-block local binary pattern for object tracking

NASA Astrophysics Data System (ADS)

Li, Tianwen; Gao, Yun; Zhao, Lei; Zhou, Hao

2018-04-01

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

Bringing authentic service learning to the classroom: benefits and lessons learned

NASA Astrophysics Data System (ADS)

Chamberlain, Leslie C.

2016-06-01

Project-based learning, which has gained significant attention within K-12 education, provides rich hands-on experiences for students. Bringing an element of service to the projects allow students to engage in a local or global community, providing an abundance of benefits to the students’ learning. For example, service projects build confidence, increase motivation, and exercise problem-solving and communication skills in addition to developing a deep understanding of content. I will present lessons I have learned through four years of providing service learning opportunities in my classroom. I share ideas for astronomy projects, tips for connecting and listening to a community, and helpful guidelines to hold students accountable in order to ensure a productive and educational project.

Developing a Deeper Understanding of Community-Based Pedagogies with Teachers: Learning with and from Teachers in Colombia

ERIC Educational Resources Information Center

Sharkey, Judy; Clavijo Olarte, Amparo; Ramírez, Luz Maribel

2016-01-01

Here we share findings from a 9-month qualitative case study involving a school-university professional development inquiry into how teachers develop, implement, and interpret community-based pedagogies (CBPs), an asset-based approach to curriculum that acknowledges mandated standards but begins with recognizing and valuing local knowledge. After…

Guided Inquiry Learning With Sea Water Battery Project

NASA Astrophysics Data System (ADS)

Mashudi, A.

2017-02-01

Science learning process is expected to produce valuable product, innovative and real learning environment, and provide memorable learning experience. That orientation can be contained in Inquiry Based Learning. SMP N 4 Juwana is located close to the beach. That’s why, Sea Water Battery Project is very suitable to be applied in learning activity as an effort to fulfill the renewable energy based on local wisdom. This study aims to increase interest, activity and achievement of students. Learning implementation stage, namely : Constructing Sea Water Battery project, observation, group presentations, and feedback. Sea Water Battery is renewable energy battery from materials easily found around the learner. The materials used are copper plate as the anode, zinc plate as the cathode and sea water as the electrolyte. Average score of students Interest on the first cycle 76, while on the second cycle 85. Average score of students Activity on the first cycle 76 and on the second cycle 86. Average score of students achievement on the first cycle 75, while on the second cycle 84. This learning process gave nurturant effect for students to keep innovating and construct engineering technology for the future.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

The MCH navigator: tools for MCH workforce development and lifelong learning.

PubMed

Grason, Holly; Huebner, Colleen; Crawford, Alyssa Kim; Ruderman, Marjory; Taylor, Cathy R; Kavanagh, Laura; Farel, Anita; Wightkin, Joan; Long-White, Deneen; Ramirez, Shokufeh M; Preskitt, Julie; Morrissette, Meredith; Handler, Arden

2015-02-01

Maternal and child health (MCH) leadership requires an understanding of MCH populations and systems as well as continuous pursuit of new knowledge and skills. This paper describes the development, structure, and implementation of the MCH Navigator, a web-based portal for ongoing education and training for a diverse MCH workforce. Early development of the portal focused on organizing high quality, free, web-based learning opportunities that support established learning competencies without duplicating existing resources. An academic-practice workgroup developed a conceptual model based on the MCH Leadership Competencies, the Core Competencies for Public Health Professionals, and a structured review of MCH job responsibilities. The workgroup used a multi-step process to cull the hundreds of relevant, but widely scattered, trainings and select those most valuable for the primary target audiences of state and local MCH professionals and programs. The MCH Navigator now features 248 learning opportunities, with additional tools to support their use. Formative assessment findings indicate that the portal is widely used and valued by its primary audiences, and promotes both an individual's professional development and an organizational culture of continuous learning. Professionals in practice and academic settings are using the MCH Navigator for orientation of new staff and advisors, "just in time" training for specific job functions, creating individualized professional development plans, and supplementing course content. To achieve its intended impact and ensure the timeliness and quality of the Navigator's content and functions, the MCH Navigator will need to be sustained through ongoing partnership with state and local MCH professionals and the MCH academic community.

The Development of Cooperative Learning Model Based on Local Wisdom of Bali for Physical Education, Sport and Health Subject in Junior High School

NASA Astrophysics Data System (ADS)

Yoda, I. K.

2017-03-01

The purpose of this research is to develop a cooperative learning model based on local wisdom (PKBKL) of Bali (Tri Pramana’s concept), for physical education, sport, and health learning in VII grade of Junior High School in Singaraja-Buleleng Bali. This research is the development research of the development design chosen refers to the development proposed by Dick and Carey. The development of model and learning devices was conducted through four stages, namely: (1) identification and needs analysis stage (2) the development of design and draft of PKBKL and RPP models, (3) testing stage (expert review, try out, and implementation). Small group try out was conducted on VII-3 grade of Undiksha Laboratory Junior High School in the academic year 2013/2014, large group try out was conducted on VIIb of Santo Paulus Junior High School Singaraja in the academic year 2014/2015, and the implementation of the model was conducted on three (3) schools namely SMPN 2 Singaraja, SMPN 3 Singaraja, and Undiksha laboratory Junior High School in the academic year 2014/2015. Data were collected using documentation, testing, non-testing, questionnaire, and observation. The data were analyzed descriptively. The findings of this research indicate that: (1) PKBKL model has met the criteria of the operation of a learning model namely: syntax, social system, principles of reaction, support system, as well as instructional and nurturing effects, (2) PKBKL model is a valid, practical, and effective model, (3) the practicality of the learning devices (RPP), is at the high category. Based on the research results, there are two things recommended: (1) in order that learning stages (syntax) of PKBKL model can be performed well, then teachers need to have an understanding of the cooperative learning model of Student Team Achievement Division (STAD) type and the concepts of scientifically approach well, (2) PKBKL model can be performed well on physical education, sport and health learning, if the teachers understand the concept of Tri Pramana, therefore if the physical education, sport and health teachers want to apply this PKBKL model, they must first learn and master the concept of Tri Pramana well.

Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition.

PubMed

Lu, Jiwen; Erin Liong, Venice; Zhou, Jie

2017-08-09

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which usually require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which automatically learns face representation from raw pixels. Unlike existing binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact binary face descriptor (CBFD) which use a two-stage feature extraction procedure, our SLBFLE jointly learns binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local binary feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogeneous face matching. Unlike most existing coupled feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets are presented to demonstrate the effectiveness of the proposed method.

Testing the limits of long-distance learning: Learning beyond a three-segment window

PubMed Central

Finley, Sara

2012-01-01

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones since long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ('sh') and triggered a suffix that was either [−su] or [−∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. PMID:22303815

Teacher Education as or for Social and Ecological Transformation: Place-Based Reflections on Local and Global Participatory Methods and Collaborative Practices

ERIC Educational Resources Information Center

Alsop, Steve; Dippo, Don; Zandvliet, David B.

2007-01-01

This paper offers reflections on two transformative teacher education projects. The first a global communities module is set in a university in Vancouver and utilizes the lens of social ecology to examine the roles of teachers in bringing an awareness of local/global issues to their students' learning experiences. The second, a Canadian…

Developing midwifery practice through work-based learning: an exploratory study.

PubMed

Marshall, Jayne E

2012-09-01

To explore what effect the introduction of a Work-Based Learning Module undertaken by midwives in a range of maternity settings has had on their personal professional development, as well as the impact on developing local maternity and neonatal care provision. A case study approach was used consisting of mixed methods. Quantitative data were collected through questionnaires from midwives and their Clinical Supervisors at the end of the module, with a survey questionnaire to each midwifery manager, six months following the implementation of the midwives' project in practice. Qualitative data were collected by focus groups at six different work place locations, with health professionals who had experienced the midwives' projects within the workplace. Quantitative data were manually analysed whereas content analysis was used to identify recurrent themes from the qualitative data, with the support of Computer Assisted Qualitative Data Analysis Software. The University of Nottingham granted ethical approval for the study. Twelve midwives who undertook the work-based module, their respective Clinical Supervisors (n = 12), their employers/managers (n = 12) and health professionals (n = 28) within six individual National Health Service Trusts in the East Midlands of the United Kingdom took part in the study. The work-based learning module not only led to the personal and professional development of the midwife, but also to improving multi-professional collaboration and the consequential development of maternity services within the local Trusts. The value of leading change by completing an innovative and tangible work-based project through a flexible mode of study strengthened the midwives' clinical credibility among colleagues and employers and supports the philosophy of inter-professional learning and working. This novel Work Based approach to learning has the potential to further develop the provision of post-registration education for midwives and other health professionals, as it helps to bridge the theory-practice gap. Learning in the workplace is efficient and cost effective to employee and employer and serves in increasing the link between higher education and the workplace. Furthermore, as the principles of work-based learning could be transferred to other contexts outside of the United Kingdom, such an approach has the potential to directly influence the development of global midwifery education and maternity services and ultimately benefit mothers, their babies and families throughout the world. Copyright © 2012 Elsevier Ltd. All rights reserved.

Diagnosing and treating Enquiry Based Learning fatigue in Graduate Entry Nursing students.

PubMed

Stacey, Gemma; Wilson, Claire; Reddy, Helen; Palmer, Chris; Henderson, James; Little, Hannah; Bull, Heather

2018-01-01

The use of student directed study approaches such as Enquiry Based Learning (EBL) in the design and implementation of Graduate Entry Nursing Circular is well established. The rational relates to the maximisation of graduate attributes such as motivation to learn, the ability to identify, search and assimilate relevant literature and the desire to take ownership of the direction and pace of learning. Existing alongside this however, is the observation that students remain under confident in the application of knowledge to a clinical context and frustrated with learning approaches which do not appear directly related to improving their competence in this area. We suggest the result of this is a gradual disengagement and dissatisfaction the learning forum amongst students and faculty, which we have defined as EBL fatigue. The symptoms and consequences of EBL fatigue amongst students and faculty are discussed alongside strategies which we suggest may act as preventative measures in reducing the risk of a local epidemic. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Drawing from Freirian empowerment methods to develop and use innovative learning maps: increasing enrollment of uninsured children on Detroit's eastside.

PubMed

Lopez, Ellen D S; Lichtenstein, Richard; Lewis, Alonzo; Banaszak-Holl, Jane; Lewis, Cheryl; Johnson, Penni; Riley, Scherry; Baum, Nancy M

2007-04-01

In 2001, virtually every child on Detroit's eastside was eligible for health coverage, yet approximately 3,000 children remained uninsured. The primary aim of the Eastside Access Partnership (EAP), a community-based participatory research collaboration, was to increase enrollment of uninsured children in state programs. To achieve this aim, one of the approaches that EAP is using is the innovative Learning Map titled Choosing the Healthy Path, which was developed in collaboration with Root Learning, Inc. Although Learning Maps were originally developed to assist corporations in implementing strategic change, their integration of visualization and interactive dialogue incorporates Freirian principles of empowerment education, making them a viable option for providing meaningful learning opportunities for community residents. This article presents the collaborative process involving the University of Michigan, local community-based organizations, community members, and Root Learning consultants to develop a visual map that enables community residents to understand and overcome the barriers that prevent them from obtaining health insurance for their children.

Epidermis area detection for immunofluorescence microscopy

NASA Astrophysics Data System (ADS)

Dovganich, Andrey; Krylov, Andrey; Nasonov, Andrey; Makhneva, Natalia

2018-04-01

We propose a novel image segmentation method for immunofluorescence microscopy images of skin tissue for the diagnosis of various skin diseases. The segmentation is based on machine learning algorithms. The feature vector is filled by three groups of features: statistical features, Laws' texture energy measures and local binary patterns. The images are preprocessed for better learning. Different machine learning algorithms have been used and the best results have been obtained with random forest algorithm. We use the proposed method to detect the epidermis region as a part of pemphigus diagnosis system.

Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models.

PubMed

Liu, Zhiguang; Zhou, Liuyang; Leung, Howard; Shum, Hubert P H

2016-11-01

Depth sensor based 3D human motion estimation hardware such as Kinect has made interactive applications more popular recently. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy data derived from depth images and self-occluding action performed by the user. In this paper, we propose a new real-time probabilistic framework to enhance the accuracy of live captured postures that belong to one of the action classes in the database. We adopt the Gaussian Process model as a prior to leverage the position data obtained from Kinect and marker-based motion capture system. We also incorporate a temporal consistency term into the optimization framework to constrain the velocity variations between successive frames. To ensure that the reconstructed posture resembles the accurate parts of the observed posture, we embed a set of joint reliability measurements into the optimization framework. A major drawback of Gaussian Process is its cubic learning complexity when dealing with a large database due to the inverse of a covariance matrix. To solve the problem, we propose a new method based on a local mixture of Gaussian Processes, in which Gaussian Processes are defined in local regions of the state space. Due to the significantly decreased sample size in each local Gaussian Process, the learning time is greatly reduced. At the same time, the prediction speed is enhanced as the weighted mean prediction for a given sample is determined by the nearby local models only. Our system also allows incrementally updating a specific local Gaussian Process in real time, which enhances the likelihood of adapting to run-time postures that are different from those in the database. Experimental results demonstrate that our system can generate high quality postures even under severe self-occlusion situations, which is beneficial for real-time applications such as motion-based gaming and sport training.

Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Jie

2015-12-01

In this paper, we propose a cost-sensitive local binary feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for feature representation, our CS-LBFL method learns discriminative local features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local binary features are more robust to illumination and expression variations than holistic features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional binary codes, where binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local binary codes within each face image as a real-valued histogram feature for face representation. Moreover, we propose a cost-sensitive local binary multi-feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets.

Formative assessment as a vehicle for changing classroom practice in a specific cultural context

NASA Astrophysics Data System (ADS)

Chen, Jingping

2015-09-01

In this commentary, I interpret Xinying Yin and Gayle Ann Buck's collaborative action research from a social-cultural perspective. Classroom implementation of formative assessment is viewed as interaction between this assessment method and the local learning culture. I first identify Yin and Buck's definition of the formative assessment, and then analyze the role of formative assessment in the change of local learning culture. Based on the practice of Yin and Buck I emphasize the significance of their "bottom up" strategy to the teachers' epistemological change. I believe that this strategy may provide practicable solutions to current Chinese educational problems as well as a means for science educators to shift toward systematic professional development.

Using Online Dialogues to Connect Local Leaders and Climate Experts: Methods, Feedback and Lessons Learned from the Resilience Dialogues

NASA Astrophysics Data System (ADS)

Goodwin, M.; Pandya, R.; Weaver, C. P.; Zerbonne, S.; Bennett, N.; Spangler, B.

2017-12-01

Inclusive, multi-stakeholder dialogue, participatory planning and actionable science are necessary for just and effective climate resilience outcomes. How can we support that in practice? The Resilience Dialogues launched a public Beta in 2016-2017 to allow scientists and resilience practitioners to engage with local leaders from 10 communities around the US through a series of facilitated, online dialogues. We developed two, one-week dialogues for each community: one to consider ways to respond to observed and anticipated climate impacts through a resilience lens, and one to identify next steps and resources to advance key priorities. We divided the communities into three cohorts and refined the structure and facilitation strategy for these dialogues from one to the next based on participant feedback. This adaptive method helped participants engage in the dialogues more effectively and develop useful results. We distributed a survey to all participants following each cohort to capture feedback on the use and utility of the dialogues. While there was room for improvement in the program's technical interface, survey participants valued the dialogues and the opportunity to engage as equals. Local leaders said the dialogues helped identify new local pathways to approach resilience priorities. They felt they benefited from focused conversation and personalized introductions to best-matched resources. Practitioners learned how local leaders seek to apply climate science, and how to effectively communicate their expertise to community leaders in support of local planning efforts. We learned there is demand for specialized dialogues on issues like communication, financing and extreme weather. Overall, the desire of participants to continue to engage through this program, and others to enter, indicates that facilitated, open conversations between experts and local leaders can break down communication and access barriers between climate services providers and end-users. This presentation will share lessons learned from this process and methods that we found most effective.

Improving access to screening for people with learning disabilities.

