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

Science.gov (United States)

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.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

Science.gov (United States)

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Multi-task Vector Field Learning.

Science.gov (United States)

Lin, Binbin; Yang, Sen; Zhang, Chiyuan; Ye, Jieping; He, Xiaofei

2012-01-01

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously and identifying the shared information among tasks. Most of existing MTL methods focus on learning linear models under the supervised setting. We propose a novel semi-supervised and nonlinear approach for MTL using vector fields. A vector field is a smooth mapping from the manifold to the tangent spaces which can be viewed as a directional derivative of functions on the manifold. We argue that vector fields provide a natural way to exploit the geometric structure of data as well as the shared differential structure of tasks, both of which are crucial for semi-supervised multi-task learning. In this paper, we develop multi-task vector field learning (MTVFL) which learns the predictor functions and the vector fields simultaneously. MTVFL has the following key properties. (1) The vector fields MTVFL learns are close to the gradient fields of the predictor functions. (2) Within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace. (3) The vector fields from all tasks share a low dimensional subspace. We formalize our idea in a regularization framework and also provide a convex relaxation method to solve the original non-convex problem. The experimental results on synthetic and real data demonstrate the effectiveness of our proposed approach.

Multi-instance dictionary learning via multivariate performance measure optimization

KAUST Repository

Wang, Jim Jing-Yan

2016-12-29

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

Multi-instance dictionary learning via multivariate performance measure optimization

KAUST Repository

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

2016-01-01

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

A fast learning method for large scale and multi-class samples of SVM

Science.gov (United States)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Teaching a Large Multi-Level Class Using Different Strategies and Activities to Motivate English Language Learning

OpenAIRE

Julia Sevy

2016-01-01

Many challenges face English language teachers today, but two common problems in Ecuador specifically in universities are large class sizes and multi-level students. These problems can create boredom, anxiety, and over all lack of interest in English language learning. It is shown in this article how to combat these particular problems through various strategies utilized to teach to the students’ needs, help them work together and intrinsically motivate them to learn different English languag...

Multi-task feature learning by using trace norm regularization

Directory of Open Access Journals (Sweden)

Jiangmei Zhang

2017-11-01

Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

PENGEMBANGAN MEDIA LUBANG MULTI LEVEL UNTUK PEMBELAJARAN LEMPAR TANGKAP BOLA KECIL

Directory of Open Access Journals (Sweden)

Tri Aryo Trubus Anom

2017-02-01

Full Text Available This research aims to develop a media Hole Multi Level to capture the small ball throwing learning grade IV elementary school level that can increase students roll control. The model of the research is the development of research and data analysis in the form of a percentage of data types with a description of the qualitative and quantitative. Procedure development include; 1 Potential problems, 2 Data collection, 3 Early media product design Multi Level Hole, 4 Design Validation by expert penjas and learning experts, 5 Revision products I, 6 Trials I in MI Ma’arif NU Darmakradenan, 7 Product revision II, 8 Trial II at four elementary school in the village of Darmakradenan, 9 Product revision III, 10 And products. The results of expert validation against the media Pit Multi Level was 80%, I Test of 83,23%, and II trials of 85.97%. Those results can be concluded that the development of the media Pit Multi Level can be used to capture the small ball throwing learning grade IV elementary school level.

Improving Video Generation for Multi-functional Applications

OpenAIRE

Kratzwald, Bernhard; Huang, Zhiwu; Paudel, Danda Pani; Dinesh, Acharya; Van Gool, Luc

2017-01-01

In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications. Our improved video GAN model does not separate foreground from background nor dynamic from static patterns, but learns to generate the entire video clip conjointly. Our model can thus be trained to generate - and learn from - a broad set of videos with no restriction. This is achieved by designing a robust one-stream video generation architectur...

Learning Evaluation: blending quality improvement and implementation research methods to study healthcare innovations.

Science.gov (United States)

Balasubramanian, Bijal A; Cohen, Deborah J; Davis, Melinda M; Gunn, Rose; Dickinson, L Miriam; Miller, William L; Crabtree, Benjamin F; Stange, Kurt C

2015-03-10

In healthcare change interventions, on-the-ground learning about the implementation process is often lost because of a primary focus on outcome improvements. This paper describes the Learning Evaluation, a methodological approach that blends quality improvement and implementation research methods to study healthcare innovations. Learning Evaluation is an approach to multi-organization assessment. Qualitative and quantitative data are collected to conduct real-time assessment of implementation processes while also assessing changes in context, facilitating quality improvement using run charts and audit and feedback, and generating transportable lessons. Five principles are the foundation of this approach: (1) gather data to describe changes made by healthcare organizations and how changes are implemented; (2) collect process and outcome data relevant to healthcare organizations and to the research team; (3) assess multi-level contextual factors that affect implementation, process, outcome, and transportability; (4) assist healthcare organizations in using data for continuous quality improvement; and (5) operationalize common measurement strategies to generate transportable results. Learning Evaluation principles are applied across organizations by the following: (1) establishing a detailed understanding of the baseline implementation plan; (2) identifying target populations and tracking relevant process measures; (3) collecting and analyzing real-time quantitative and qualitative data on important contextual factors; (4) synthesizing data and emerging findings and sharing with stakeholders on an ongoing basis; and (5) harmonizing and fostering learning from process and outcome data. Application to a multi-site program focused on primary care and behavioral health integration shows the feasibility and utility of Learning Evaluation for generating real-time insights into evolving implementation processes. Learning Evaluation generates systematic and rigorous cross

Teaching a Large Multi-Level Class Using Different Strategies and Activities to Motivate English Language Learning

Directory of Open Access Journals (Sweden)

Julia Sevy

2016-09-01

Full Text Available Many challenges face English language teachers today, but two common problems in Ecuador specifically in universities are large class sizes and multi-level students. These problems can create boredom, anxiety, and over all lack of interest in English language learning. It is shown in this article how to combat these particular problems through various strategies utilized to teach to the students’ needs, help them work together and intrinsically motivate them to learn different English language skills, specifically grammar and sentence structure. These strategies include group work, task-based learning, the inverted or flipped classroom, role-play and intrinsic learning. The author explains how these strategies work in a specific group of university pupils in Ecuador to overcome these specific problems in a classroom, but without student participation they can be flawed.

A Container-based Trusted Multi-level Security Mechanism

Directory of Open Access Journals (Sweden)

Li Xiao-Yong

2017-01-01

Full Text Available Multi-level security mechanism has been widely applied in the military, government, defense and other domains in which information is required to be divided by security-level. Through this type of security mechanism, users at different security levels are provided with information at corresponding security levels. Traditional multi-level security mechanism which depends on the safety of operating system finally proved to be not practical. We propose a container-based trusted multi-level security mechanism in this paper to improve the applicability of the multi-level mechanism. It guarantees multi-level security of the system through a set of multi-level security policy rules and trusted techniques. The technical feasibility and application scenarios are also discussed. The ease of realization, strong practical significance and low cost of our method will largely expand the application of multi-level security mechanism in real life.

Governance and the Commons in a Multi-Level World

Directory of Open Access Journals (Sweden)

Derek Armitage

2007-11-01

Full Text Available Multi-level governance may facilitate learning and adaptation in complex social-ecological circumstances. Such arrangements should connect community-based management with regional/national government-level management, link scientific management and traditional management systems, encourage the sharing of knowledge and information, and promote collaboration and dialogue around goals and outcomes. Governance innovations of this type can thus build capacity to adapt to change and manage for resilience. However, critical reflection on the emergence of adaptive, multi-level governance for the commons is warranted. Drawing on examples from the North and South, the purpose of this review is to connect three complementary bodies of scholarship with insights for commons governance in a multi-level world: common property theory, resilience thinking and political ecology. From the commons and resilience literature, normative principles of adaptive, multi-level governance are synthesized (e.g., participation, accountability, leadership, knowledge pluralism, learning and trust. Political ecological interpretations, however, help to reveal the challenge of actualizing these principles and the contextual forces that make entrenched, top-down management systems resilient to change. These forces include the role of power, scale and levels of organization, knowledge valuation, the positioning of social actors and social constructions of nature. Also addressed are the policy narratives that shape governance, and the dialectic relationship among ecological systems and social change. tekst

An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources

Directory of Open Access Journals (Sweden)

H. Shayeghi

2017-12-01

Full Text Available This paper presents an online two-stage Q-learning based multi-agent (MA controller for load frequency control (LFC in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs. The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO algorithm and are fixed. The second one is a reinforcement learning (RL based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.

A diagram retrieval method with multi-label learning

Science.gov (United States)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

Detecting bots using multi-level traffic analysis

DEFF Research Database (Denmark)

Stevanovic, Matija; Pedersen, Jens Myrup

2016-01-01

introduces a novel multi-level botnet detection approach that performs network traffic analysis of three protocols widely considered as the main carriers of botnet Command and Control (C&C) and attack traffic, i.e. TCP, UDP and DNS. The proposed method relies on supervised machine learning for identifying...

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

Science.gov (United States)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Innovation in Multi-Level Governance for Energy Efficiency. Sharing experience with multi-level governance to enhance energy efficiency. Information paper

Energy Technology Data Exchange (ETDEWEB)

Jollands, Nigel; Gasc, Emilien; Pasquier, Sara Bryan

2009-12-15

Despite creating a plethora of national and international regulations and voluntary programmes to improve energy efficiency, countries are far from achieving full energy efficiency potential across all sectors of the economy. One major challenge, among numerous barriers, is policy implementation. One strategy that many national governments and international organisations have used to address the implementation issue is to engage regional and local authorities. To that end, many programmes have been created that foster energy efficiency action and collaboration across levels of government. The aim of this report is to identify trends and detail recent developments in multi-level governance in energy efficiency (MLGEE). By sharing lessons learned from daily practitioners in the field, the IEA hopes energy efficiency policy makers at all levels of government will be able to identify useful multilevel governance (MLG) practices across geographical and political contexts and use these to design robust programmes; modify existing programmes, and connect and share experiences with other policy makers in this field.

Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

Science.gov (United States)

Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

2014-01-01

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

Genistein improves spatial learning and memory in male rats with elevated glucose level during memory consolidation.

Science.gov (United States)

Kohara, Yumi; Kawaguchi, Shinichiro; Kuwahara, Rika; Uchida, Yutaro; Oku, Yushi; Yamashita, Kimihiro

2015-03-01

Cognitive dysfunction due to higher blood glucose level has been reported previously. Genistein (GEN) is a phytoestrogen that we hypothesized might lead to improved memory, despite elevated blood glucose levels at the time of memory consolidation. To investigate this hypothesis, we compared the effects of orally administered GEN on the central nervous system in normal versus glucose-loaded adult male rats. A battery of behavioral assessments was carried out. In the MAZE test, which measured spatial learning and memory, the time of normal rats was shortened by GEN treatment compared to the vehicle group, but only in the early stages of testing. In the glucose-loaded group, GEN treatment improved performance as mazes were advanced. In the open-field test, GEN treatment delayed habituation to the new environment in normal rats, and increased the exploratory behaviors of glucose-loaded rats. There were no significant differences observed for emotionality or fear-motivated learning and memory. Together, these results indicate that GEN treatment improved spatial learning and memory only in the early stages of testing in the normal state, but improved spatial learning and memory when glucose levels increased during memory consolidation. Copyright © 2014 Elsevier Inc. All rights reserved.

Development of multi-representation learning tools for the course of fundamental physics

Science.gov (United States)

Huda, C.; Siswanto, J.; Kurniawan, A. F.; Nuroso, H.