PubMed

Marriott, Anna; Turner, Sue; Giraud-Saunders, Alison

2014-11-04

People with learning disabilities have poorer health than their non-disabled peers, and are less likely to access screening services than the general population. The National Development Team for Inclusion and the Norah Fry Research Centre developed a toolkit and guidance to improve uptake of five national (English) screening programmes (one of which is delivered through local programmes), based on work to improve access by people with learning disabilities in the south west peninsula of the UK. This article describes the findings in relation to the five English screening programmes and suggests ways to improve uptake of cancer screening by people with learning disabilities.

Moral vindications.

PubMed

Kumar, Victor

2017-10-01

Psychologists and neuroscientists have recently been unearthing the unconscious processes that give rise to moral intuitions and emotions. According to skeptics like Joshua Greene, what has been found casts doubt on many of our moral beliefs. However, a new approach in moral psychology develops a learning-theoretic framework that has been successfully applied in a number of other domains. This framework suggests that model-based learning shapes intuitions and emotions. Model-based learning explains how moral thought and feeling are attuned to local material and social conditions. Philosophers can draw on these explanations, in some cases, in order to vindicate episodes of moral change. Explanations can support justifications by showing that they are not mere rationalizations. In addition, philosophical justifications are a fertile source for empirical hypotheses about the rational learning mechanisms that shape moral intuitions and emotions. Copyright © 2017 Elsevier B.V. All rights reserved.

Protein subcellular localization prediction using multiple kernel learning based support vector machine.

PubMed

Hasan, Md Al Mehedi; Ahmad, Shamim; Molla, Md Khademul Islam

2017-03-28

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization prediction by purely laboratory tests prohibitively laborious and expensive. In this context, to tackle the challenges, computational methods are being developed as an alternative choice to aid biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging issue, particularly, when query proteins have multi-label characteristics, i.e., if they exist simultaneously in more than one subcellular location or if they move between two or more different subcellular locations. To date, to address this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions to the protein subcellular localization prediction problem. However, the practicability of an SVM is affected by the challenges of selecting an appropriate kernel and selecting the parameters of the selected kernel. To address this difficulty, in this study, we aimed to develop an efficient multi-label protein subcellular localization prediction system, named as MKLoc, by introducing multiple kernel learning (MKL) based SVM. We evaluated MKLoc using a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). Note that this dataset was used by Briesemeister et al. in their extensive comparison of multi-localization prediction systems. Finally, our experimental results indicate that MKLoc not only achieves higher accuracy than a single kernel based SVM system but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+). Moreover, MKLoc requires less computation time to tune and train the system than that required for BNCs and single kernel based SVM.

Lessons Learned: Cultural and linguistic enhancement of surveys through community-based participatory research

PubMed Central

Formea, Christine M.; Mohamed, Ahmed A.; Hassan, Abdullahi; Osman, Ahmed; Weis, Jennifer A.; Sia, Irene G.; Wieland, Mark L.

2014-01-01

Background Surveys are frequently implemented in community-based participatory research (CBPR), but adaptation and translation of surveys can be logistically and methodologically challenging when working with immigrant and refugee populations. Objective To describe a process of participatory survey adaptation and translation. Methods Within an established CBPR partnership, a survey about diabetes was adapted for health literacy and local relevance and then translated through a process of forward translation, group deliberation, and back translation. Lessons Learned The group deliberation process was the most time-intensive and important component of the process. The process enhanced community ownership of the larger project while maximizing local applicability of the product. Conclusions A participatory process of survey adaptation and translation resulted in significant revisions to approximate semantic, cultural, and conceptual equivalence with the original surveys. This approach is likely to enhance community acceptance of the survey instrument during the implementation phase. PMID:25435559

[Medical computer-aided detection method based on deep learning].

PubMed

Tao, Pan; Fu, Zhongliang; Zhu, Kai; Wang, Lili

2018-03-01

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.

Reinforcement of Science Learning through Local Culture: A Delphi Study

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2008-01-01

This study aims to explore the ways to reinforce science learning through local culture by using Delphi technique. Twenty four participants in various fields of study were selected. The result of study provides a framework for reinforcement of science learning through local culture on the theme life and environment. (Contains 1 table.)

Learning from Software Localization.

ERIC Educational Resources Information Center

Guo, She-Sen

2003-01-01

Localization is the process of adapting a product to meet the language, cultural and other requirements of a specific target environment or market. This article describes ways in which software localization impacts upon curriculum, and discusses what students will learn from software localization. (AEF)

Dig into Learning: A Program Evaluation of an Agricultural Literacy Innovation

ERIC Educational Resources Information Center

Edwards, Erica Brown

2016-01-01

This study is a mixed-methods program evaluation of an agricultural literacy innovation in a local school district in rural eastern North Carolina. This evaluation describes the use of a theory-based framework, the Concerns-Based Adoption Model (CBAM), in accordance with Stufflebeam's Context, Input, Process, Product (CIPP) model by evaluating the…

The Case of Lobster Shell Disease

ERIC Educational Resources Information Center

Hollen, Shawna; Toney, Jaime L.; Bisaccio, Daniel; Haberstroh, Karen Marie; Herbert, Timothy

2011-01-01

The authors combined content-driven and inquiry-based lessons into the framework of problem-based learning (PBL). They did this in eight third- through sixth-grade classrooms--two each from grades 3-5, one from sixth grade, and one mixed-grade special education. These older elementary students explored a local problem of lobsters infected by…

The "I-Thou" Relationship, Place-Based Education, and Aldo Leopold

ERIC Educational Resources Information Center

Knapp, Clifford E.

2005-01-01

This article describes a new educational field labeled "place-based education" and relates it to experiential learning. This term has appeared in the educational literature over the last 10 years and illustrates a concern for providing participants with quality experiences in local settings. After defining and describing the term, one…

DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2015-09-30

Clark (2014), "Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia...34Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia Computer Science 20

An off-lattice, self-learning kinetic Monte Carlo method using local environments.

PubMed

Konwar, Dhrubajit; Bhute, Vijesh J; Chatterjee, Abhijit

2011-11-07

We present a method called local environment kinetic Monte Carlo (LE-KMC) method for efficiently performing off-lattice, self-learning kinetic Monte Carlo (KMC) simulations of activated processes in material systems. Like other off-lattice KMC schemes, new atomic processes can be found on-the-fly in LE-KMC. However, a unique feature of LE-KMC is that as long as the assumption that all processes and rates depend only on the local environment is satisfied, LE-KMC provides a general algorithm for (i) unambiguously describing a process in terms of its local atomic environments, (ii) storing new processes and environments in a catalog for later use with standard KMC, and (iii) updating the system based on the local information once a process has been selected for a KMC move. Search, classification, storage and retrieval steps needed while employing local environments and processes in the LE-KMC method are discussed. The advantages and computational cost of LE-KMC are discussed. We assess the performance of the LE-KMC algorithm by considering test systems involving diffusion in a submonolayer Ag and Ag-Cu alloy films on Ag(001) surface.

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

New estimation architecture for multisensor data fusion

NASA Astrophysics Data System (ADS)

Covino, Joseph M.; Griffiths, Barry E.

1991-07-01

This paper describes a novel method of hierarchical asynchronous distributed filtering called the Net Information Approach (NIA). The NIA is a Kalman-filter-based estimation scheme for spatially distributed sensors which must retain their local optimality yet require a nearly optimal global estimate. The key idea of the NIA is that each local sensor-dedicated filter tells the global filter 'what I've learned since the last local-to-global transmission,' whereas in other estimation architectures the local-to-global transmission consists of 'what I think now.' An algorithm based on this idea has been demonstrated on a small-scale target-tracking problem with many encouraging results. Feasibility of this approach was demonstrated by comparing NIA performance to an optimal centralized Kalman filter (lower bound) via Monte Carlo simulations.

The Opposite of Denial: Social Learning at the Onset of the Ebola Emergency in Liberia.

PubMed

Abramowitz, Sharon; McKune, Sarah Lindley; Fallah, Mosoka; Monger, Josephine; Tehoungue, Kodjo; Omidian, Patricia A

2017-01-01

This study analyzes findings from a rapid-response community-based qualitative research initiative to study the content of Ebola-related communications and the transmission of Ebola-related behaviors and practices through mass media communications and social learning in Monrovia, Liberia during August-September 2014. Thirteen neighborhoods in the common Monrovia media market were studied to appraise the reach of health communications and outreach regarding Ebola prevention and response measures. A World Health Organization (WHO) research team collected data on social learning and Ebola knowledge, attitudes, and practices through focus group-based discussions and key informant interviews over a 14-day period to assess the spread of information during a period of rapidly escalating crisis. Findings show that during a 2-week period, Monrovia neighborhood residents demonstrated rapid changes in beliefs about the source of Ebola, modes of contagion, and infection prevention and control (IPC) practices, discarding incorrect information. Changes in practices tended to lag behind the acquisition of learning. Findings also show that many continued to support conspiracy theories even as correct information was acquired. The implications for community engagement are substantial: (1) Under conditions of accelerating mortality, communities rapidly assimilate health information and abandon incorrect information; (2) Behavior change is likely to lag behind changes in beliefs due to local physical, structural, sociocultural, and institutional constraints; (3) Reports of "resistance" in Monrovia during the Ebola response were overstated and based on a limited number of incidents, and failed to account for specific local conditions and constraints.

Building a strong geoscience department by emphasizing curriculum and pedagogy

NASA Astrophysics Data System (ADS)

Lea, P. D.; Beane, R. J.; Laine, E. P.

2005-12-01

About a decade ago the Bowdoin College Geology Department recognized a need for a new curriculum that more fully engaged majors and non-majors as active learners. To accomplish this curricular change the faculty have adopted differing pedagogies that all engage students in real projects. Research project-based learning, community-based learning, and problem-based service-learning form the core of our teaching efforts. The emphasis on problem-solving and inquiry in our courses has greatly strengthened our department's contributions to research, education, and service at the college. These courses have an added benefit of acquainting students with various aspects of their local and global environment. Geology majors leave Bowdoin equipped with tools and experiences they need for employment or graduate school as well life-long learners. To support the integration of research into our teaching we have successfully sought funding from NSF's CCLI and MRI programs. As a consequence, even first year students work with an SEM/EDAX/EBSD, with instrumented watersheds, and soon with an ocean observatory adjacent to our Coastal Studies Center, as well as taking greater advantage of local field opportunities. Our intense focus on improving curriculum and pedagogy organized and energized us within the department and helped us to present ourselves and our goals to the college.

Locality preserving non-negative basis learning with graph embedding.

PubMed

Ghanbari, Yasser; Herrington, John; Gur, Ruben C; Schultz, Robert T; Verma, Ragini

2013-01-01

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.

Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification.

PubMed

Mei, Jiangyuan; Liu, Meizhu; Wang, Yuan-Fang; Gao, Huijun

2016-06-01

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.

Effects of conformism on the cultural evolution of social behaviour.

PubMed

Molleman, Lucas; Pen, Ido; Weissing, Franz J

2013-01-01

Models of cultural evolution study how the distribution of cultural traits changes over time. The dynamics of cultural evolution strongly depends on the way these traits are transmitted between individuals by social learning. Two prominent forms of social learning are payoff-based learning (imitating others that have higher payoffs) and conformist learning (imitating locally common behaviours). How payoff-based and conformist learning affect the cultural evolution of cooperation is currently a matter of lively debate, but few studies systematically analyse the interplay of these forms of social learning. Here we perform such a study by investigating how the interaction of payoff-based and conformist learning affects the outcome of cultural evolution in three social contexts. First, we develop a simple argument that provides insights into how the outcome of cultural evolution will change when more and more conformist learning is added to payoff-based learning. In a social dilemma (e.g. a Prisoner's Dilemma), conformism can turn cooperation into a stable equilibrium; in an evasion game (e.g. a Hawk-Dove game or a Snowdrift game) conformism tends to destabilize the polymorphic equilibrium; and in a coordination game (e.g. a Stag Hunt game), conformism changes the basin of attraction of the two equilibria. Second, we analyse a stochastic event-based model, revealing that conformism increases the speed of cultural evolution towards pure equilibria. Individual-based simulations as well as the analysis of the diffusion approximation of the stochastic model by and large confirm our findings. Third, we investigate the effect of an increasing degree of conformism on cultural group selection in a group-structured population. We conclude that, in contrast to statements in the literature, conformism hinders rather than promotes the evolution of cooperation.

Distance Learning as a Tool for Poverty Reduction and Economic Development: A Focus on China and Mexico

NASA Astrophysics Data System (ADS)

Larson, Richard C.; Murray, M. Elizabeth

2008-04-01

This paper uses case studies to focus on distance learning in developing countries as an enabler for economic development and poverty reduction. To provide perspective, we first review the history of telecottages, local technology-equipped facilities to foster community-based learning, which have evolved into "telecenters" or "Community Learning Centers" (CLCs). Second, we describe extensive site visits to CLCs in impoverished portions of China and Mexico, the centers operated by premier universities in each respective country. These CLCs constitute the core of new emerging systems of distance education, and their newness poses challenges and opportunities, which are discussed. Finally, we offer 12 points to develop further the concept and reality of distance learning in support of economic development.

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Training anesthesiology residents in providing anesthesia for awake craniotomy: learning curves and estimate of needed case load.

PubMed

Bilotta, Federico; Titi, Luca; Lanni, Fabiana; Stazi, Elisabetta; Rosa, Giovanni

2013-08-01

To measure the learning curves of residents in anesthesiology in providing anesthesia for awake craniotomy, and to estimate the case load needed to achieve a "good-excellent" level of competence. Prospective study. Operating room of a university hospital. 7 volunteer residents in anesthesiology. Residents underwent a dedicated training program of clinical characteristics of anesthesia for awake craniotomy. The program was divided into three tasks: local anesthesia, sedation-analgesia, and intraoperative hemodynamic management. The learning curve for each resident for each task was recorded over 10 procedures. Quantitative assessment of the individual's ability was based on the resident's self-assessment score and the attending anesthesiologist's judgment, and rated by modified 12 mm Likert scale, reported ability score visual analog scale (VAS). This ability VAS score ranged from 1 to 12 (ie, very poor, mild, moderate, sufficient, good, excellent). The number of requests for advice also was recorded (ie, resident requests for practical help and theoretical notions to accomplish the procedures). Each task had a specific learning rate; the number of procedures necessary to achieve "good-excellent" ability with confidence, as determined by the recorded results, were 10 procedures for local anesthesia, 15 to 25 procedures for sedation-analgesia, and 20 to 30 procedures for intraoperative hemodynamic management. Awake craniotomy is an approach used increasingly in neuroanesthesia. A dedicated training program based on learning specific tasks and building confidence with essential features provides "good-excellent" ability. © 2013 Elsevier Inc. All rights reserved.

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Localization of the lumbar discs using machine learning and exact probabilistic inference.

PubMed

Oktay, Ayse Betul; Akgul, Yusuf Sinan

2011-01-01

We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.

Comparing Local and International Chinese Students' English Language Learning Strategies

ERIC Educational Resources Information Center

Anthony, Margreat Aloysious; Ganesen, Sree Nithya

2012-01-01

According to Horwitz (1987) learners' belief about language learning are influenced by previous language learning experiences as well as cultural background. This study examined the English Language Learning Strategies between local and international Chinese students who share the same cultural background but have been exposed to different…

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

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

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.

Compiling a register of patients with moderate or severe learning disabilities: experience at one United Kingdom general practice.

PubMed

Lodge, Keri-Michèle; Milnes, David; Gilbody, Simon M

2011-03-01

Background Identifying patients with learning disabilities within primary care is central to initiatives for improving the health of this population. UK general practitioners (GPs) receive additional income for maintaining registers of patients with learning disabilities as part of the Quality and Outcomes Framework (QOF), and may opt to provide Directed Enhanced Services (DES), which requires practices to maintain registers of patients with moderate or severe learning disabilities and offer them annual health checks.Objectives This paper describes the development of a register of patients with moderate or severe learning disabilities at one UK general practice.Methods A Read code search of one UK general practice's electronic medical records was conducted in order to identify patients with learning disabilities. Confirmation of diagnoses was sought by scrutinising records and GP verification. Cross-referencing with the practice QOF register of patients with learning disabilities of any severity, and the local authority's list of clients with learning disabilities, was performed.Results Of 15 001 patients, 229 (1.5%) were identified by the Read code search as possibly having learning disabilities. Scrutiny of records and GP verification confirmed 64 had learning disabilities and 24 did not, but the presence or absence of learning disability remained unclear in 141 cases. Cross-referencing with the QOF register (n=81) and local authority list (n=49) revealed little overlap.Conclusion Identifying learning disability and assessing its severity are tasks GPs may be unfamiliar with, and relying on Read code searches may result in under-detection. Further research is needed to define optimum strategies for identifying, cross-referencing and validating practice-based registers of patients with learning disabilities.