2016-08-01

This research is aimed at designing a learning tool based on multi-representation that can improve problem solving skills. It used the research and development approach. It was applied for the course of Fundamental Physics at Universitas PGRI Semarang for the 2014/2015 academic year. Results show gain analysis value of 0.68, which means some medium improvements. The result of t-test is shows a calculated value of 27.35 and a table t of 2.020 for df = 25 and α = 0.05. Results of pre-tests and post-tests increase from 23.45 to 76.15. Application of multi-representation learning tools significantly improves students’ grades.

Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

Directory of Open Access Journals (Sweden)

Johan Parent

2004-01-01

Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

Switching dynamics of multi-agent learning

NARCIS (Netherlands)

Vrancx, P.; Tuyls, K.P.; Westra, R.

2008-01-01

This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcement learning agents and replicator dynamics in stateless multi-agent games. More precisely, in this work we use a

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

Science.gov (United States)

Sanchez-Vazquez, Manuel J; Nielen, Mirjam; Edwards, Sandra A; Gunn, George J; Lewis, Fraser I

2012-08-31

Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

Conceptual Understanding and Representation Quality through Multi-representation Learning on Newton Law Content

Directory of Open Access Journals (Sweden)

Suci Furwati

2017-08-01

Full Text Available Abstract: Students who have good conceptual acquisition will be able to represent the concept by using multi representation. This study aims to determine the improvement of students' understanding of the concept of Newton's Law material, and the quality of representation used in solving problems on Newton's Law material. The results showed that the concept acquisition of students increased from the average of 35.32 to 78.97 with an effect size of 2.66 (strong and N-gain of 0.68 (medium. The quality of each type of student representation also increased from level 1 and level 2 up to level 3. Key Words: concept aquisition, represetation quality, multi representation learning, Newton’s Law Abstrak: Siswa yang memiliki penguasaan konsep yang baik akan mampu merepresentasikan konsep dengan menggunakan multi representasi. Penelitian ini bertujuan untuk mengetahui peningkatan pemahaman konsep siswa SMP pada materi Hukum Newton, dan kualitas representasi yang digunakan dalam menyelesaikan masalah pada materi Hukum Newton. Hasil penelitian menunjukkan bahwa penguasaan konsep siswa meningkat dari rata-rata 35,32 menjadi 78,97 dengan effect size sebesar 2,66 (kuat dan N-gain sebesar 0,68 (sedang. Kualitas tiap jenis representasi siswa juga mengalami peningkatan dari level 1 dan level 2 naik menjadi level 3. Kata kunci: hukum Newton, kualitas representasi, pemahaman konsep, pembelajaran multi representasi

Multi-agent machine learning a reinforcement approach

CERN Document Server

Schwartz, H M

2014-01-01

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-pla

Active Multi-Field Learning for Spam Filtering

OpenAIRE

Wuying Liu; Lin Wang; Mianzhu Yi; Nan Xie

2015-01-01

Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The...

Multi-level predictive maintenance for multi-component systems

International Nuclear Information System (INIS)

Nguyen, Kim-Anh; Do, Phuc; Grall, Antoine

2015-01-01

In this paper, a novel predictive maintenance policy with multi-level decision-making is proposed for multi-component system with complex structure. The main idea is to propose a decision-making process considered on two levels: system level and component one. The goal of the decision rules at the system level is to address if preventive maintenance actions are needed regarding the predictive reliability of the system. At component level the decision rules aim at identifying optimally a group of several components to be preventively maintained when preventive maintenance is trigged due to the system level decision. Selecting optimal components is based on a cost-based group improvement factor taking into account the predictive reliability of the components, the economic dependencies as well as the location of the components in the system. Moreover, a cost model is developed to find the optimal maintenance decision variables. A 14-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance policy. Different sensitivity analysis are also investigated and discussed. Indeed, the proposed policy provides more flexibility in maintenance decision-making for complex structure systems, hence leading to significant profits in terms of maintenance cost when compared with existing policies. - Highlights: • A predictive maintenance policy for complex structure systems is proposed. • Multi-level decision process based on prognostic results is proposed. • A cost-based group importance measure is introduced for decision-making. • Both positive and negative dependencies between components are investigated. • A cost model and Monte Carlo simulation are developed for optimization process.

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-06-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing p, q mixed norms (p D; 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p q 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers. © 2012 IEEE.

Improving e-learning by Emotive Feedback

DEFF Research Database (Denmark)

Sharp, Robin; Gjedde, Lisa

2011-01-01

This paper considers the use of feedback with emotive elements in order to improve the efficiency of e-learning for teaching complex technical subjects to the general public by stimulation of implicit learning. An example is presented, based on an effort to investigate the current level of IT sec......This paper considers the use of feedback with emotive elements in order to improve the efficiency of e-learning for teaching complex technical subjects to the general public by stimulation of implicit learning. An example is presented, based on an effort to investigate the current level...

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

Directory of Open Access Journals (Sweden)

Sanchez-Vazquez Manuel J

2012-08-01

Full Text Available Abstract Background Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Results Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. Conclusions The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

E-learning process maturity level: a conceptual framework

Science.gov (United States)

Rahmah, A.; Santoso, H. B.; Hasibuan, Z. A.

2018-03-01

ICT advancement is a sure thing with the impact influencing many domains, including learning in both formal and informal situations. It leads to a new mindset that we should not only utilize the given ICT to support the learning process, but also improve it gradually involving a lot of factors. These phenomenon is called e-learning process evolution. Accordingly, this study attempts to explore maturity level concept to provide the improvement direction gradually and progression monitoring for the individual e-learning process. Extensive literature review, observation, and forming constructs are conducted to develop a conceptual framework for e-learning process maturity level. The conceptual framework consists of learner, e-learning process, continuous improvement, evolution of e-learning process, technology, and learning objectives. Whilst, evolution of e-learning process depicted as current versus expected conditions of e-learning process maturity level. The study concludes that from the e-learning process maturity level conceptual framework, it may guide the evolution roadmap for e-learning process, accelerate the evolution, and decrease the negative impact of ICT. The conceptual framework will be verified and tested in the future study.