Compiling a register of patients with moderate or severe learning disabilities: experience at one United Kingdom general practice

PubMed Central

2011-01-01

Background Identifying patients with learning disabilities within primary care is central to initiatives for improving the health of this population. UK general practitioners (GPs) receive additional income for maintaining registers of patients with learning disabilities as part of the Quality and Outcomes Framework (QOF), and may opt to provide Directed Enhanced Services (DES), which requires practices to maintain registers of patients with moderate or severe learning disabilities and offer them annual health checks. Objectives This paper describes the development of a register of patients with moderate or severe learning disabilities at one UK general practice. Methods A Read code search of one UK general practice's electronic medical records was conducted in order to identify patients with learning disabilities. Confirmation of diagnoses was sought by scrutinising records and GP verification. Cross-referencing with the practice QOF register of patients with learning disabilities of any severity, and the local authority's list of clients with learning disabilities, was performed. Results Of 15 001 patients, 229 (1.5%) were identified by the Read code search as possibly having learning disabilities. Scrutiny of records and GP verification confirmed 64 had learning disabilities and 24 did not, but the presence or absence of learning disability remained unclear in 141 cases. Cross-referencing with the QOF register (n=81) and local authority list (n=49) revealed little overlap. Conclusion Identifying learning disability and assessing its severity are tasks GPs may be unfamiliar with, and relying on Read code searches may result in under-detection. Further research is needed to define optimum strategies for identifying, cross-referencing and validating practice-based registers of patients with learning disabilities. PMID:22479290

Multiple-instance ensemble learning for hyperspectral images

NASA Astrophysics Data System (ADS)

Ergul, Ugur; Bilgin, Gokhan

2017-10-01

An ensemble framework for multiple-instance (MI) learning (MIL) is introduced for use in hyperspectral images (HSIs) by inspiring the bagging (bootstrap aggregation) method in ensemble learning. Ensemble-based bagging is performed by a small percentage of training samples, and MI bags are formed by a local windowing process with variable window sizes on selected instances. In addition to bootstrap aggregation, random subspace is another method used to diversify base classifiers. The proposed method is implemented using four MIL classification algorithms. The classifier model learning phase is carried out with MI bags, and the estimation phase is performed over single-test instances. In the experimental part of the study, two different HSIs that have ground-truth information are used, and comparative results are demonstrated with state-of-the-art classification methods. In general, the MI ensemble approach produces more compact results in terms of both diversity and error compared to equipollent non-MIL algorithms.

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

Why research-informed teaching in engineering education? A review of the evidence

NASA Astrophysics Data System (ADS)

Bubou, Gordon Monday; Offor, Ibebietei Temple; Bappa, Abubakar Saddiq

2017-05-01

Challenges of today's engineering education (EE) are emergent, necessitating calls for its reformation to empower future engineers function optimally as innovative leaders, in both local and international contexts. These challenges: keeping pace with technological dynamism; high attrition; and most importantly, quality teaching/learning require multifaceted approaches. But how can EE respond to the growing demand for relevant teaching? What can we do for engineering faculties to leverage on quality teaching? How do we embed quality teaching in EE? Scholarship of teaching and learning is advocated as one viable approach. It uses evidence-based teaching (EBT) strategies, and research-informed evidence to guide educational decisions regarding teaching and learning. We review the theories underpinning EBT, the scientific evidence on which it is based, and innovative instructional strategies that enhance active learning. Some of these issues have been discussed already, largely through developing countries lens. Nevertheless, linkages to equivalent global perspectives are presented here.

Drawing Connections Between Local and Global Observations: An Essential Element of Geoscience Education

NASA Astrophysics Data System (ADS)

Manduca, C. A.; Mogk, D. W.

2002-12-01

One of the hallmarks of geoscience research is the process of moving between observations and interpretations on local and global scales to develop an integrated understanding of Earth processes. Understanding this interplay is an important aspect of student geoscience learning which leads to an understanding of the fundamental principles of science and geoscience and of the connections between local natural phenomena or human activity and global processes. Several techniques that engage students in inquiry and discovery (as recommended in the National Science Education Standards, NRC 1996, Shaping the Future of Undergraduate Earth Science Education, AGU, 1997) hold promise for helping students make these connections. These include the development of global data sets from local observations (e.g. GLOBE); studying small scale or local phenomenon in the context of global models (e.g. carbon storage in local vegetation and its role in the carbon cycle); or an analysis of local environmental issues in a global context (e.g. a comparison of local flooding to flooding in other countries and analysis in the context of weather, geology and development patterns). Research on learning suggests that data-rich activities linking the local and global have excellent potential for enhancing student learning because 1) students have already developed observations and interpretations of their local environment which can serve as a starting point for constructing new knowledge and 2) this context may motivate learning and develop understanding that can be transferred to other situations. (How People Learn, NRC, 2001). Faculty and teachers at two recent workshops confirm that projects that involve local or global data can engage students in learning by providing real world context, creating student ownership of the learning process, and developing scientific skills applicable to the complex problems that characterize modern science and society. Workshop participants called for increased dissemination of examples of effective practice, evaluation of the impact of data-rich activities on learning, and further development of data access infrastructure and services. (for additional workshop results and discussion see http://serc.carleton.edu/research_education/usingdata)

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.

PubMed

Lustberg, Tim; van Soest, Johan; Gooding, Mark; Peressutti, Devis; Aljabar, Paul; van der Stoep, Judith; van Elmpt, Wouter; Dekker, Andre

2018-02-01

Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. Twenty CT scans of stage I-III NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Creating the first MedlinePlus "Go Local" service: lessons and recommendations from the evaluation of NC Health Info.

PubMed

Silbajoris, Christie; McDuffee, Diana; Olney, Cynthia

2007-01-01

NC Health Info is an online collection of North Carolina based health services Web sites that seamlessly links local health resources to topical health information on MedlinePlus, the National Library of Medicine's consumer health information Web site. NC Health Info was the first project to connect local resources with MedlinePlus in the "Go Local" initiative. As such, NC Health Info serves as a model for other states to follow in connecting their own local information with Medline- Plus. This paper describes the processes used and lessons learned during a year-long evaluation of NC Health Info. Evaluation results may be of interest and applicable to any existing or planned "Go Local" project.

Olivocochlear Efferent Control in Sound Localization and Experience-Dependent Learning

PubMed Central

Irving, Samuel; Moore, David R.; Liberman, M. Charles; Sumner, Christian J.

2012-01-01

Efferent auditory pathways have been implicated in sound localization and its plasticity. We examined the role of the olivocochlear system (OC) in horizontal sound localization by the ferret and in localization learning following unilateral earplugging. Under anesthesia, adult ferrets underwent olivocochlear bundle section at the floor of the fourth ventricle, either at the midline or laterally (left). Lesioned and control animals were trained to localize 1 s and 40ms amplitude-roved broadband noise stimuli from one of 12 loudspeakers. Neither type of lesion affected normal localization accuracy. All ferrets then received a left earplug and were tested and trained over 10 d. The plug profoundly disrupted localization. Ferrets in the control and lateral lesion groups improved significantly during subsequent training on the 1 s stimulus. No improvement (learning) occurred in the midline lesion group. Markedly poorer performance and failure to learn was observed with the 40 ms stimulus in all groups. Plug removal resulted in a rapid resumption of normal localization in all animals. Insertion of a subsequent plug in the right ear produced similar results to left earplugging. Learning in the lateral lesion group was independent of the side of the lesion relative to the earplug. Lesions in all reported cases were verified histologically. The results suggest the OC system is not needed for accurate localization, but that it is involved in relearning localization during unilateral conductive hearing loss. PMID:21325517

Online Neonatal Training and Orientation Programme in India (ONTOP-IN)--the way forward for distance education in developing countries.

PubMed

Thukral, Anu; Sasi, Arun; Chawla, Deepak; Datta, Parul; Wahid, Sheeza; Rao, Suman; Kannan, Venkatnarayan; Veeragandam, Aruna; Murki, Srinivas; Deorari, Ashok K

2012-12-01

Internet-based distance learning combined with local hands-on skill enhancement can provide high-quality standardized education to in-service healthcare professionals in a wide geographical area. Primary objective of this study was to evaluate the efficacy of internet-based distance learning in conjunction with local hands-on skill enhancement in improving knowledge and skills of essential newborn care among in-service nursing health professionals. A total of 98 participants from seven health facilities in India and Maldives were enrolled in the study. Delivery of course material staggered over 5 weeks in the form of two lessons every week was moderated by two to three online tutors at each site. Participants managed actual case scenarios, participated in discussion forums and synchronous chat sessions within a closed group. Skill learning was administered by local tutor at the partnering health facilities. Knowledge and skill enhancement were evaluated by administering online multiple-choice questions (MCQs) test and on-site objective structured clinical evaluation (OSCE) stations before and after completion of the course. Participants' satisfaction was evaluated on a five-point Likert scale. Among 98 participants enrolled in the study, 78 (79%) completed the post-test assessment. There was significant increase in knowledge and skills scores (MCQ test: mean difference: 6.4 (95% CI: 5.6-7.17), OSCE: mean difference: 15.4 (95% CI: 12.7-18.1). All the participants expressed satisfaction with content and delivery of the learning module. To conclude, online training and teaching in essential newborn care is feasible and acceptable for in-service nursing professionals and serves as a useful tool for professional development of their practical skills and knowledge.

The identity of the North East of England has been shaped by the rocks beneath our feet

NASA Astrophysics Data System (ADS)

Shields, Deborah

2017-04-01

Geology and Geography students within England learn about the earth's processes and human processes, however it is not always easy for them to see the link between them and to their own lives. The changes to the specification within A-level Geography has seen an emphasis on how processes are linked to their own lives and the local area. I am fortunate to teach both Geography and Geology and I want my students who study both subjects to appreciate the links within the subjects. I also want them to appreciate the local geology and see how it has shaped the North East of England. I have therefore, created a series of lessons to help them to explore the local geology and place identity of the North East of England. To help them to develop an understanding of how the local geology influences place identity. I have used an enquiry based approach which uses the KWL chart and a concept map for students to demonstrate their understanding. These lessons are structured using the learning cycle. The lessons are differentiated through the use of cheat sheets, different levels of hand-outs and grouping of students. The learning objectives are:- 1. Describe the Geology of the North East of England. 2. Explain at least one process which has formed local geology. 3. Define place identity. 4. Discuss the North East of England's identity. 5. Discuss how the local Geology has influenced the North East of England's identity. The North East of England's geology mainly consists of coal and limestone. There is rich industrial heritage of the North East which is based around coal mining. Therefore, coal mining has had a great impact on the identity of the North East of England. There are also a number of different SSSIs which is due to the Magnesium limestone in the area, which has helped to shape the identity of the region. There are a number of areas of outstanding natural beauty due to the local geology and this has helped to create a positive identity for the North East of England.

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

DOE PAGES

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; ...

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

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

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmentalmore » breathing studies. In conclusion, we implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.« less

Teaching students in place: the languages of third space learning

NASA Astrophysics Data System (ADS)

Morawski, Cynthia M.

2017-09-01

With a perceptive eye cast on geoscience pedagogy for students labeled as disabled, Martinez-Álvarez makes important contributions to the existing conversation on placed-based learning. It is in our local backyards, from the corner basketball court, to the mud bank of a city lake, to the adjacent field where rocky outcrops spill down to a forgotten farmer's field, that we find rich working material for connecting self and community, moving students' out-of-school experiences that feature their cultural and linguistic knowledge, from misconceptions to "alternative conceptions." Informed by her insights regarding the learning of students whose literacy does not match conventional classroom practice, geoscience learning in the place of third space can act as a model of meaning making across the entire curriculum. In the pages that follow, I transact, both aesthetically and efferently, with Martinez-Álvarez's text as she presents her research on special ways of learning in placed-based geoscience explorations with bilingual children experiencing disabilities.

45 CFR 2516.720 - What is the length of each type of grant?

Code of Federal Regulations, 2011 CFR

2011-10-01

... NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Funding Requirements § 2516.720 What... under § 2516.200 (a), (c) or (e); and (2) A grant to a local partnership for activities in a...

Development and Implementation of a School-based Obesity Prevention Intervention: Lessons Learned from Community-Based Participatory Research

PubMed Central

Uyeda, Kimberly; Bogart, Laura M.; Hawes-Dawson, Jennifer; Schuster, Mark A.

2010-01-01

Background National, state, and local policies aim to change school environments to prevent child obesity. Community-based participatory research (CBPR) can be effective in translating public health policy into practice. Objectives We describe lessons learned from developing and pilot testing a middle school-based obesity prevention intervention using CBPR in Los Angeles, California. Methods We formed a community–academic partnership between the Los Angeles Unified School District (LAUSD) and the UCLA/RAND Center for Adolescent Health Promotion to identify community needs and priorities for addressing adolescent obesity and to develop and pilot test a school-based intervention. Lessons Learned Academic partners need to be well-versed in organizational structures and policies. Partnerships should be built on relationships of trust, shared vision, and mutual capacity building, with genuine community engagement at multiple levels. Conclusion These lessons are critical, not only for partnering with schools on obesity prevention, but also for working in other community settings and on other health issues. PMID:20208226

Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization.

PubMed

Karayiannis, N B; Pai, P I

1999-02-01

This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.

Testing the limits of long-distance learning: learning beyond a three-segment window.

PubMed

Finley, Sara

2012-01-01

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ("sh") and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. Copyright © 2012 Cognitive Science Society, Inc.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum.

PubMed

Olde Scheper, Tjeerd V; Meredith, Rhiannon M; Mansvelder, Huibert D; van Pelt, Jaap; van Ooyen, Arjen

2017-01-01

Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

Identifying Key Elements of Community-Based ESD: ESD-J's Projects to Collect ESD in Practice in Japan and Asia

ERIC Educational Resources Information Center

Noguchi, Fumiko

2010-01-01

Since its establishment in 2003, the Japan Council on the UN Decade of Education for Sustainable Development (ESD-J) has paid close attention to informal learning processes in community-based efforts to promote local sustainable development. ESD-J carried out two projects to collect information on and visualise community-based ESD practice: the…

Medical student use of digital learning resources.

PubMed

Scott, Karen; Morris, Anne; Marais, Ben

2018-02-01

University students expect to use technology as part of their studies, yet health professional teachers can struggle with the change in student learning habits fuelled by technology. Our research aimed to document the learning habits of contemporary medical students during a clinical rotation by exploring the use of locally and externally developed digital and print self-directed learning resources, and study groups. We investigated the learning habits of final-stage medical students during their clinical paediatric rotation using mixed methods, involving learning analytics and a student questionnaire. Learning analytics tracked aggregate student usage statistics of locally produced e-learning resources on two learning management systems and mobile learning resources. The questionnaire recorded student-reported use of digital and print learning resources and study groups. The students made extensive use of digital self-directed learning resources, especially in the 2 weeks before the examination, which peaked the day before the written examination. All students used locally produced digital formative assessment, and most (74/98; 76%) also used digital resources developed by other institutions. Most reported finding locally produced e-learning resources beneficial for learning. In terms of traditional forms of self-directed learning, one-third (28/94; 30%) indicated that they never read the course textbook, and few students used face-to-face 39/98 (40%) or online 6/98 (6%) study groups. Learning analytics and student questionnaire data confirmed the extensive use of digital resources for self-directed learning. Through clarification of learning habits and experiences, we think teachers can help students to optimise effective learning strategies; however, the impact of contemporary learning habits on learning efficacy requires further evaluation. Health professional teachers can struggle with the change in student learning habits fuelled by technology. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Lesson Study to Scale up Research-Based Knowledge: A Randomized, Controlled Trial of Fractions Learning

ERIC Educational Resources Information Center

Lewis, Catherine; Perry, Rebecca

2017-01-01

An understanding of fractions eludes many U.S. students, and research-based knowledge about fraction, such as the utility of linear representations, has not broadly influenced instruction. This randomized trial of lesson study supported by mathematical resources assigned 39 educator teams across the United States to locally managed lesson study…

A Formative Evaluation of Healthy Habits, Healthy U: A Collaborative School-Based Cancer Education Program

ERIC Educational Resources Information Center

Anderson, Alicia; Spear, Caile; Pritchard, Mary; George, Kayla; Young, Kyle; Smith, Carrie

2017-01-01

Purpose: Healthy Habits, Healthy U (HHHU) is a two-day school-based primary prevention cancer education program that uses interactive classroom presentations designed to help students learn how to reduce their cancer risks. HHHU is a collaboration between a local cancer hospital, school district and university. HHHU incorporates real cancerous and…

QUICR-learning for Multi-Agent Coordination

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2006-01-01

Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Constructing Learning Spaces? Videoconferencing at Local Learning Centres in Sweden

ERIC Educational Resources Information Center

Logdlund, Ulrik

2010-01-01

This article explores videoconferencing in the context of local learning centres in Sweden. The practice is described as a "learning space" in which adult learners construct socio-spatial relations. The study goes beyond a sociological apprehension of actors and opposes the idea of the material as neutral, passive and conformed by…

Show Me the Evidence: How a Unit Challenge Can Support Middle School Teachers and Students in Investigating Climate Change Using Real-World Data and Science Practices

NASA Astrophysics Data System (ADS)

Gochis, E. E.; Tubman, S.; Grazul, K.; Bluth, G.; Huntoon, J. E.