Multi-Label Learning via Random Label Selection for Protein Subcellular Multi-Locations Prediction.

Science.gov (United States)

Wang, Xiao; Li, Guo-Zheng

2013-03-12

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multi-location proteins to multiple proteins with single location, which doesn't take correlations among different subcellular locations into account. In this paper, a novel method named RALS (multi-label learning via RAndom Label Selection), is proposed to learn from multi-location proteins in an effective and efficient way. Through five-fold cross validation test on a benchmark dataset, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark datasets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multi-locations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

Instance annotation for multi-instance multi-label learning

Science.gov (United States)

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

2013-01-01

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

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

Science.gov (United States)

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Improving survey response rates from parents in school-based research using a multi-level approach.

Science.gov (United States)

Schilpzand, Elizabeth J; Sciberras, Emma; Efron, Daryl; Anderson, Vicki; Nicholson, Jan M

2015-01-01

While schools can provide a comprehensive sampling frame for community-based studies of children and their families, recruitment is challenging. Multi-level approaches which engage multiple school stakeholders have been recommended but few studies have documented their effects. This paper compares the impact of a standard versus enhanced engagement approach on multiple indicators of recruitment: parent response rates, response times, reminders required and sample characteristics. Parents and teachers were distributed a brief screening questionnaire as a first step for recruitment to a longitudinal study, with two cohorts recruited in consecutive years (cohort 1 2011, cohort 2 2012). For cohort 2, additional engagement strategies included the use of pre-notification postcards, improved study materials, and recruitment progress graphs provided to school staff. Chi-square and t-tests were used to examine cohort differences. Compared to cohort 1, a higher proportion of cohort 2 parents responded to the survey (76% versus 69%; p value of investing in a relatively simple multi-level strategy to maximise parent response rates, and potentially reduce recruitment time and costs.

Project Based Learning in Multi-Grade Class

Science.gov (United States)

Ciftci, Sabahattin; Baykan, Ayse Aysun

2013-01-01

The purpose of this study is to evaluate project based learning in multi-grade classes. This study, based on a student-centered learning approach, aims to analyze students' and parents' interpretations. The study was done in a primary village school belonging to the Centre of Batman, already adapting multi-grade classes in their education system,…

A Cognitive Skill Classification Based on Multi Objective Optimization Using Learning Vector Quantization for Serious Games

Directory of Open Access Journals (Sweden)

Moh. Aries Syufagi

2013-09-01

Full Text Available Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG. CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective XE "multi objective"  target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments employ 33 respondent players demonstrates that 61% of players have high trial and error, 21% have high carefully, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. 

An Improved Reinforcement Learning System Using Affective Factors

Directory of Open Access Journals (Sweden)

Takashi Kuremoto

2013-07-01

Full Text Available As a powerful and intelligent machine learning method, reinforcement learning (RL has been widely used in many fields such as game theory, adaptive control, multi-agent system, nonlinear forecasting, and so on. The main contribution of this technique is its exploration and exploitation approaches to find the optimal solution or semi-optimal solution of goal-directed problems. However, when RL is applied to multi-agent systems (MASs, problems such as “curse of dimension”, “perceptual aliasing problem”, and uncertainty of the environment constitute high hurdles to RL. Meanwhile, although RL is inspired by behavioral psychology and reward/punishment from the environment is used, higher mental factors such as affects, emotions, and motivations are rarely adopted in the learning procedure of RL. In this paper, to challenge agents learning in MASs, we propose a computational motivation function, which adopts two principle affective factors “Arousal” and “Pleasure” of Russell’s circumplex model of affects, to improve the learning performance of a conventional RL algorithm named Q-learning (QL. Compared with the conventional QL, computer simulations of pursuit problems with static and dynamic preys were carried out, and the results showed that the proposed method results in agents having a faster and more stable learning performance.

Multi-level spondylolysis.

Science.gov (United States)

Hersh, David S; Kim, Yong H; Razi, Afshin

2011-01-01

The incidence of isthmic spondylolysis is approximately 3% to 6% in the general population. Spondylolytic defects involving multiple vertebral levels, on the other hand, are extremely rare. Only a handful of reports have examined the outcomes of surgical treatment of multi-level spondylolysis. Here, we present one case of bilateral pars defects at L3, L4, and L5. The patient, a 46-year-old female, presented with lower back pain radiating into the left lower extremity. Radiographs and CT scans of the lumbar spine revealed bilateral pars defects at L3-L5. The patient underwent lumbar discectomy and interbody fusion of L4-S1 as well as direct repair of the pars defect at L3. There were no postoperative complications, and by seven months the patient had improved clinically. While previous reports describe the use of either direct repair or fusion in the treatment of spondylolysis, we are unaware of reports describing the use of both techniques at adjacent levels.

Fast Conflict Resolution Based on Reinforcement Learning in Multi-agent System

Institute of Scientific and Technical Information of China (English)

PIAOSonghao; HONGBingrong; CHUHaitao

2004-01-01

In multi-agent system where each agen thas a different goal (even the team of agents has the same goal), agents must be able to resolve conflicts arising in the process of achieving their goal. Many researchers presented methods for conflict resolution, e.g., Reinforcement learning (RL), but the conventional RL requires a large computation cost because every agent must learn, at the same time the overlap of actions selected by each agent results in local conflict. Therefore in this paper, we propose a novel method to solve these problems. In order to deal with the conflict within the multi-agent system, the concept of potential field function based Action selection priority level (ASPL) is brought forward. In this method, all kinds of environment factor that may have influence on the priority are effectively computed with the potential field function. So the priority to access the local resource can be decided rapidly. By avoiding the complex coordination mechanism used in general multi-agent system, the conflict in multi-agent system is settled more efficiently. Our system consists of RL with ASPL module and generalized rules module. Using ASPL, RL module chooses a proper cooperative behavior, and generalized rule module can accelerate the learning process. By applying the proposed method to Robot Soccer, the learning process can be accelerated. The results of simulation and real experiments indicate the effectiveness of the method.