2017-12-01

Michigan Science Teaching and Assessment Reform (Mi-STAR) is developing an NGSS-aligned integrated science middle school curriculum and associated teacher professional learning program that addresses all performance expectations for the 6-8 grade-band. The Mi-STAR instructional model is a unit- and lesson-level model that scaffolds students in using science practices to investigate scientific phenomena and apply engineering principles to address a real-world challenge. Mi-STAR has developed an 8th grade unit on climate change based on the Mi-STAR instructional model and NGSS performance expectations. The unit was developed in collaboration with Michigan teachers, climate scientists, and curriculum developers. The unit puts students in the role of advisers to local officials who need an evidence-based explanation of climate change and recommendations about community-based actions to address it. Students discover puzzling signs of global climate change, ask questions about these signs, and engage in a series of investigations using simulations and real data to develop scientific models for the mechanisms of climate change. Students use their models as the basis for evidence-based arguments about the causes and impacts of climate change and employ engineering practices to propose local actions in their community to address climate change. Dedicated professional learning supports teachers before and during implementation of the unit. Before implementing the unit, all teachers complete an online self-paced "unit primer" during which they assume the role of their students as they are introduced to the unit challenge. During this experience, teachers experience science as a practice by using real data and simulations to develop a model of the causes of climate change, just as their students will later do. During unit implementation, teachers are part of a professional learning community led by a teacher facilitator in their local area or school. This professional learning community serves as a resource both for implementing student-directed pedagogy and for the development of content knowledge. Eight teachers pilot tested the unit with more than 500 students in spring 2017, and teachers who participated in the first professional learning cohort are currently implementing the unit around Michigan.

Discriminative object tracking via sparse representation and online dictionary learning.

PubMed

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Neuromorphic audio-visual sensor fusion on a sound-localizing robot.

PubMed

Chan, Vincent Yue-Sek; Jin, Craig T; van Schaik, André

2012-01-01

This paper presents the first robotic system featuring audio-visual (AV) sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localization through self motion and visual feedback, using an adaptive ITD-based sound localization algorithm. After training, the robot can localize sound sources (white or pink noise) in a reverberant environment with an RMS error of 4-5° in azimuth. We also investigate the AV source binding problem and an experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. Despite the simplicity of this method and a large number of false visual events in the background, a correct match can be made 75% of the time during the experiment.

Fragile X Mental Retardation Protein and Dendritic Local Translation of the Alpha Subunit of the Calcium/Calmodulin-Dependent Kinase II Messenger RNA Are Required for the Structural Plasticity Underlying Olfactory Learning.

PubMed

Daroles, Laura; Gribaudo, Simona; Doulazmi, Mohamed; Scotto-Lomassese, Sophie; Dubacq, Caroline; Mandairon, Nathalie; Greer, Charles August; Didier, Anne; Trembleau, Alain; Caillé, Isabelle

2016-07-15

In the adult brain, structural plasticity allowing gain or loss of synapses remodels circuits to support learning. In fragile X syndrome, the absence of fragile X mental retardation protein (FMRP) leads to defects in plasticity and learning deficits. FMRP is a master regulator of local translation but its implication in learning-induced structural plasticity is unknown. Using an olfactory learning task requiring adult-born olfactory bulb neurons and cell-specific ablation of FMRP, we investigated whether learning shapes adult-born neuron morphology during their synaptic integration and its dependence on FMRP. We used alpha subunit of the calcium/calmodulin-dependent kinase II (αCaMKII) mutant mice with altered dendritic localization of αCaMKII messenger RNA, as well as a reporter of αCaMKII local translation to investigate the role of this FMRP messenger RNA target in learning-dependent structural plasticity. Learning induces profound changes in dendritic architecture and spine morphology of adult-born neurons that are prevented by ablation of FMRP in adult-born neurons and rescued by an metabotropic glutamate receptor 5 antagonist. Moreover, dendritically translated αCaMKII is necessary for learning and associated structural modifications and learning triggers an FMRP-dependent increase of αCaMKII dendritic translation in adult-born neurons. Our results strongly suggest that FMRP mediates structural plasticity of olfactory bulb adult-born neurons to support olfactory learning through αCaMKII local translation. This reveals a new role for FMRP-regulated dendritic local translation in learning-induced structural plasticity. This might be of clinical relevance for the understanding of critical periods disruption in autism spectrum disorder patients, among which fragile X syndrome is the primary monogenic cause. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Squeezeposenet: Image Based Pose Regression with Small Convolutional Neural Networks for Real Time Uas Navigation

NASA Astrophysics Data System (ADS)

Müller, M. S.; Urban, S.; Jutzi, B.

2017-08-01

The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Low-dose CT reconstruction with patch based sparsity and similarity constraints

NASA Astrophysics Data System (ADS)

Xu, Qiong; Mou, Xuanqin

2014-03-01

As the rapid growth of CT based medical application, low-dose CT reconstruction becomes more and more important to human health. Compared with other methods, statistical iterative reconstruction (SIR) usually performs better in lowdose case. However, the reconstructed image quality of SIR highly depends on the prior based regularization due to the insufficient of low-dose data. The frequently-used regularization is developed from pixel based prior, such as the smoothness between adjacent pixels. This kind of pixel based constraint cannot distinguish noise and structures effectively. Recently, patch based methods, such as dictionary learning and non-local means filtering, have outperformed the conventional pixel based methods. Patch is a small area of image, which expresses structural information of image. In this paper, we propose to use patch based constraint to improve the image quality of low-dose CT reconstruction. In the SIR framework, both patch based sparsity and similarity are considered in the regularization term. On one hand, patch based sparsity is addressed by sparse representation and dictionary learning methods, on the other hand, patch based similarity is addressed by non-local means filtering method. We conducted a real data experiment to evaluate the proposed method. The experimental results validate this method can lead to better image with less noise and more detail than other methods in low-count and few-views cases.

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

PubMed

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

2015-04-01

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

Machine vision and appearance based learning

NASA Astrophysics Data System (ADS)

Bernstein, Alexander

2017-03-01

Smart algorithms are used in Machine vision to organize or extract high-level information from the available data. The resulted high-level understanding the content of images received from certain visual sensing system and belonged to an appearance space can be only a key first step in solving various specific tasks such as mobile robot navigation in uncertain environments, road detection in autonomous driving systems, etc. Appearance-based learning has become very popular in the field of machine vision. In general, the appearance of a scene is a function of the scene content, the lighting conditions, and the camera position. Mobile robots localization problem in machine learning framework via appearance space analysis is considered. This problem is reduced to certain regression on an appearance manifold problem, and newly regression on manifolds methods are used for its solution.

Mathematical modeling in realistic mathematics education

NASA Astrophysics Data System (ADS)

Riyanto, B.; Zulkardi; Putri, R. I. I.; Darmawijoyo

2017-12-01

The purpose of this paper is to produce Mathematical modelling in Realistics Mathematics Education of Junior High School. This study used development research consisting of 3 stages, namely analysis, design and evaluation. The success criteria of this study were obtained in the form of local instruction theory for school mathematical modelling learning which was valid and practical for students. The data were analyzed using descriptive analysis method as follows: (1) walk through, analysis based on the expert comments in the expert review to get Hypothetical Learning Trajectory for valid mathematical modelling learning; (2) analyzing the results of the review in one to one and small group to gain practicality. Based on the expert validation and students’ opinion and answers, the obtained mathematical modeling problem in Realistics Mathematics Education was valid and practical.

Village Green Design, Operations, and Maintenance Document

EPA Science Inventory

The Village Green Project is a community-based activity to demonstrate the capabilities of new real-time monitoring technology for residents and citizen scientists to learn about local air quality. The goal of the project is to provide the public and communities with information ...

Community-Based Engineering

ERIC Educational Resources Information Center

Dalvi, Tejaswini; Wendell, Kristen

2015-01-01

A team of science teacher educators working in collaboration with local elementary schools explored opportunities for science and engineering "learning by doing" in the particular context of urban elementary school communities. In this article, the authors present design task that helps students identify and find solutions to a…

Community-based prevention marketing: organizing a community for health behavior intervention.

PubMed

Bryant, Carol A; Brown, Kelli R McCormack; McDermott, Robert J; Forthofer, Melinda S; Bumpus, Elizabeth C; Calkins, Susan A; Zapata, Lauren B

2007-04-01

This article describes the application and refinement of community-based prevention marketing (CBPM), an example of community-based participatory research that blends social marketing theories and techniques and community organization principles to guide voluntary health behavior change. The Florida Prevention Research Center has worked with a community coalition in Sarasota County, Florida to define locally important health problems and issues and to develop responsive health-promotion interventions. The CBPM framework has evolved as academic and community-based researchers have gained experience applying it. Community boards can use marketing principles to design evidence-based strategies for addressing local public health concerns. Based on 6 years of experience with the "Believe in All Your Possibilities" program, lessons learned that have led to revision and improvement of the CBPM framework are described.

Localism: The Changing Picture for Adult Learning

ERIC Educational Resources Information Center

Lamb, Penny

2012-01-01

The rapidly changing picture on localism and the government's focus on local economic growth have significant implications for adult learning and skills providers in England. Government now sees a sense of place as key to economic growth and recognises the need for a renewed debate on how business and state interact with localities. There is a…

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.

Global faculty development: lessons learned from the Foundation for Advancement of International Medical Education and Research (FAIMER) initiatives.

PubMed

Burdick, William P

2014-08-01

Foundation for Advancement of International Medical Education and Research (FAIMER) faculty development programs have operated since 2001 and are designed to overcome many of the challenges inherent in global health collaborations, including alignment with local needs, avoiding persistent dependency, and development of trust. FAIMER fellowship programs, developed for midcareer faculty members in all health professions from around the world, share goals of strengthening knowledge and skills in education leadership, education methods, and project management and evaluation. Building community is another explicit goal that allows participants to support and learn from each other.The author recommends several practices for successful international collaborations based on 13 years of experience with FAIMER fellowships. These include using authentic education projects to maintain alignment with local needs and apply newly acquired knowledge and skills, teaching leadership across cultures with careful communication and adaptation of concepts to local environments, cultivating a strong field of health professions education to promote diffusion of ideas and advocate for policy change, intentionally promoting field development and leadership to reduce dependency, giving generously of time and resources, learning from others as much as teaching others, and recognizing that effective partnerships revolve around personal relationships to build trust. These strategies have enabled the FAIMER fellowship programs to stay aligned with local needs, reduce dependency, and maintain trust.

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

PubMed

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

PubMed Central

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-01-01

Abstract Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact andyli@ece.ufl.edu or aconesa@ufl.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29272325

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

PubMed

Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo

2018-02-15

Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.

Bats without borders: Predators learn novel prey cues from other predatory species.

PubMed

Patriquin, Krista J; Kohles, Jenna E; Page, Rachel A; Ratcliffe, John M

2018-03-01

Learning from others allows individuals to adapt rapidly to environmental change. Although conspecifics tend to be reliable models, heterospecifics with similar resource requirements may be suitable surrogates when conspecifics are few or unfamiliar with recent changes in resource availability. We tested whether Trachops cirrhosus , a gleaning bat that localizes prey using their mating calls, can learn about novel prey from conspecifics and the sympatric bat Lophostoma silvicolum. Specifically, we compared the rate for naïve T. cirrhosus to learn an unfamiliar tone from either a trained conspecific or heterospecific alone through trial and error or through social facilitation. T. cirrhosus learned this novel cue from L. silvicolum as quickly as from conspecifics. This is the first demonstration of social learning of a novel acoustic cue in bats and suggests that heterospecific learning may occur in nature. We propose that auditory-based social learning may help bats learn about unfamiliar prey and facilitate their adaptive radiation.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

The local enhancement conundrum: in search of the adaptive value of a social learning mechanism.

PubMed

Arbilly, Michal; Laland, Kevin N

2014-02-01

Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima facie appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer-scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. Copyright © 2013 Elsevier Inc. All rights reserved.

Do fourth year pharmacy students use Facebook to form workplace-based learning peer groups during rotations?

PubMed

Phillips, Jennifer; Gettig, Jacob; Goliak, Kristen; Allen, Sheila; Fjortoft, Nancy

2017-11-01

The objective of this study was to gain an understanding of whether pharmacy students are using Facebook ® to create formal or informal workplace-based peer groups to learn from each other and share information while completing their advanced pharmacy practice experiences (APPEs). Fourth-year pharmacy students from two colleges of pharmacy in the same geographical area were recruited by email to participate. Inclusion criteria were: completion of two or more APPEs, current assignment to an APPE rotation in the local area, and a Facebook ® profile. Two focus groups, of eight students each were conducted on each of the two colleges' campuses. An incentive to participate was provided. Thematic analysis was used to analyze responses. Students reported using Facebook ® to learn about rotation expectations, roles/responsibilities, and preceptors. However, frequency and depth of interactions varied among the participants. Most participants noted that they prefer more private methods of communication to learn about APPE experiences. Students found Facebook ® to be a good source of motivation and support during experiential learning. The use of social media sites like Facebook ® may help students form "virtual" workplace-based peer groups during APPEs. Pharmacy schools interested in providing support for formal workplace-based learning groups should consider using social media sites as one component of this program. Copyright © 2017 Elsevier Inc. All rights reserved.

Technology-Enhanced Physics Programme for Community-Based Science Learning: Innovative Design and Programme Evaluation in a Theme Park

NASA Astrophysics Data System (ADS)

Tho, Siew Wei; Chan, Ka Wing; Yeung, Yau Yuen

2015-10-01

In this study, a new physics education programme is specifically developed for a famous theme park in Hong Kong to provide community-based science learning to her visitors, involving her three newly constructed rides. We make innovative use of digital technologies in this programme and incorporate a rigorous evaluation of the learning effectiveness of the programme. A total of around 200 students from nine local secondary schools participated in both the physics programme and its subsequent evaluation which consists of a combination of research and assessment tools, including pre- and post-multiple-choice tests, a questionnaire survey and an interview as specifically developed for this programme, or adopted from some well-accepted research instruments. Based on the evaluation of students' academic performance, there are two educationally significant findings on enhancing the students' physics learning: (a) traditionally large gender differences in physics performance and interest of learning are mostly eliminated; and (b) a less-exciting ride called the aviator (instead of the most exciting roller-coaster ride) can induce the largest learning effect (or gain in academic performance) amongst teenagers. Besides, findings from the questionnaire survey and interviews of participants are reported to reveal their views, perceptions, positive and negative comments or feedback on this programme which could provide valuable insights for future development of other similar community-based programmes.

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters.

PubMed

Schneider, Matthias; Hirsch, Sven; Weber, Bruno; Székely, Gábor; Menze, Bjoern H

2015-01-01

We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. Copyright © 2014 Elsevier B.V. All rights reserved.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

IntellEditS: intelligent learning-based editor of segmentations.