Simultaneous allocation of distributed resources using improved teaching learning based optimization

International Nuclear Information System (INIS)

Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

2015-01-01

Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

Science.gov (United States)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field

Directory of Open Access Journals (Sweden)

Hong Sun

2013-05-01

Full Text Available This paper proposes a multi-level max-margin discriminative analysis (M3DA framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines and topic-level features (generated by MedLDA via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information.

Multi-level governance of forest resources (Editorial to the special feature

Directory of Open Access Journals (Sweden)

Esther Mwangi

2012-08-01

Full Text Available A major challenge for many researchers and practitioners relates to how to recognize and address cross-scale dynamics in space and over time in order to design and implement effective governance arrangements. This editorial provides an overview of the concept of multi-level governance (MLG. In particular we highlight definitional issues, why the concept matters as well as more practical concerns related to the processes and structure of multi-level governance. It is increasingly clear that multi-level governance of forest resources involves complex interactions of state, private and civil society actors at various levels, and institutions linking higher levels of social and political organization. Local communities are increasingly connected to global networks and influences. This creates new opportunities to learn and address problems but may also introduce new pressures and risks. We conclude by stressing the need for a much complex approach to the varieties of MLG to better understand how policies work as instruments of governance and to organize communities within systems of power and authority.

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

Science.gov (United States)

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

International Nuclear Information System (INIS)

Park, Gee Yong; Seong, Poong Hyun

1994-01-01

In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

Concurrent Learning of Control in Multi agent Sequential Decision Tasks

Science.gov (United States)

2018-04-17

Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement... learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable... learning of policies in Dec-POMDPs, established performance bounds, evaluated these algorithms both theoretically and empirically, The views

Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer’s Disease in AIBL Data: Group and Individual Analyses

Directory of Open Access Journals (Sweden)

Vahab Youssofzadeh

2017-07-01

Full Text Available Magnetic resonance imaging (MRI and positron emission tomography (PET are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD. Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB-PET data related to 58 AD, 108 mild cognitive impairment (MCI and 120 healthy elderly (HE subjects from the Australian imaging, biomarkers and lifestyle (AIBL dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (parahippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r2 = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r2 = 0.62 (p < 0.01. Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD.

A cross-level investigation of informal field-based learning and performance improvements.

Science.gov (United States)

Wolfson, Mikhail A; Tannenbaum, Scott I; Mathieu, John E; Maynard, M Travis

2018-01-01

Organizations often operate in complex and dynamic environments which place a premium on employees' ongoing learning and acquisition of new competencies. Additionally, the majority of learning in organizations does not take place in formal training settings, but we know relatively little about how informal field-based learning (IFBL) behaviors relate to changes in job performance. In this study, we first clarified the construct of IFBL as a subset of informal learning. Second, on the basis of this clarified construct definition, we developed a measure of IFBL behaviors and demonstrated its psychometric properties using (a) a sample of subject matter experts who made item content validity judgments and (b) both an Amazon Mechanical Turk sample (N = 400) and a sample of 1,707 healthcare employees. Third, we advanced a grounded theory of IFBL in healthcare, and related it to individuals' regulatory foci and contextual moderators of IFBL behaviors-job performance relationships using a cross-level design and lagged nonmethod bound measures. Specifically, using a sample of 407 healthcare workers from 49 hospital units, our results suggested that promotion-focused individuals, especially in well-staffed units, readily engage in IFBL behaviors. Additionally, we found that the IFBL-changes in job performance relationship was strengthened to the extent that individuals worked in units with relatively nonpunitive climates. Interestingly, staffing levels had a weakening moderating effect on the positive IFBL-performance improvements relationship. Detailed follow-up analyses revealed that the peculiar effect was attributable to differential relationships from IFBL subdimensions. Implications for future theory building, research, and practice are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Multi-Agent Framework for Virtual Learning Spaces.

Science.gov (United States)

Sheremetov, Leonid; Nunez, Gustavo

1999-01-01

Discussion of computer-supported collaborative learning, distributed artificial intelligence, and intelligent tutoring systems focuses on the concept of agents, and describes a virtual learning environment that has a multi-agent system. Describes a model of interactions in collaborative learning and discusses agents for Web-based virtual…

Multi-stage decoding of multi-level modulation codes

Science.gov (United States)

Lin, Shu; Kasami, Tadao; Costello, Daniel J., Jr.

1991-01-01

Various types of multi-stage decoding for multi-level modulation codes are investigated. It is shown that if the component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. Particularly, it is shown that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum soft-decision decoding of the code is very small, only a fraction of dB loss in signal to noise ratio at a bit error rate (BER) of 10(exp -6).

Identifying beneficial task relations for multi-task learning in deep neural networks

DEFF Research Database (Denmark)

Bingel, Joachim; Søgaard, Anders

2017-01-01

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

Interconnected Levels of Multi-Stage Marketing

DEFF Research Database (Denmark)

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. The results from the case study indicate that multi-stage marketing exists on different levels. Thus, managers...... must not only decide in general on the merits of multi-stage marketing for their firm, but must also decide on which level they will engage in multi-stage marketing. The triadic perspective enables a rich and multi-dimensional understanding of how different business relationships influence each other...... in a multi-stage marketing context. This understanding assists managers in assessing and balancing different aspects of multi- stage marketing. The triadic perspective also offers avenues for further research....

The use of multi representative learning materials: definitive, macroscopic, microscopic, symbolic, and practice in analyzing students’ concept understanding

Science.gov (United States)

Susilaningsih, E.; Wulandari, C.; Supartono; Kasmui; Alighiri, D.