PubMed

Harrison, Adam P; Birkbeck, Neil; Sofka, Michal

2013-01-01

Automatic segmentation techniques, despite demonstrating excellent overall accuracy, can often produce inaccuracies in local regions. As a result, correcting segmentations remains an important task that is often laborious, especially when done manually for 3D datasets. This work presents a powerful tool called Intelligent Learning-Based Editor of Segmentations (IntellEditS) that minimizes user effort and further improves segmentation accuracy. The tool partners interactive learning with an energy-minimization approach to editing. Based on interactive user input, a discriminative classifier is trained and applied to the edited 3D region to produce soft voxel labeling. The labels are integrated into a novel energy functional along with the existing segmentation and image data. Unlike the state of the art, IntellEditS is designed to correct segmentation results represented not only as masks but also as meshes. In addition, IntellEditS accepts intuitive boundary-based user interactions. The versatility and performance of IntellEditS are demonstrated on both MRI and CT datasets consisting of varied anatomical structures and resolutions.

Recruitment Strategies and Lessons Learned from the Children's Healthy Living Program Prevalence Survey

PubMed Central

Fialkowski, Marie K.; Yamanaka, Ashley; Wilkens, Lynne R.; Braun, Kathryn L.; Butel, Jean; Ettienne, Reynolette; McGlone, Katalina; Remengesau, Shelley; Power, Julianne M.; Johnson, Emihner; Gilmatam, Daisy; Fleming, Travis; Acosta, Mark; Belyeu-Camacho, Tayna; Shomour, Moria; Sigrah, Cecilia; Nigg, Claudio; Novotny, Rachel

2016-01-01

The US Affiliated Pacific region's childhood obesity prevalence has reached epidemic proportions. To guide program and policy development, a multi-site study was initiated, in collaboration with partners from across the region, to gather comprehensive information on the regional childhood obesity prevalence. The environmental and cultural diversity of the region presented challenges to recruiting for and implementing a shared community-based, public health research program. This paper presents the strategies used to recruit families with young children (n = 5775 for children 2 – 8 years old) for obesity-related measurement across eleven jurisdictions in the US Affiliated Pacific Region. Data were generated by site teams that provided summaries of their recruitment strategies and lessons learned. Conducting this large multi-site prevalence study required considerable coordination, time and flexibility. In every location, local staff knowledgeable of the community was hired to lead recruitment, and participant compensation reflected jurisdictional appropriateness (e.g., gift cards, vouchers, or cash). Although recruitment approaches were site-specific, they were predominantly school-based or a combination of school- and community-based. Lessons learned included the importance of organization buy-in; communication, and advance planning; local travel and site peculiarities; and flexibility. Future monitoring of childhood obesity prevalence in the region should consider ways to integrate measurement activities into existing organizational infrastructures for sustainability and cost-effectiveness, while meeting programmatic (e.g. study) goals. PMID:29546153

Recruitment Strategies and Lessons Learned from the Children's Healthy Living Program Prevalence Survey.

PubMed

Fialkowski, Marie K; Yamanaka, Ashley; Wilkens, Lynne R; Braun, Kathryn L; Butel, Jean; Ettienne, Reynolette; McGlone, Katalina; Remengesau, Shelley; Power, Julianne M; Johnson, Emihner; Gilmatam, Daisy; Fleming, Travis; Acosta, Mark; Belyeu-Camacho, Tayna; Shomour, Moria; Sigrah, Cecilia; Nigg, Claudio; Novotny, Rachel

2016-01-01

The US Affiliated Pacific region's childhood obesity prevalence has reached epidemic proportions. To guide program and policy development, a multi-site study was initiated, in collaboration with partners from across the region, to gather comprehensive information on the regional childhood obesity prevalence. The environmental and cultural diversity of the region presented challenges to recruiting for and implementing a shared community-based, public health research program. This paper presents the strategies used to recruit families with young children (n = 5775 for children 2 - 8 years old) for obesity-related measurement across eleven jurisdictions in the US Affiliated Pacific Region. Data were generated by site teams that provided summaries of their recruitment strategies and lessons learned. Conducting this large multi-site prevalence study required considerable coordination, time and flexibility. In every location, local staff knowledgeable of the community was hired to lead recruitment, and participant compensation reflected jurisdictional appropriateness (e.g., gift cards, vouchers, or cash). Although recruitment approaches were site-specific, they were predominantly school-based or a combination of school- and community-based. Lessons learned included the importance of organization buy-in; communication, and advance planning; local travel and site peculiarities; and flexibility. Future monitoring of childhood obesity prevalence in the region should consider ways to integrate measurement activities into existing organizational infrastructures for sustainability and cost-effectiveness, while meeting programmatic (e.g. study) goals.

Social Learning in the Ultimatum Game

PubMed Central

Zhang, Boyu

2013-01-01

In the ultimatum game, two players divide a sum of money. The proposer suggests how to split and the responder can accept or reject. If the suggestion is rejected, both players get nothing. The rational solution is that the responder accepts even the smallest offer but humans prefer fair share. In this paper, we study the ultimatum game by a learning-mutation process based on quantal response equilibrium, where players are assumed boundedly rational and make mistakes when estimating the payoffs of strategies. Social learning is never stabilized at the fair outcome or the rational outcome, but leads to oscillations from offering 40 percent to 50 percent. To be precise, there is a clear tendency to increase the mean offer if it is lower than 40 percent, but will decrease when it reaches the fair offer. If mutations occur rarely, fair behavior is favored in the limit of local mutation. If mutation rate is sufficiently high, fairness can evolve for both local mutation and global mutation. PMID:24023950

Dynamically stable associative learning: a neurobiologically based ANN and its applications

NASA Astrophysics Data System (ADS)

Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (< 10) and does not change with pattern size or content; and (7) The network exhibits monotonic convergence, reaching equilibrium (fully trained) values without oscillating. The performance of Dystal on pattern completion tasks such as faces with different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

Cloud-Based versus Local-Based Web Development Education: An Experimental Study in Learning Experience

ERIC Educational Resources Information Center

Pike, Ronald E.; Pittman, Jason M.; Hwang, Drew

2017-01-01

This paper investigates the use of a cloud computing environment to facilitate the teaching of web development at a university in the Southwestern United States. A between-subjects study of students in a web development course was conducted to assess the merits of a cloud computing environment instead of personal computers for developing websites.…

Teachers' Perceptions of Digital Game Based Learning as a Pertinent Instructional Method

ERIC Educational Resources Information Center

Zigo, Suzanne L.

2016-01-01

Digital game-based instruction is a relatively new phenomenon in the world of education. Teachers hold the key to unlock the world of DGBL within the classroom. Within the classroom a teacher is much like an artist with a blank canvas and typically artistic freedom is granted. What is taught, the curriculum, is generally mandated by local,…

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Bridging Scientific Expertise to Underserved Communities: Initiating and Sustaining Local STEM Outreach

NASA Astrophysics Data System (ADS)

Anderson, Tania; Kenney, Jessica; Maple, John

2017-06-01

This presentation will feature effective outreach strategies used to recruit, engage, and sustain student involvement from underserved communities in out-of-school science outreach programs. For example, one strategy is to partner with subject matter experts to provide your audience with a deeper understanding of and a unique perspective on current science. Join us to learn more about how you can initiate and sustain a STEM based program in your local community.

Students Active in Leadership.

ERIC Educational Resources Information Center

Brutcher, Robert

2001-01-01

Describes SAIL (Students Active in Leadership) as a school-based, youth-directed group. States that the program helps teenagers learn leadership skills by developing and implementing community service activities. SAIL finds partners with whom to collaborate among local businesses, government, and health associations, and these partners provide the…

Undergraduate nursing students' perceptions of service-learning through a school-based community project.

PubMed

Bassi, Sherry

2011-01-01

Service-learning (SL) is an experiential teaching method that combines instruction with community service, with the aim of enriching students' academic learning, interpersonal skills and sense of responsibility while making meaningful contributions to the community. However, measuring outcomes of service-learning projects is difficult. This article reports on the perceptions of 18 third-year undergraduate nursing students who took part in a pilot service-learning project targeting tobacco use in a local elementary school. Faculty members evaluated the program's outcomes by engaging students in structured reflection on the program about its relevance to their future careers as practicing professionals, especially in community-based settings. The students' perceptions were elicited through three sets of reflective assignments following the project. Findings from the reflective assignments suggest that the pilot program was successful in enhancing the students' academic, social, and personal development while building a partnership between the school of nursing and key players in the community, including school-based nurses, teachers, administrators, families, and community leaders. The author suggests that service-learning projects can help nursing students accomplish key developmental tasks of the college years (such as building their competence, autonomy, and integrity), while helping impart the skills and values they will need as they graduate and seek professional nursing roles.

Stop, Collaborate, and Listen: Lessons Learned from Collaborating with a Houston Environmental Justice Organization

NASA Astrophysics Data System (ADS)

Johnson, C.; Arellano, Y.; Phartiyal, P.

2016-12-01

Scientists are increasingly showing interest in conducting research at the community level, yet community groups often struggle with lack of access to scientific information. Collaborations between the two are mutually beneficial: scientists can include assessment of societal implications in their research, and community-specific scientific evidence can be used by local groups to inform public decisions that benefit community interests. Recognizing the need for and utility of such partnerships, the Center for Science and Democracy at the Union of Concerned Scientists, a science-based policy and advocacy organization, partnered with Texas Environmental Justice Advocacy Services (TEJAS), an environmental justice organization based in Manchester in Houston, to provide the technical support and resources needed to strengthen TEJAS' advocacy work. Working closely with TEJAS, we connected community members with local experts, developed educational products to inform community members about environmental health risks in their neighborhoods, published a report highlighting chemical safety issues in the community, and assisted in constructing a community survey to assess residents' health concerns. The products were created with the intention of raising the profile of these issues with local government and regional EPA officials. This talk will discuss the projects done in collaboration with TEJAS, as well as important lessons learned that offer insight into best practices for other organizations and technical experts to partner with community groups on local projects.

Underlying Mechanisms of Cooperativity, Input Specificity, and Associativity of Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis.

PubMed

Hao, Lijie; Yang, Zhuoqin; Lei, Jinzhi

2018-01-01

Long-term potentiation (LTP) is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF) signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.

Decentralized reinforcement-learning control and emergence of motion patterns

NASA Astrophysics Data System (ADS)

Svinin, Mikhail; Yamada, Kazuyaki; Okhura, Kazuhiro; Ueda, Kanji

1998-10-01

In this paper we propose a system for studying emergence of motion patterns in autonomous mobile robotic systems. The system implements an instance-based reinforcement learning control. Three spaces are of importance in formulation of the control scheme. They are the work space, the sensor space, and the action space. Important feature of our system is that all these spaces are assumed to be continuous. The core part of the system is a classifier system. Based on the sensory state space analysis, the control is decentralized and is specified at the lowest level of the control system. However, the local controllers are implicitly connected through the perceived environment information. Therefore, they constitute a dynamic environment with respect to each other. The proposed control scheme is tested under simulation for a mobile robot in a navigation task. It is shown that some patterns of global behavior--such as collision avoidance, wall-following, light-seeking--can emerge from the local controllers.

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

PubMed

He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang

2016-01-01

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

Investigating Health Disparities through Community-Based Participatory Research: Lessons Learned from a Process Evaluation

PubMed Central

Bryan, Valerie; Brye, Willette; Hudson, Kenneth; Dubose, Leevones; Hansberry, Shantisha; Arrieta, Martha

2014-01-01

This article describes one university's efforts to partner with a local agency (the “Coalition”) within a disadvantaged, predominantly African American neighborhood, to assist them with studying their community's health disparities and health care access. The final, mutually agreed-upon plan used a community-based participatory research approach, wherein university researchers prepared neighborhood volunteers and Coalition members to conduct face-to-face interviews with residents about their health and health care access. Subsequently, the Coalition surveyed 138 residents, and the agency now possesses extensive data about the nature and extent of health problems in their community. Lessons learned from these experiences are offered. PMID:24871770

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel

Academic-practice collaboration in nursing education: service-learning for injury prevention.

PubMed

Alexander, Gina K; Canclini, Sharon B; Krauser, Debbie L

2014-01-01

Teams of senior-level baccalaureate nursing students at a private, urban university complete a population-focused public health nursing practicum through service-learning partnerships. Recently, students collaborated with local service agencies for Safe Communities America, a program of the National Safety Council in affiliation with the World Health Organization. This article describes the student-led process of community assessment, followed by systematic planning, implementation, and evaluation of evidence-based interventions to advance prescription drug overdose/poisoning prevention efforts in the community.

Change as Challenge for Shop-Floor Learning: The Case of Western and Local Manufacturing Companies in South China.

ERIC Educational Resources Information Center

Hong, Jianzhong

2000-01-01

Explores the process of workplace learning and problem solving by examining Western and local enterprises in South China. Discusses whether the managerial concepts embedded in Chinese culture help or impede collective learning and concludes that new ways of working and learning are emerging through the interaction of Western and Chinese culture.…

Mathematics. Grades 3, 6, 8, 10, 12. State Goals for Learning and Sample Learning Objectives.

ERIC Educational Resources Information Center

Illinois State Board of Education, Springfield. Dept. of School Improvement Services.

This publication is designed to provide assistance to local school districts in Illinois in meeting two new requirements: (1) to submit objectives for student learning to the State Board of Education which meet or exceed the State Goals for Learning and (2) to identify local goals for excellence in education. School districts have the option to…

Encouraging Sudanese Schoolboys to Learn English Effectively--A Case Study of Learning EFL in Eddueim Locality's Schools

ERIC Educational Resources Information Center

Gawi, Elsadig Mohamed Khalifa

2013-01-01

This study is aiming at investigating the impact of encouragement on Sudanese learners when learning EFL. The main question of the present study is asking about the influence of encouragement on learning EFL in Sudanese setting. Population of this study are English teachers and students in Eddueim Locality's schools in Sudan. Questionnaire was…

Influence of an Intensive, Field-Based Life Science Course on Preservice Teachers' Self-Efficacy for Environmental Science Teaching

NASA Astrophysics Data System (ADS)

Trauth-Nare, Amy

2015-08-01

Personal and professional experiences influence teachers' perceptions of their ability to implement environmental science curricula and to positively impact students' learning. The purpose of this study was twofold: to determine what influence, if any, an intensive field-based life science course and service learning had on preservice teachers' self-efficacy for teaching about the environment and to determine which aspects of the combined field-based course/service learning preservice teachers perceived as effective for enhancing their self-efficacy. Data were collected from class documents and written teaching reflections of 38 middle-level preservice teachers. Some participants ( n = 18) also completed the Environmental Education Efficacy Belief Instrument at the beginning and end of the semester. Both qualitative and quantitative data analyses indicated a significant increase in PSTs' personal efficacies for environmental teaching, t(17) = 4.50, p = .000, d = 1.30, 95 % CI (.33, .90), but not outcome expectancy, t(17) = 1.15, p = .268, d = .220, 95 % CI (-.06, .20). Preservice teachers reported three aspects of the course as important for enhancing their self-efficacies: learning about ecological concepts through place-based issues, service learning with K-5 students and EE curriculum development. Data from this study extend prior work by indicating that practical experiences with students were not the sole factor in shaping PSTs' self-efficacy; learning ecological concepts and theories in field-based activities grounded in the local landscape also influenced PSTs' self-efficacy.

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Solving and Learning Soft Temporal Constraints: Experimental Scenario and Examples

NASA Technical Reports Server (NTRS)

Rossi, F.; Venable, K. B.; Sperduti, A.; Khatib, L.; Morris, P.; Morris, R.; Koga, Dennis (Technical Monitor)

2001-01-01

Soft temporal constraint problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preference use machine learning techniques which learn the local preferences from the global ones. In this paper we describe the existing framework for both solving and learning preferences in temporal constraint problems, the implemented modules, the experimental scenario, and preliminary results on some examples.

Document page structure learning for fixed-layout e-books using conditional random fields

NASA Astrophysics Data System (ADS)

Tao, Xin; Tang, Zhi; Xu, Canhui

2013-12-01

In this paper, a model is proposed to learn logical structure of fixed-layout document pages by combining support vector machine (SVM) and conditional random fields (CRF). Features related to each logical label and their dependencies are extracted from various original Portable Document Format (PDF) attributes. Both local evidence and contextual dependencies are integrated in the proposed model so as to achieve better logical labeling performance. With the merits of SVM as local discriminative classifier and CRF modeling contextual correlations of adjacent fragments, it is capable of resolving the ambiguities of semantic labels. The experimental results show that CRF based models with both tree and chain graph structures outperform the SVM model with an increase of macro-averaged F1 by about 10%.