2018-03-01

This research aims to compose learning material which contains definitive macroscopic, microscopic and symbolic to analyze students’ conceptual understanding in acid-base learning materials. This research was conducted in eleven grade, natural science class, senior high school 1 (SMAN 1) Karangtengah, Demak province, Indonesia as the low level of students’ conceptual understanding and the high level of students’ misconception. The data collecting technique is by test to assess the cognitive aspect, questionnaire to assess students’ responses to multi representative learning materials (definitive, macroscopic, microscopic, symbolic), and observation to assess students’ macroscopic aspects. Three validators validate the multi-representative learning materials (definitive, macroscopic, microscopic, symbolic). The results of the research show that the multi-representative learning materials (definitive, macroscopic, microscopes, symbolic) being used is valid in the average score 62 of 75. The data is analyzed using the descriptive qualitative method. The results of the research show that 72.934 % students understand, 7.977 % less understand, 8.831 % do not understand, and 10.256 % misconception. In comparison, the second experiment class shows 54.970 % students understand, 5.263% less understand, 11.988 % do not understand, 27.777 % misconception. In conclusion, the application of multi representative learning materials (definitive, macroscopic, microscopic, symbolic) can be used to analyze the students’ understanding of acid-base materials.

Multi-level trellis coded modulation and multi-stage decoding

Science.gov (United States)

Costello, Daniel J., Jr.; Wu, Jiantian; Lin, Shu

1990-01-01

Several constructions for multi-level trellis codes are presented and many codes with better performance than previously known codes are found. These codes provide a flexible trade-off between coding gain, decoding complexity, and decoding delay. New multi-level trellis coded modulation schemes using generalized set partitioning methods are developed for Quadrature Amplitude Modulation (QAM) and Phase Shift Keying (PSK) signal sets. New rotationally invariant multi-level trellis codes which can be combined with differential encoding to resolve phase ambiguity are presented.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

International Nuclear Information System (INIS)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-01-01

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

Science.gov (United States)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

The effectiveness of multi modal representation text books to improve student's scientific literacy of senior high school students

Science.gov (United States)

Zakiya, Hanifah; Sinaga, Parlindungan; Hamidah, Ida

2017-05-01

The results of field studies showed the ability of science literacy of students was still low. One root of the problem lies in the books used in learning is not oriented toward science literacy component. This study focused on the effectiveness of the use of textbook-oriented provisioning capability science literacy by using multi modal representation. The text books development method used Design Representational Approach Learning to Write (DRALW). Textbook design which was applied to the topic of "Kinetic Theory of Gases" is implemented in XI grade students of high school learning. Effectiveness is determined by consideration of the effect and the normalized percentage gain value, while the hypothesis was tested using Independent T-test. The results showed that the textbooks which were developed using multi-mode representation science can improve the literacy skills of students. Based on the size of the effect size textbooks developed with representation multi modal was found effective in improving students' science literacy skills. The improvement was occurred in all the competence and knowledge of scientific literacy. The hypothesis testing showed that there was a significant difference on the ability of science literacy between class that uses textbooks with multi modal representation and the class that uses the regular textbook used in schools.

Multi-level Control Framework for Enhanced Flexibility of Active Distribution Network

DEFF Research Database (Denmark)

Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

2017-01-01

In this paper, the control objectives of future active distribution networks with high penetration of renewables and flexible loads are analyzed and reviewed. From a state of the art review, the important control objectives seen from the perspective of a distribution system operator are identifie......-ordination and management of the network assets at different voltage levels and geographical locations. The paper finally shows the applicability of the multi-level control architecture to some of the key challenges in the distribution system operation by relevant scenarios....... to be hosting capacity improvement, high reliable operation and cost effective network management. Based on this review and a state of the art review concerning future distribution network control methods, a multi-level control architecture is constructed for an active distribution network, which satisfies...... the selected control objectives and provides enhanced flexibility. The control architecture is supported by generation/load forecasting and distribution state estimation techniques to improve the controllability of the network. The multi-level control architecture consists of three levels of hierarchical...

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-11-09

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing lp,q mixed norms (specifically p∈2,∞ and q=1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the L11 tracker) when p=q=1. Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. © 2012 Springer Science+Business Media New York.

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

Science.gov (United States)

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

2016-01-01

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

Multi-stage decoding for multi-level block modulation codes

Science.gov (United States)

Lin, Shu

1991-01-01

In this paper, we investigate various types of multi-stage decoding for multi-level block modulation codes, in which the decoding of a component code at each stage can be either soft-decision or hard-decision, maximum likelihood or bounded-distance. Error performance of codes is analyzed for a memoryless additive channel based on various types of multi-stage decoding, and upper bounds on the probability of an incorrect decoding are derived. Based on our study and computation results, we find that, if component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. In particular, we find that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum decoding of the overall code is very small: only a fraction of dB loss in SNR at the probability of an incorrect decoding for a block of 10(exp -6). Multi-stage decoding of multi-level modulation codes really offers a way to achieve the best of three worlds, bandwidth efficiency, coding gain, and decoding complexity.

Multi level configuration of ETO products

DEFF Research Database (Denmark)

Petersen, Thomas Ditlev; Jørgensen, Kaj Asbjørn; Hvolby, Hans-Henrik

2007-01-01

The paper introduces and defines central concepts related to multi level configuration and analyzes which challenges an engineer to order company must deal with to be able to realize a multi level configuration system. It is argued that high flexibility can be achieved and focus can be directed...... in certain business processes if a multi level configuration system is realized....

Multi-Level Secure Local Area Network

OpenAIRE

Naval Postgraduate School (U.S.); Center for Information Systems Studies Security and Research (CISR)

2011-01-01

Multi-Level Secure Local Area Network is a cost effective, multi-level, easy to use office environment leveraging existing high assurance technology. The Department of Defense and U.S. Government have an identified need to securely share information classified at differing security levels. Because there exist no commercial solutions to this problem, NPS is developing a MLS LAN. The MLS LAN extends high assurance capabilities of an evaluated multi-level secure system to commercial personal com...

A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

NARCIS (Netherlands)

De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

2008-01-01

De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

Using a multi-state Learning Community as an implementation strategy for immediate postpartum long-acting reversible contraception.

Science.gov (United States)

DeSisto, Carla L; Estrich, Cameron; Kroelinger, Charlan D; Goodman, David A; Pliska, Ellen; Mackie, Christine N; Waddell, Lisa F; Rankin, Kristin M

2017-11-21

Implementation strategies are imperative for the successful adoption and sustainability of complex evidence-based public health practices. Creating a learning collaborative is one strategy that was part of a recently published compilation of implementation strategy terms and definitions. In partnership with the Centers for Disease Control and Prevention and other partner agencies, the Association of State and Territorial Health Officials recently convened a multi-state Learning Community to support cross-state collaboration and provide technical assistance for improving state capacity to increase access to long-acting reversible contraception (LARC) in the immediate postpartum period, an evidence-based practice with the potential for reducing unintended pregnancy and improving maternal and child health outcomes. During 2015-2016, the Learning Community included multi-disciplinary, multi-agency teams of state health officials, payers, clinicians, and health department staff from 13 states. This qualitative study was conducted to better understand the successes, challenges, and strategies that the 13 US states in the Learning Community used for increasing access to immediate postpartum LARC. We conducted telephone interviews with each team in the Learning Community. Interviews were semi-structured and organized by the eight domains of the Learning Community. We coded transcribed interviews for facilitators, barriers, and implementation strategies, using a recent compilation of expert-defined implementation strategies as a foundation for coding the latter. Data analysis showed three ways that the activities of the Learning Community helped in policy implementation work: structure and accountability, validity, and preparing for potential challenges and opportunities. Further, the qualitative data demonstrated that the Learning Community integrated six other implementation strategies from the literature: organize clinician implementation team meetings, conduct

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

OWL model of multi-agent Smart-system of distance learning for people with vision disabilities

Directory of Open Access Journals (Sweden)

Galina A. Samigulina

2017-01-01

Full Text Available The aim of the study is to develop an ontological model of multiagent smart-system of distance learning for visually impaired people based on Java Agent Development Framework for obtaining high-quality engineering education in laboratories of join use on modern equipment.Materials and methods of research. In developing multi-agent smart-system of distance learning, using various agents based on cognitive, ontological, statistical and intellectual methods is important. It is more convenient to implement this task in the form of software using multi-agent approach and Java Agent Development Framework. The main advantages of the platform are stability of operation, clear interface, simplicity of creating agents and extensive user database. In multi-agent systems, the solution is obtained automatically as result of interaction of many independent, purposeful agents. Each agent can perform certain tasks and pursue specified goals. Intellectual multi-agent systems and practical applications in distance learning based on them are considered.Results. The structural diagram of functioning of smart system distance learning for visually impaired people using various agents based on the system approach and the multi-agent platform Java Agent Development Framework is developed. The complex approach of distance learning of visually impaired people for obtaining highquality engineering education in laboratories of joint use on modern equipment is offered.The ontological model of multi-agent smart-system with a detailed description of the functions of following agents is created: personal, manager, ontological, cognitive, statistical, intellectual, shared laboratory agent, health agent, assistant to the agent and state agent. These agents execute their individual functions and provide a quality environment for learning.Conclusion. Thus, the proposed smart-system of distance learning for visually impaired people can significantly improve effectiveness and

Multi-dimensional technology-enabled social learning approach

DEFF Research Database (Denmark)

Petreski, Hristijan; Tsekeridou, Sofia; Prasad, Neeli R.

2013-01-01

’t respond to this systemic and structural changes and/or challenges and retains its status quo than it is jeopardizing its own existence or the existence of the education, as we know it. This paper aims to precede one step further by proposing a multi-dimensional approach for technology-enabled social...... in learning while socializing within their learning communities. However, their “educational” usage is still limited to facilitation of online learning communities and to collaborative authoring of learning material complementary to existing formal (e-) learning services. If the educational system doesn...

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

International Nuclear Information System (INIS)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko; Harauchi, Hajime; Inamura, Kiyonari

1999-01-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

Multi-viewpoint Smartphone AR-based Learning System for Solar Movement Observations

Directory of Open Access Journals (Sweden)

Ke Tian

2014-06-01

Full Text Available Understanding solar movement (e.g., solar diurnal motion is difficult for those are beginning to learn about astronomy. Previous research has revealed that observation-based learning can help make astronomical phenomena clearer to understand for such learners. In this research, Smartphone Augmented Reality (AR technology and 3D content were used to develop a multi-viewpoint Smartphone AR-based learning system (M-VSARLS for solar movement observations that can be used in the real-world environment. The goal of this research is to assess the usefulness of the system, usability of the AR function and 3D content, and the overall effect of the system on the learner’s motivation through task-based experiments with follow-up questionnaires. The results show that the M-VSARL system is effective in improving the observational skills and learning ability of learners, and in enhancing their motivation to learn about solar movement.

Can we use Earth Observations to improve monthly water level forecasts?

Science.gov (United States)

Slater, L. J.; Villarini, G.

2017-12-01

Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

Extended feature-fusion guidelines to improve image-based multi-modal biometrics

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

Multi-segmental movement patterns reflect juggling complexity and skill level.

Science.gov (United States)

Zago, Matteo; Pacifici, Ilaria; Lovecchio, Nicola; Galli, Manuela; Federolf, Peter Andreas; Sforza, Chiarella

2017-08-01

The juggling action of six experts and six intermediates jugglers was recorded with a motion capture system and decomposed into its fundamental components through Principal Component Analysis. The aim was to quantify trends in movement dimensionality, multi-segmental patterns and rhythmicity as a function of proficiency level and task complexity. Dimensionality was quantified in terms of Residual Variance, while the Relative Amplitude was introduced to account for individual differences in movement components. We observed that: experience-related modifications in multi-segmental actions exist, such as the progressive reduction of error-correction movements, especially in complex task condition. The systematic identification of motor patterns sensitive to the acquisition of specific experience could accelerate the learning process. Copyright © 2017 Elsevier B.V. All rights reserved.