Learning moment-based fast local binary descriptor

NASA Astrophysics Data System (ADS)

Bellarbi, Abdelkader; Zenati, Nadia; Otmane, Samir; Belghit, Hayet

2017-03-01

Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

A hypothetical learning trajectory for conceptualizing matrices as linear transformations

NASA Astrophysics Data System (ADS)

Andrews-Larson, Christine; Wawro, Megan; Zandieh, Michelle

2017-08-01

In this paper, we present a hypothetical learning trajectory (HLT) aimed at supporting students in developing flexible ways of reasoning about matrices as linear transformations in the context of introductory linear algebra. In our HLT, we highlight the integral role of the instructor in this development. Our HLT is based on the 'Italicizing N' task sequence, in which students work to generate, compose, and invert matrices that correspond to geometric transformations specified within the problem context. In particular, we describe the ways in which the students develop local transformation views of matrix multiplication (focused on individual mappings of input vectors to output vectors) and extend these local views to more global views in which matrices are conceptualized in terms of how they transform a space in a coordinated way.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

Benefits of off-campus education for students in the health sciences: a text-mining analysis.

PubMed

Nakagawa, Kazumasa; Asakawa, Yasuyoshi; Yamada, Keiko; Ushikubo, Mitsuko; Yoshida, Tohru; Yamaguchi, Haruyasu

2012-08-28

In Japan, few community-based approaches have been adopted in health-care professional education, and the appropriate content for such approaches has not been clarified. In establishing community-based education for health-care professionals, clarification of its learning effects is required. A community-based educational program was started in 2009 in the health sciences course at Gunma University, and one of the main elements in this program is conducting classes outside school. The purpose of this study was to investigate using text-analysis methods how the off-campus program affects students. In all, 116 self-assessment worksheets submitted by students after participating in the off-campus classes were decomposed into words. The extracted words were carefully selected from the perspective of contained meaning or content. With the selected terms, the relations to each word were analyzed by means of cluster analysis. Cluster analysis was used to select and divide 32 extracted words into four clusters: cluster 1-"actually/direct," "learn/watch/hear," "how," "experience/participation," "local residents," "atmosphere in community-based clinical care settings," "favorable," "communication/conversation," and "study"; cluster 2-"work of staff member" and "role"; cluster 3-"interaction/communication," "understanding," "feel," "significant/important/necessity," and "think"; and cluster 4-"community," "confusing," "enjoyable," "proactive," "knowledge," "academic knowledge," and "class." The students who participated in the program achieved different types of learning through the off-campus classes. They also had a positive impression of the community-based experience and interaction with the local residents, which is considered a favorable outcome. Off-campus programs could be a useful educational approach for students in health sciences.

Human tracking in thermal images using adaptive particle filters with online random forest learning

NASA Astrophysics Data System (ADS)

Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

2013-11-01

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

The 3D Digital Story-telling Media on Batik Learning in Vocational High Schools

NASA Astrophysics Data System (ADS)

Widiaty, I.; Achdiani, Y.; Kuntadi, I.; Mubaroq, S. R.; Zakaria, D.

2018-02-01

The aim of this research is to make 3D digital Story-telling Media on Batik Learning in Vocational High School. The digital story-telling developed in this research is focused on 3D-based story-telling. In contrast to the digital story-telling that has been developed in existing learning, this research is expected to be able to improve understanding of vocational students about the value of local wisdom batik more meaningful and “live”. The process of making 3D digital story-telling media consists of two processes, namely the creation of 3D objects and the creation of 3D object viewer.

Ship localization in Santa Barbara Channel using machine learning classifiers.

PubMed

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

15 CFR 760.2 - Prohibitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... learning of these discussions, an official of the local boycott office in Y advises A's local branch... this part). (xvii) A, a U.S. exporter of machine tools, receives an order for drill presses from... learned of Y's requirement orally. It makes no difference how A learns about Y's discriminatory...

A Learning Democracy

ERIC Educational Resources Information Center

de Groot, Lucy

2009-01-01

Adult learning, in all its forms, is a pre-requisite for a dynamic local democracy where councils play a crucial role, politically, socially, and culturally. Local government has recognised that investment in adult learning provides significant benefits for the wider welfare and wellbeing of the community. This investment has not come solely…

A care improvement program acting as a powerful learning environment to support nursing students learning facilitation competencies.

PubMed

Jukema, Jan S; Harps-Timmerman, Annelies; Stoopendaal, Annemiek; Smits, Carolien H M

2015-11-01

Change management is an important area of training in undergraduate nursing education. Successful change management in healthcare aimed at improving practices requires facilitation skills that support teams in attaining the desired change. Developing facilitation skills in nursing students requires formal educational support. A Dutch Regional Care Improvement Program based on a nationwide format of change management in healthcare was designed to act as a Powerful Learning Environment for nursing students developing competencies in facilitating change. This article has two aims: to provide comprehensive insight into the program components and to describe students' learning experiences in developing their facilitation skills. This Dutch Regional Care Improvement Program considers three aspects of a Powerful Learning Environment: self-regulated learning; problem-based learning; and complex, realistic and challenging learning tasks. These three aspects were operationalised in five distinct areas of facilitation: increasing awareness of the need for change; leadership and project management; relationship building and communication; importance of the local context; and ongoing monitoring and evaluation. Over a period of 18 months, 42 nursing students, supported by trained lecturer-coaches, took part in nine improvement teams in our Regional Care Improvement Program, executing activities in all five areas of facilitation. Based on the students' experiences, we propose refinements to various components of this program, aimed at strengthenin the learning environment. There is a need for further detailed empirical research to study the impact this kind of learning environment has on students developing facilitation competencies in healthcare improvement. Copyright © 2015 Elsevier Ltd. All rights reserved.

Drug-related webpages classification based on multi-modal local decision fusion

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Su, Xiaojing; Liu, Yanxin

2018-03-01

In this paper, multi-modal local decision fusion is used for drug-related webpages classification. First, meaningful text are extracted through HTML parsing, and effective images are chosen by the FOCARSS algorithm. Second, six SVM classifiers are trained for six kinds of drug-taking instruments, which are represented by PHOG. One SVM classifier is trained for the cannabis, which is represented by the mid-feature of BOW model. For each instance in a webpage, seven SVMs give seven labels for its image, and other seven labels are given by searching the names of drug-taking instruments and cannabis in its related text. Concatenating seven labels of image and seven labels of text, the representation of those instances in webpages are generated. Last, Multi-Instance Learning is used to classify those drugrelated webpages. Experimental results demonstrate that the classification accuracy of multi-instance learning with multi-modal local decision fusion is much higher than those of single-modal classification.

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2013-01-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

A Statewide Partnership for Implementing Inquiry Science

NASA Astrophysics Data System (ADS)

Lytle, Charles

The North Carolina Infrastructure for Science Education (NC-ISE) is a statewide partnership for implementing standards-based inquiry science using exemplary curriculum materials in the public schools of North Carolina. North Carolina is the 11th most populous state in the USA with 8,000,000 residents, 117 school districts and a geographic area of 48,718 miles. NC-ISE partners include the state education agency, local school systems, three branches of the University of North Carolina, the state mathematics and science education network, businesses, and business groups. The partnership, based upon the Science for All Children model developed by the National Science Resources Centre, was initiated in 1997 for improvement in teaching and learning of science and mathematics. This research-based model has been successfully implemented in several American states during the past decade. Where effectively implemented, the model has led to significant improvements in student interest and student learning. It has also helped reduce the achievement gap between minority and non-minority students and among students from different economic levels. A key program element of the program is an annual Leadership Institute that helps teams of administrators and teachers develop a five-year strategic plan for their local systems. Currently 33 of the117 local school systems have joined the NC-ISE Program and are in various stages of implementation of inquiry science in grades K-8.

Development of teaching modules for geology and engineering coursework using terrestrial LiDAR scanning systems

NASA Astrophysics Data System (ADS)

Yarbrough, L. D.; Katzenstein, K.

2012-12-01

Exposing students to active and local examples of physical geologic processes is beneficial to the learning process. Students typically respond with interest to examples that use state-of-the-art technologies to investigate local or regional phenomena. For lower cognitive level of learning (e.g. knowledge, comprehension, and application), the use of "close-to-home" examples ensures that students better understand concepts. By providing these examples, the students may already have a familiarity or can easily visit the location. Furthermore, these local and regional examples help students to offer quickly other examples of similar phenomena. Investigation of these examples using normal photographic techniques, as well as a more sophisticated 3-D Light Detection And Ranging (LiDAR) (AKA Terrestrial Laser Scanning or TLS) system, allows students to gain a better understanding of the scale and the mechanics of the geologic processes and hazards. The systems are used for research, teaching and outreach efforts and depending on departmental policies can be accessible to students are various learning levels. TLS systems can yield scans at sub-centimeter resolution and contain surface reflectance of targets. These systems can serve a number of learning goals that are essential for training geoscientists and engineers. While querying the data to answer geotechnical or geomorphologic related questions, students will develop skills using large, spatial databases. The upper cognitive level of learning (e.g. analysis, synthesis, and evaluation) is also promoted by using a subset of the data and correlating the physical geologic process of stream bank erosion and rock slope failures with mathematical and computer models using the scanned data. Students use the examples and laboratory exercises to help build their engineering judgment skills with Earth materials. The students learn not only applications of math and engineering science but also the economic and social implication of designed engineering solutions. These course learning modules were developed for traditional geological engineering courses delivered on campus, for more intensive field work courses and online-based asynchronous course delivery.

Cultural influences on science museum practices: A case study

NASA Astrophysics Data System (ADS)

Duensing, Sally Jeanne

This dissertation looks at how informal science museums and centers both reflect and create the cultural contexts in which they are embedded. Specifically, it explores the multiple cultural perspectives held by the staff of the Yapollo Science Center in Trinidad, West Indies. This study focuses on how these perspectives impact the science center's sense of mission, design of educational programs, and development of exhibits. The findings in this case study have implications for other science museums and learning environments. Through the conduct and analysis of interviews, group meetings and on-site observations, this study found that there are several cultural domains in which staff perspectives of museum practice are situated. These include the local popular Trinidadian culture, the formal school system, and international science center community practices. For example, learning in the science center is seen by Yapollo staff as a social endeavor, more than an individual act. There is an emphasis on group engagement and social learning processes in exhibit design and teaching programs. The impact of local culture is further evidenced by Trinidadian practices of social learning and social competition in steel pan learning and calypso competition. These practices inform images of learning at Yapollo. The study highlights the role of formal educational systems by discussing how staff's informal educational approaches have resulted in a dialectic with the local formal British based school system practices. The study also explores the ways staff have adapted exhibit and program ideas from the international science museum. The synthesis of these cultures creates its own cultural ways of thinking and practice about exhibits and pedagogy that form the shared common wisdom at Yapollo. Museum practice, in this context, is viewed as a culture shaping enterprise that is itself shaped by culture. It demonstrates that teaching and learning practices occur in, and can be reflected upon, in multiple cultural contexts. The findings of this study have implications for many other areas of sociocultural and educational research.

Establishing a Campus-Based Assessment Program.

ERIC Educational Resources Information Center

Ewell, Peter T.

1987-01-01

Assessment has at two purposes--to improve teaching and learning and to promote greater external accountability. Determining an appropriate assessment approach depends on clear knowledge of what is intended, solid research about available instruments and about the experiences of other institutions, and a diagnosis of the local organizational and…

Humanitarian Engineering Placements in Our Own Communities

ERIC Educational Resources Information Center

VanderSteen, J. D. J.; Hall, K. R.; Baillie, C. A.

2010-01-01

There is an increasing interest in the humanitarian engineering curriculum, and a service-learning placement could be an important component of such a curriculum. International placements offer some important pedagogical advantages, but also have some practical and ethical limitations. Local community-based placements have the potential to be…

The Pedagogical Benefits of Participatory GIS for Geographic Education

ERIC Educational Resources Information Center

Sinha, Gaurav; Smucker, Thomas A.; Lovell, Eric J.; Velempini, Kgosietsile; Miller, Samuel A.; Weiner, Daniel; Wangui, Elizabeth Edna

2017-01-01

In this article, participatory geographic information systems GIS (PGIS) is explored and established as a powerful platform for geographic education. PGIS pedagogy can help educators meet diverse learning objectives pertaining to: (1) local knowledge and place-based thinking; (2) community engagement; (3) field mapping with geospatial…

Standardised Library Instruction Assessment: An Institution-Specific Approach

ERIC Educational Resources Information Center

Staley, Shannon M.; Branch, Nicole A.; Hewitt, Tom L.

2010-01-01

Introduction: We explore the use of a psychometric model for locally-relevant, information literacy assessment, using an online tool for standardised assessment of student learning during discipline-based library instruction sessions. Method: A quantitative approach to data collection and analysis was used, employing standardised multiple-choice…

International Students' Motivation and Learning Approach: A Comparison with Local Students

ERIC Educational Resources Information Center

Chue, Kah Loong; Nie, Youyan

2016-01-01

Psychological factors contribute to motivation and learning for international students as much as teaching strategies. 254 international students and 144 local students enrolled in a private education institute were surveyed regarding their perception of psychological needs support, their motivation and learning approach. The results from this…

Learning to play Go using recursive neural networks.

PubMed

Wu, Lin; Baldi, Pierre

2008-11-01

Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N(4), skills learned on a board of size N(2) can easily be transferred to boards of other sizes. A system trained using only 9 x 9 amateur game data performs surprisingly well on a test set derived from 19 x 19 professional game data. Possible directions for further improvements are briefly discussed.

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Women in water management: the need for local planning.

PubMed

Bhatt, M R

1995-08-01

This article on women's role in water resource management is based on a paper delivered at a seminar organized at the Water and Land Management Institute in Anand, India, in 1994. The article reflects Family Planning International's (FPI) experience in community-based water resource development. Most analyses of village and household water management data exclude women's role. The reasons are identified as the lack of inclusion of women's thinking in land-development research and planning, the dominance of males in planning and consequent male assumptions made about women's work and use of water, the lack of valuation of the nonmonetary nature of women's relationship to water, and the ease of ignoring women. Women's roles that are obstacles to inclusion in research and planning are identified as the lack of effective women's lobbies, the undervaluation by women of their work, and the lack of professional recognition of women as potential users of water or spokespersons for more than their own self-interests as women. National water policies are shifting to community-based management because local authorities are in daily contact with users, of whom about 50% are women. Historically national policy shifted from attention to distribution of investments in the water sector to reorganization of water agencies and to building up the capacity of private or voluntary agencies. The local context allows for more efficient and effective responses to local conditions. Local institutions and groups are better equipped to solicit local participation. One primary lesson learned by FPI is that local water resource planning is very important in strengthening the economic and individual capacity of poor people in underdeveloped areas. FPI's experience in Mahesana, Banaskantha, and Sabarkantha in Gujarat state supports this lesson learned. Water resource development policies resulted in mixed outcomes, and national control has been inefficient and disrespectful to local authorities. Another obstacle in Gujarat to water resource development is identified as increased demand for public water services and inadequate provision of services due to remoteness of the area and financial limitations of central agencies. Infrastructure is poorly maintained.

The "Local" Fetish as Reproductive Praxis in Democratic Learning

ERIC Educational Resources Information Center

Carpenter, Sara

2015-01-01

This article explores the theoretical conceptualization of the local as the preferential spatial domain for democratic participation and learning. It critiques the ideological nature of educational theory that bifurcates the local from the "the global" through the application of the Marxist concept of "fetish". The argument…

Local Area Networks and the Learning Lab of the Future.

ERIC Educational Resources Information Center

Ebersole, Dennis C.

1987-01-01

Considers educational applications of local area computer networks and discusses industry standards for design established by the International Standards Organization (ISO) and Institute of Electrical and Electronic Engineers (IEEE). A futuristic view of a learning laboratory using a local area network is presented. (Author/LRW)

Patch-based automatic retinal vessel segmentation in global and local structural context.

PubMed

Cao, Shuoying; Bharath, Anil A; Parker, Kim H; Ng, Jeffrey

2012-01-01

In this paper, we extend our published work [1] and propose an automated system to segment retinal vessel bed in digital fundus images with enough adaptability to analyze images from fluorescein angiography. This approach takes into account both the global and local context and enables both vessel segmentation and microvascular centreline extraction. These tools should allow researchers and clinicians to estimate and assess vessel diameter, capillary blood volume and microvascular topology for early stage disease detection, monitoring and treatment. Global vessel bed segmentation is achieved by combining phase-invariant orientation fields with neighbourhood pixel intensities in a patch-based feature vector for supervised learning. This approach is evaluated against benchmarks on the DRIVE database [2]. Local microvascular centrelines within Regions-of-Interest (ROIs) are segmented by linking the phase-invariant orientation measures with phase-selective local structure features. Our global and local structural segmentation can be used to assess both pathological structural alterations and microemboli occurrence in non-invasive clinical settings in a longitudinal study.

Climate Voices: Bridging Scientist Citizens and Local Communities across the United States

NASA Astrophysics Data System (ADS)

Wegner, K.; Ristvey, J. D., Jr.

2016-12-01

Based out of the University Corporation for Atmospheric Research (UCAR), the Climate Voices Science Speakers Network (climatevoices.org) has more than 400 participants across the United States that volunteer their time as scientist citizens in their local communities. Climate Voices experts engage in nonpartisan conversations about the local impacts of climate change with groups such as Rotary clubs, collaborate with faith-based groups on climate action initiatives, and disseminate their research findings to K-12 teachers and classrooms through webinars. To support their participants, Climate Voices develops partnerships with networks of community groups, provides trainings on how to engage these communities, and actively seeks community feedback. In this presentation, we will share case studies of science-community collaborations, including meta-analyses of collaborations and lessons learned.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A safety incident reporting system for primary care. A systematic literature review and consensus procedure by the LINNEAUS collaboration on patient safety in primary care

PubMed Central

Klemp, Kerstin; Zwart, Dorien; Hansen, Jørgen; Hellebek, Torben; Luettel, Dagmar; Verstappen, Wim; Beyer, Martin; Gerlach, Ferdin M.; Hoffmann, Barbara; Esmail, Aneez

2015-01-01

Background: Incident reporting is widely used in both patient safety improvement programmes, and in research on patient safety. Objective: To identify the key requirements for incident reporting systems in primary care; to develop an Internet-based incident reporting and learning system for primary care. Methods: A literature review looking at the purpose, design and requirements of an incident reporting system (IRS) was used to update an existing incident reporting system, widely used in Germany. Then, an international expert panel with knowledge on IRS developed the criteria for the design of a new web-based incident reporting system for European primary care. A small demonstration project was used to create a web-based reporting system, to be made freely available for practitioners and researchers. The expert group compiled recommendations regarding the desirable features of an incident reporting system for European primary care. These features covered the purpose of reporting, who should be involved in reporting, the mode of reporting, design considerations, feedback mechanisms and preconditions necessary for the implementation of an IRS. Results: A freely available web-based reporting form was developed, based on these criteria. It can be modified for local contexts. Practitioners and researchers can use this system as a means of recording patient safety incidents in their locality and use it as a basis for learning from errors. Conclusion: The LINNEAUS collaboration has provided a freely available incident reporting system that can be modified for a local context and used throughout Europe. PMID:26339835

Connecting People to Place: Stories, Science, Deep Maps, and Geo-Quests for Place-Based Learning

NASA Astrophysics Data System (ADS)

Hagley, C. A.; Silbernagel, J.; Host, G.; Hart, D. A.; Axler, R.; Fortner, R. W.; Axler, M.; Smith, V.; Drewes, A.; Bartsch, W.; Danz, N.; Mathews, J.; Wagler, M.

2016-02-01

The St. Louis River Estuary project (stlouisriverestuary.org) is about connecting the stories with the science of this special place to enhance spatial awareness and stewardship of the estuary. The stories, or spatial narratives, are told through vignettes of local resource activities, framed by perspectives of local people. The spatial narratives, developed through interviews and research, target six key activities of the estuary. The science is based on stressor gradients research, incorporating factors such as population and road density, pollutant point source density, and land use. The stressor gradient developed based on these factors was used as a basis for sampling water quality and plant and macroinvertebrate communities, with the intent of quantifying relationships between land-based stressors and aquatic ecosystem indicators of condition. The stories and science are interwoven, located in place on a Deep Map, and played out in GeoQuests to illustrate the complexity and multiple perspectives within the estuary's social, economic and ecological systems. Students, decision-makers, and Lake Superior enthusiasts can engage more deeply in the complexity of the stories and science by challenging themselves with these GeoQuests played on mobile devices. We hope these place-based learning tools will be valuable in advancing spatial literacy and conversation around environmental sustainability in coastal communities.

Building social capital with interprofessional student teams in rural settings: A service-learning model.

PubMed

Craig, Pippa L; Phillips, Christine; Hall, Sally

2016-08-01

To describe outcomes of a model of service learning in interprofessional learning (IPL) aimed at developing a sustainable model of training that also contributed to service strengthening. A total of 57 semi-structured interviews with key informants and document review exploring the impacts of interprofessional student teams engaged in locally relevant IPL activities. Six rural towns in South East New South Wales. Local facilitators, staff of local health and other services, health professionals who supervised the 89 students in 37 IPL teams, and academic and administrative staff. Perceived benefits as a consequence of interprofessional, service-learning interventions in these rural towns. Reported outcomes included increased local awareness of a particular issue addressed by the team; improved communication between different health professions; continued use of the team's product or a changed procedure in response to the teams' work; and evidence of improved use of a particular local health service. Given the limited workforce available in rural areas to supervise clinical IPL placements, a service-learning IPL model that aims to build social capital may be a useful educational model. © 2015 National Rural Health Alliance Inc.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Predicting protein complexes using a supervised learning method combined with local structural information.

PubMed

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

PubMed Central

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

PubMed

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Earth Systems Field Work: Service Learning at Local and Global Scales

NASA Astrophysics Data System (ADS)

Moore, A.; Derry, L. A.

2016-12-01

The Earth & Environmental Systems (EES) Field Program engages students in hands-on exploration along the boundaries of the living earth, solid earth, ocean, and atmosphere. Based on Hawaíi Island, the semester-length program integrates scientific study with environmental stewardship and service learning. Each year EES students contribute 3000 hours of service to their host community. Throughout the semester students engage in different service activities. Most courses includes a service component - for example - study of the role of invasive species in native ecosystems includes an invasive species removal project. Each student completes a 4-week service internship with a local school, NGO, state or federal agency. Finally, the student group works to offset the carbon footprint of the program in collaboration with local conservation projects. This effort sequesters CO2 emissions while at the same time contributing to reforestation of degraded native ecosystems. Students learn that expertise is not confined to "the academy," and that wisdom and inspiration can be found in unexpected venues. Much of the service learning in the EES Program occurs in collaboration with local partners. Service internships require students to identify a partner and to design a tractable project. Students work daily with their sponsor and make a formal presentation of their project at the end of the internship period. This includes speaking to a non-technical community gathering as well as to a scientific audience. For many students the opportunity to work on a real problem, of interest in the real world, is a highlight of the semester. Beyond working in support of local community groups, the EES Prograḿs C-neutral project engages students with work in service to the global commons. Here the outcome is not measurable within the time frame of a semester, yet the intangible result makes the experience even more powerful. Students take responsibility for an important issue that is not quantified in terms of an end-of-semester grade and without feedback from the academic or local community. By working through the process of calculating and offsetting their carbon footprint - entirely with their own labor - students learn that every individual has the tools and the ability to create change, and that they have the responsibility to do so.

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

PubMed

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

PubMed Central

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

PubMed

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Integrative, Interdisciplinary Learning in Bermuda Through Video Projects

NASA Astrophysics Data System (ADS)

Fox, R. J.; Connaughton, M.

2017-12-01

Understanding an ecosystem and how humans impact it requires a multidisciplinary perspective and immersive, experiential learning is an exceptional way to achieve understanding. In summer 2017 we took 18 students to the Bermuda Institute of Ocean Sciences (BIOS) as part of a Washington College two-week, four-credit summer field course. We took a multi-disciplinary approach in choosing the curriculum. We focused on the ecology of the islands and surrounding coral reefs as well as the environmental impacts humans are having on the islands. Additionally, we included geology and both local and natural history. Our teaching was supplemented by the BIOS staff and local tour guides. The student learning was integrated and reinforced through student-led video projects. Groups of three students were tasked with creating a 5-7 minute video appropriate for a public audience. We selected video topics based upon locations we would visit in the first week and topics were randomly assigned. The project intention was for the students to critically analyze and evaluate an area of Bermuda that is a worthwhile tourist destination. Students presented why a tourist should visit a locale, the area's ecological distinctiveness and complexity, the impact humans are having, and ways tourists can foster stewardship of that locale. These projects required students to learn how to make and edit videos, collaborate with peers, communicate a narrative to the public, integrate multi-disciplinary topics for a clear, whole-system perspective, observe the environment from a critical viewpoint, and interview local experts. The students produced the videos within the two-week period, and we viewed the videos as a group on the last day. The students worked hard, were proud of their final products, and produced excellent videos. They enjoyed the process, which provided them opportunities to collaborate, show individual strengths, be creative, and work independently of the instructors.

Random Boolean networks for autoassociative memory: Optimization and sequential learning

NASA Astrophysics Data System (ADS)

Sherrington, D.; Wong, K. Y. M.

Conventional neural networks are based on synaptic storage of information, even when the neural states are discrete and bounded. In general, the set of potential local operations is much greater. Here we discuss some aspects of the properties of networks of binary neurons with more general Boolean functions controlling the local dynamics. Two specific aspects are emphasised; (i) optimization in the presence of noise and (ii) a simple model for short-term memory exhibiting primacy and recency in the recall of sequentially taught patterns.

Local inhibition modulates learning-dependent song encoding in the songbird auditory cortex

PubMed Central

Thompson, Jason V.; Jeanne, James M.

2013-01-01

Changes in inhibition during development are well documented, but the role of inhibition in adult learning-related plasticity is not understood. In songbirds, vocal recognition learning alters the neural representation of songs across the auditory forebrain, including the caudomedial nidopallium (NCM), a region analogous to mammalian secondary auditory cortices. Here, we block local inhibition with the iontophoretic application of gabazine, while simultaneously measuring song-evoked spiking activity in NCM of European starlings trained to recognize sets of conspecific songs. We find that local inhibition differentially suppresses the responses to learned and unfamiliar songs and enhances spike-rate differences between learned categories of songs. These learning-dependent response patterns emerge, in part, through inhibitory modulation of selectivity for song components and the masking of responses to specific acoustic features without altering spectrotemporal tuning. The results describe a novel form of inhibitory modulation of the encoding of learned categories and demonstrate that inhibition plays a central role in shaping the responses of neurons to learned, natural signals. PMID:23155175

Deep Learning for Lowtextured Image Matching

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Fedorenko, V. V.; Fomin, N. A.

2018-05-01

Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Hence, automatic documentation of the archeological process using Structure from Motion (SfM) methods is challenging. Nevertheless, such documentation is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development of a new Wide Image Zone Adaptive Robust feature Descriptor (WIZARD) based on the deep learning. We use a convolutional auto-encoder to compress discriminative features of a local path into a descriptor code. We build a codebook to perform point matching on multiple images. The matching is performed using the nearest neighbor search and a modified voting algorithm. We present a new "Multi-view Amphora" (Amphora) dataset for evaluation of point matching algorithms. The dataset includes images of an Ancient Greek vase found at Taman Peninsula in Southern Russia. The dataset provides color images, a ground truth 3D model, and a ground truth optical flow. We evaluated the WIZARD descriptor on the "Amphora" dataset to show that it outperforms the SIFT and SURF descriptors on the complex patch pairs.

The Department of Veterans Affairs National Quality Scholars Fellowship Program

PubMed Central

Splaine, Mark E.; Ogrinc, Greg; Gilman, Stuart C.; Aron, David C.; Estrada, Carlos; Rosenthal, Gary E.; Lee, Sei; Dittus, Robert S.; Batalden, Paul B.

2013-01-01

The Department of Veterans Affairs National Quality Scholars Fellowship Program (VAQS) was established in 1998 as a post-graduate medical education fellowship to train physicians in new methods of improving the quality and safety of health care for Veterans and the nation. The VAQS curriculum is based on adult learning theory, with a national core curriculum of face-to-face components, technologically mediated distance learning components, and a unique local curriculum that draws from the strengths of regional resources. VAQS has established strong ties with other VA programs. Fellows’ research and projects are integrated with local and regional VA leaders’ priorities, enhancing the relevance and visibility of the fellows’ efforts and promoting recruitment of fellows to VA positions. VAQS has enrolled 96 fellows from 1999 to 2008; 75 have completed the program and 11 are currently enrolled. Fellowship graduates have pursued a variety of career paths: 20% are continuing training (most in VA); 32% hold a VA faculty/staff position; 63% are academic faculty; and 80% conduct clinical or research work related to health care improvement. Graduates have held leadership positions in VA, Department of Defense, and public health. Combining knowledge about the improvement of health care with adult learning strategies, distance learning technologies, face-to-face meetings, local mentorship, and experiential projects has been successful in improving care in VA and preparing physicians to participate in, study, and lead the improvement of health care quality and safety. PMID:19940583

Supporting Ecological Understanding through In-Depth and Imaginative Study of a Place-Based Topic or Issue

ERIC Educational Resources Information Center

Judson, Gillian

2015-01-01

Many have observed that the curriculum is a mile wide and scarcely an inch deep. This article provides a rationale for including in-depth study of a place-based/local topic within educational programs aimed at cultivating ecological understanding. Following a brief exploration of some of the obstacles to in-depth learning, it describes the ways in…

Developing a Critical Consciousness of Race in Place-Based Environmental Education: Franco's Story

ERIC Educational Resources Information Center

Miller, Hannah K.

2018-01-01

Environmental education (EE) has a history of support for critical place-based pedagogy as a means of learning through engagement in space, both cultural and biophysical. In this paper I tell the story of how Franco--a non-white, non-American undergraduate--engaged with local discourses in a watershed-focused EE program in the rural Midwestern US.…

A Field-Based Biomimicry Exercise Helps Students Discover Connections among Biodiversity, Form and Function, and Species Conservation during Earth's Sixth Extinction

ERIC Educational Resources Information Center

Soja, Constance M.

2014-01-01

In a first-year seminar on mass extinctions, a field-based, paleontology-focused exercise promotes active learning about Earth's biodiversity, form and function, and the biomimicry potential of ancient and modern life. Students study Devonian fossils at a local quarry and gain foundational experience in describing anatomy and relating form to…

Learning from Successful School-based Vaccination Clinics during 2009 pH1N1

ERIC Educational Resources Information Center

Klaiman, Tamar; O'Connell, Katherine; Stoto, Michael A.

2014-01-01

Background: The 2009 H1N1 vaccination campaign was the largest in US history. State health departments received vaccines from the federal government and sent them to local health departments (LHDs) who were responsible for getting vaccines to the public. Many LHD's used school-based clinics to ensure children were the first to receive limited…

17,000 Islands, One Goal: Indonesia Turns to Online Resources to Create a Network of School-Based Coaches

ERIC Educational Resources Information Center

Burns, Mary

2010-01-01

Community has taken on a new meaning for several school-based coaches spread across Indonesia. For far too long in developing countries, educators have been forced to rely on one-shot centralized professional development for teachers and those who work with them. A shortage of money, locally trained staff, and access to learning materials has made…

The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

NASA Technical Reports Server (NTRS)

Chen, Alexander Y.

1990-01-01

Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Regulation of Local Ambient GABA Levels via Transporter-Mediated GABA Import and Export for Subliminal Learning.