E-Learning Capability Maturity Level in Kingdom of Bahrain

Science.gov (United States)

Al-Ammary, Jaflah; Mohammed, Zainab; Omran, Fatima

2016-01-01

Despite the effectiveness of using e-learning, educational institutions are still facing many challenges with the e-learning infrastructure and technical aspects, practices and capabilities, and improvement in learning outcome. Hence, a need for framework to benchmark the e-learning capability maturity level and measure the extent to what it is…

The learner’s perspective in GP teaching practices with multi-level learners: a qualitative study

Science.gov (United States)

2014-01-01

Background Medical students, junior hospital doctors on rotation and general practice (GP) registrars are undertaking their training in clinical general practices in increasing numbers in Australia. Some practices have four levels of learner. This study aimed to explore how multi-level teaching (also called vertical integration of GP education and training) is occurring in clinical general practice and the impact of such teaching on the learner. Methods A qualitative research methodology was used with face-to-face, semi-structured interviews of medical students, junior hospital doctors, GP registrars and GP teachers in eight training practices in the region that taught all levels of learners. Interviews were audio-recorded and transcribed. Qualitative analysis was conducted using thematic analysis techniques aided by the use of the software package N-Vivo 9. Primary themes were identified and categorised by the co-investigators. Results 52 interviews were completed and analysed. Themes were identified relating to both the practice learning environment and teaching methods used. A practice environment where there is a strong teaching culture, enjoyment of learning, and flexible learning methods, as well as learning spaces and organised teaching arrangements, all contribute to positive learning from a learners’ perspective. Learners identified a number of innovative teaching methods and viewed them as positive. These included multi-level learner group tutorials in the practice, being taught by a team of teachers, including GP registrars and other health professionals, and access to a supernumerary GP supervisor (also termed “GP consultant teacher”). Other teaching methods that were viewed positively were parallel consulting, informal learning and rural hospital context integrated learning. Conclusions Vertical integration of GP education and training generally impacted positively on all levels of learner. This research has provided further evidence about the

Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

Directory of Open Access Journals (Sweden)

Wenjun Bai

2018-02-01

Full Text Available Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners’ emotional stability in jail, can be directly benefited from our framework. Abstract: To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e.,Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty—which can be quantified in Uncertainty Flow—is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our

Multi-Level Model

Directory of Open Access Journals (Sweden)

Constanta Nicoleta BODEA

2008-01-01

Full Text Available Is an original paper, which contains a hierarchical model with three levels, for determining the linearized non-homogeneous and homogeneous credibility premiums at company level, at sector level and at contract level, founded on the relevant covariance relations between the risk premium, the observations and the weighted averages. We give a rather explicit description of the input data for the multi- level hierarchical model used, only to show that in practical situations, there will always be enough data to apply credibility theory to a real insurance portfolio.

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study.

Science.gov (United States)

Liu, Qi; Xu, Qian; Zheng, Vincent W; Xue, Hong; Cao, Zhiwei; Yang, Qiang

2010-04-10

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

Directory of Open Access Journals (Sweden)

Xue Hong

2010-04-01

Full Text Available Abstract Background Gene silencing using exogenous small interfering RNAs (siRNAs is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. Results An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. Conclusions The knowledge gained from our study provides useful insights on how to

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

Energy Technology Data Exchange (ETDEWEB)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko [Kanazawa Univ. (Japan). School of Medicine; Harauchi, Hajime; Inamura, Kiyonari

1999-09-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

Improving survey response rates from parents in school-based research using a multi-level approach.

Directory of Open Access Journals (Sweden)

Elizabeth J Schilpzand

Full Text Available While schools can provide a comprehensive sampling frame for community-based studies of children and their families, recruitment is challenging. Multi-level approaches which engage multiple school stakeholders have been recommended but few studies have documented their effects. This paper compares the impact of a standard versus enhanced engagement approach on multiple indicators of recruitment: parent response rates, response times, reminders required and sample characteristics.Parents and teachers were distributed a brief screening questionnaire as a first step for recruitment to a longitudinal study, with two cohorts recruited in consecutive years (cohort 1 2011, cohort 2 2012. For cohort 2, additional engagement strategies included the use of pre-notification postcards, improved study materials, and recruitment progress graphs provided to school staff. Chi-square and t-tests were used to examine cohort differences.Compared to cohort 1, a higher proportion of cohort 2 parents responded to the survey (76% versus 69%; p < 0.001, consented to participate (71% versus 56%; p < 0.001, agreed to teacher participation (90% versus 82%; p < 0.001 and agreed to follow-up contact (91% versus 80%; p < 0.001. Fewer cohort 2 parents required reminders (52% versus 63%; p < 0.001, and cohort 2 parents responded more promptly than cohort 1 parents (mean difference: 19.4 days, 95% CI: 18.0 to 20.9, p < 0.001.These results illustrate the value of investing in a relatively simple multi-level strategy to maximise parent response rates, and potentially reduce recruitment time and costs.

Strategic farsighted learning in competitive multi-agent games

NARCIS (Netherlands)

t Hoen, P.J.; Bohté, S.M.; Poutré, la J.A.; Brewka, G.; Coradeschi, S.; Perini, A.

2006-01-01

We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-agent games. We make the observation that in a competitive setting with adaptive agents an agent's actions will (likely) result in changes in the opponents policies. In addition to accounting for the

Multi-level decision making models, methods and applications

CERN Document Server

Zhang, Guangquan; Gao, Ya

2