PubMed

Hoshino, Osamu

2015-06-01

Perception of supraliminal stimuli might in general be reflected in bursts of action potentials (spikes), and their memory traces could be formed through spike-timing-dependent plasticity (STDP). Memory traces for subliminal stimuli might be formed in a different manner, because subliminal stimulation evokes a fraction (but not a burst) of spikes. Simulations of a cortical neural network model showed that a subliminal stimulus that was too brief (10 msec) to perceive transiently (more than about 500 msec) depolarized stimulus-relevant principal cells and hyperpolarized stimulus-irrelevant principal cells in a subthreshold manner. This led to a small increase or decrease in ongoing-spontaneous spiking activity frequency (less than 1 Hz). Synaptic modification based on STDP during this period effectively enhanced relevant synaptic weights, by which subliminal learning was improved. GABA transporters on GABAergic interneurons modulated local levels of ambient GABA. Ambient GABA molecules acted on extrasynaptic receptors, provided principal cells with tonic inhibitory currents, and contributed to achieving the subthreshold neuronal state. We suggest that ongoing-spontaneous synaptic alteration through STDP following subliminal stimulation may be a possible neuronal mechanism for leaving its memory trace in cortical circuitry. Regulation of local ambient GABA levels by transporter-mediated GABA import and export may be crucial for subliminal learning.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Primary care emergency team training in situ means learning in real context

PubMed Central

Brandstorp, Helen; Halvorsen, Peder A.; Sterud, Birgitte; Haugland, Bjørgun; Kirkengen, Anna Luise

2016-01-01

Objective The purpose of our study was to explore the local learning processes and to improve in situ team training in the primary care emergency teams with a focus on interaction. Design, setting and subjects As participating observers, we investigated locally organised trainings of teams constituted ad hoc, involving nurses, paramedics and general practitioners, in rural Norway. Subsequently, we facilitated focus discussions with local participants. We investigated what kinds of issues the participants chose to elaborate in these learning situations, why they did so, and whether and how local conditions improved during the course of three and a half years. In addition, we applied learning theories to explore and challenge our own and the local participants’ understanding of team training. Results In situ team training was experienced as challenging, engaging, and enabling. In the training sessions and later focus groups, the participants discussed a wide range of topics constitutive for learning in a sociocultural perspective, and topics constitutive for patient safety culture. The participants expanded the types of training sites, themes and the structures for participation, improved their understanding of communication and developed local procedures. The flexible structure of the model mirrors the complexity of medicine and provides space for the participants’ own sense of responsibility. Conclusion Challenging, monthly in situ team trainings organised by local health personnel facilitate many types of learning. The flexible training model provides space for the participants’ own sense of responsibility and priorities. Outcomes involve social and structural improvements, including a sustainable culture of patient safety. Key Points Challenging, monthly in situ team trainings, organised by local health personnel, facilitate many types of learning.The flexible structure of the training model mirrors the complexity of medicine and the realism of the simulation sessions.Providing room for the participants’ own priorities and sense of responsibility allows for improvement on several levels.The participants demonstrated a consistent, long-term motivation to strengthen safety, both for their patients and for themselves. PMID:27442268

Primary care emergency team training in situ means learning in real context.

PubMed

Brandstorp, Helen; Halvorsen, Peder A; Sterud, Birgitte; Haugland, Bjørgun; Kirkengen, Anna Luise

2016-09-01

The purpose of our study was to explore the local learning processes and to improve in situ team training in the primary care emergency teams with a focus on interaction. As participating observers, we investigated locally organised trainings of teams constituted ad hoc, involving nurses, paramedics and general practitioners, in rural Norway. Subsequently, we facilitated focus discussions with local participants. We investigated what kinds of issues the participants chose to elaborate in these learning situations, why they did so, and whether and how local conditions improved during the course of three and a half years. In addition, we applied learning theories to explore and challenge our own and the local participants' understanding of team training. In situ team training was experienced as challenging, engaging, and enabling. In the training sessions and later focus groups, the participants discussed a wide range of topics constitutive for learning in a sociocultural perspective, and topics constitutive for patient safety culture. The participants expanded the types of training sites, themes and the structures for participation, improved their understanding of communication and developed local procedures. The flexible structure of the model mirrors the complexity of medicine and provides space for the participants' own sense of responsibility. Challenging, monthly in situ team trainings organised by local health personnel facilitate many types of learning. The flexible training model provides space for the participants' own sense of responsibility and priorities. Outcomes involve social and structural improvements, including a sustainable culture of patient safety. KEY POINTS Challenging, monthly in situ team trainings, organised by local health personnel, facilitate many types of learning. The flexible structure of the training model mirrors the complexity of medicine and the realism of the simulation sessions. Providing room for the participants' own priorities and sense of responsibility allows for improvement on several levels. The participants demonstrated a consistent, long-term motivation to strengthen safety, both for their patients and for themselves.

Broadening the Conceptualization of Literacy in the Lives of Adults with Intellectual Disability

ERIC Educational Resources Information Center

Morgan, Michelle F.; Cuskelly, Monica; Moni, Karen B.

2011-01-01

Current pedagogical approaches recognize literacy as a social practice and yet school-based conceptualizations continue to dominate understandings of literacy learning of individuals with intellectual disability. Such understandings lead to local or everyday literacy practices being devalued and overlooked. Thus, for adults with intellectual…

Digital Geogames to Foster Local Biodiversity

ERIC Educational Resources Information Center

Schaal, Sonja; Schaal, Steffen; Lude, Armin

2015-01-01

The valuing of biodiversity is considered to be a first step towards its conservation. Therefore, the aim of the BioDiv2Go project is to combine sensuous experiences discovering biodiversity with mobile technology and a game-based learning approach. Following the competence model for environmental education (Roczen et al, 2014), Geogames (location…

Lowering Barriers to Undergraduate Research through Collaboration with Local Craft Breweries

ERIC Educational Resources Information Center

McDermott, M. Luke

2016-01-01

Laboratory research experiences are highly impactful learning environments for undergraduate students. However, a surprising number of chemistry students do not research. These students often do not research because they lack the time, interest, opportunity, or awareness. Course-based undergraduate research experiences can reach out to these…

Valor Collegiate Academies

ERIC Educational Resources Information Center

EDUCAUSE, 2015

2015-01-01

The four guiding principles behind the blended, competency-based, personalized learning model of Valor Collegiate Academies, a charter organization serving grades 5-12 in Nashville, TN: (1) Reflect the diversity of both our country and local community; (2) Personalize a student's experience to meet his/her unique academic and non-academic needs;…

Middle School and pH?

ERIC Educational Resources Information Center

Herricks, Susan

2007-01-01

A local middle school requested that the Water Center of Advanced Materials for Purification of Water With Systems (WaterCAMPWS), a National Science Foundation Science and Technology Center, provide an introduction to pH for their seventh-grade water-based service learning class. After sorting through a multitude of information about pH, a…

Facilitating the Progression of Modern Apprentices into Undergraduate Business Education.

ERIC Educational Resources Information Center

Chadwick, Simon

1999-01-01

A case study of a program to give apprentices access to undergraduate business education at a British university in cooperation with a local chamber of commerce identified these success factors: recognition that modern apprentices are unlike traditional college students and focus on technology, outcome-based learning, personal development, and…

School Students' Responses to Architecture: A Practical Studio Project.

ERIC Educational Resources Information Center

Hickman, Richard

2001-01-01

Describes a project with mixed ability learners attending Deacon's School (Peterborough, England). The project, which emphasized critical response to the built environment, involved students making "pop up cards" based on firsthand observation of local architecture. Students were encouraged to learn about art and design through reacting,…

Exploring Interagency Collaboration in a Secondary Transition Community of Practice

ERIC Educational Resources Information Center

Kester, Joan Eleanor

2013-01-01

This study examined how interagency collaboration occurs within one local transition community of practice using Wenger's (1998) social theory of learning. While postschool outcomes of youth with disabilities have improved moderately, there continue to be many barriers based upon changes in American society, including the diversity of the…

Data Sharing to Inform School-Based Asthma Services

ERIC Educational Resources Information Center

Portwood, Sharon G.; Nelson, Elissa B.

2013-01-01

Background: This article examines results and lessons learned from a collaborative project involving a large urban school district, its county health department, multiple community partners, and the local university to establish an effective system for data sharing to inform monitoring and evaluation of the Charlotte Mecklenburg Schools (CMS)…

Characteristics of Future Ready Leadership: A Research Synthesis

ERIC Educational Resources Information Center

Office of Educational Technology, US Department of Education, 2017

2017-01-01

Strong leadership is essential to systemic, sustainable change in education. Superintendents and their leadership teams, with the support of state and local leaders, are key to leading the transition to digital learning in their districts. Superintendents throughout the country have expressed the desire for evidence-based approaches they can rely…

Classroom Management. TESOL Classroom Practice Series

ERIC Educational Resources Information Center

Farrell, Thomas S. C., Ed.

2008-01-01

This series captures the dynamics of the contemporary ESOL classroom. It showcases state-of-the-art curricula, materials, tasks, and activities reflecting emerging trends in language education and seeks to build localized language teaching and learning theories based on teachers' and students' unique experiences in and beyond the classroom. Each…

To Adapt or Not to Adapt: Navigating an Implementation Conundrum

ERIC Educational Resources Information Center

Leko, Melinda M.

2015-01-01

Maximizing the effectiveness of evidence-based practices (EBPs) requires an optimal balance of implementation fidelity and adaptation so EBPs fit local contexts and meet the individual learning needs of students with disabilities. The framework for classifying adaptations presented in this article can help educators make decisions about whether…

Hunting for Ecological Learning

ERIC Educational Resources Information Center

Pontius, Joel B.; Greenwood, David A.; Ryan, Jessica L.; Greenwood, Eli A.

2013-01-01

Considering (a) the many potential connections between hunting, culture, and environmental thought, (b) how much hunters have contributed to the conservation movement and to the protection of a viable land base, and (c) renewed interest in hunting as part of the wider movement toward eating local, non-industrialized food, we seek to bring hunting…

Engaging Latino Communities from the Ground Up: Three Tools

ERIC Educational Resources Information Center

Erbstein, Nancy; Moncloa, Fe; Olagundoye, Stacy Shwartz; Diaz-Carrasco, Claudia; Hill, Russell

2017-01-01

California's 4-H Youth Development Program has adopted an asset-based community development approach to extending programming with Latino youths and families. This approach entails learning and relationship building with local Latino communities and building on untapped existing resources, such as Latino-serving organizations and networks. Here we…

The Contribution of Local Experiments and Negotiation Processes to Field-Level Learning in Emerging (Niche) Technologies: Meta-Analysis of 27 New Energy Projects in Europe

ERIC Educational Resources Information Center

Raven, Rob P. J. M.; Heiskanen, Eva; Lovio, Raimo; Hodson, Mike; Brohmann, Bettina

2008-01-01

This article examines how local experiments and negotiation processes contribute to social and field-level learning. The analysis is framed within the niche development literature, which offers a framework for analyzing the relation between projects in local contexts and the transfer of local experiences into generally applicable rules. The…

Using Appreciative Inquiry for an e-Learning Change Management Programme: The ENTICE Project at Brunel University

NASA Astrophysics Data System (ADS)

Murray, Linda A.; Alberts, Philip P.; Stephenson, Julia E.

Brunel University's e-Learning strategy provides direction for the teaching staff, but remains flexible. Although all Schools had engaged with e-Learning in the past, detailed consideration of effective e-Learning and the e-experience of students had not been generally in evidence. We sought to address this gap in the strategic work of schools by implementing a change management program, the major elements of which were the development of a local evidence-base of effectiveness of e-Learning practices and conversations for change. Our program was based on the Appreciative Inquiry (AI) method, which we adapted for this educational context. The aim was to identify the pedagogic value of the diverse range of e-Learning activities already being undertaken and to encourage more widespread use. There was also a longer-term objective of assisting schools to establish or review their own e-Learning strategies and action plans. In terms of the effectiveness of the process, it is evident that the AI methodology was very beneficial. There is greater awareness among academic staff of the range of e-Learning activities that are currently being used in teaching designs of teaching staff at the University and about student use and attitudes to those activities. The evidence provides inputs to the development/review of e-Learning action plans and strategies for each school, usually within the context of the overall school plan.

Service-Learning from a Distance: Partnering Multiple Universities and Local Governments in a Large Scale Initiative

ERIC Educational Resources Information Center

Poindexter, Sandra; Arnold, Pamela; Osterhout, Christopher

2009-01-01

Service-learning can be academically effective even when the distances between students and client organizations prevent face-to-face interchanges and site visits. Working with the State of Michigan and Michigan Townships Association, Michigan students from five universities learned about local government while helping Michigan townships develop…

Improving History Learning through Cultural Heritage, Local History and Technology

ERIC Educational Resources Information Center

Magro, Graça; de Carvalho, Joaquim Ramos; Marcelino, Maria José

2014-01-01

History learning is many times considered dull and demotivating by young students. Probably this is due because the learning process is disconnected from these students' reality and experience. One possible way to overcome this state of matters is to use technology like mobile devices with georeferencing software and local history and heritage…

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

PubMed Central

Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning.

PubMed

Zhong, Shan; Liu, Quan; Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2 -regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

PubMed Central

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control—enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates. PMID:28446872

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents.

PubMed

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Learning Grasp Context Distinctions that Generalize

NASA Technical Reports Server (NTRS)

Platt, Robert; Grupen, Roderic A.; Fagg, Andrew H.

2006-01-01

Control-based approaches to grasp synthesis create grasping behavior by sequencing and combining control primitives. In the absence of any other structure, these approaches must evaluate a large number of feasible control sequences as a function of object shape, object pose, and task. This work explores a new approach to grasp synthesis that limits consideration to variations on a generalized localize-reach-grasp control policy. A new learning algorithm, known as schema structured learning, is used to learn which instantiations of the generalized policy are most likely to lead to a successful grasp in different problem contexts. Two experiments are described where Dexter, a bimanual upper torso, learns to select an appropriate grasp strategy as a function of object eccentricity and orientation. In addition, it is shown that grasp skills learned in this way can generalize to new objects. Results are presented showing that after learning how to grasp a small, representative set of objects, the robot's performance quantitatively improves for similar objects that it has not experienced before.

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Contextual cueing in naturalistic scenes: Global and local contexts.

PubMed

Brockmole, James R; Castelhano, Monica S; Henderson, John M

2006-07-01

In contextual cueing, the position of a target within a group of distractors is learned over repeated exposure to a display with reference to a few nearby items rather than to the global pattern created by the elements. The authors contrasted the role of global and local contexts for contextual cueing in naturalistic scenes. Experiment 1 showed that learned target positions transfer when local information is altered but not when global information is changed. Experiment 2 showed that scene-target covariation is learned more slowly when local, but not global, information is repeated across trials than when global but not local information is repeated. Thus, in naturalistic scenes, observers are biased to associate target locations with global contexts. Copyright 2006 APA, all rights reserved.

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Integrating local research watersheds into hydrologic education: Lessons from the Dry Creek Experimental Watershed

NASA Astrophysics Data System (ADS)

McNamara, J. P.; Aishlin, P. S.; Flores, A. N.; Benner, S. G.; Marshall, H. P.; Pierce, J. L.

2014-12-01

While a proliferation of instrumented research watersheds and new data sharing technologies has transformed hydrologic research in recent decades, similar advances have not been realized in hydrologic education. Long-standing problems in hydrologic education include discontinuity of hydrologic topics from introductory to advanced courses, inconsistency of content across academic departments, and difficulties in development of laboratory and homework assignments utilizing large time series and spatial data sets. Hydrologic problems are typically not amenable to "back-of-the-chapter" examples. Local, long-term research watersheds offer solutions to these problems. Here, we describe our integration of research and monitoring programs in the Dry Creek Experimental Watershed into undergraduate and graduate hydrology programs at Boise State University. We developed a suite of watershed-based exercises into courses and curriculums using real, tangible datasets from the watershed to teach concepts not amenable to traditional textbook and lecture methods. The aggregation of exercises throughout a course or degree allows for scaffolding of concepts with progressive exposure of advanced concepts throughout a course or degree. The need for exercises of this type is growing as traditional lecture-based classes (passive learning from a local authoritative source) are being replaced with active learning courses that integrate many sources of information through situational factors.

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

An effective PSO-based memetic algorithm for flow shop scheduling.

PubMed

Liu, Bo; Wang, Ling; Jin, Yi-Hui

2007-02-01

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed.

Large developing receptive fields using a distributed and locally reprogrammable address-event receiver.

PubMed

Bamford, Simeon A; Murray, Alan F; Willshaw, David J

2010-02-01

A distributed and locally reprogrammable address-event receiver has been designed, in which incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology.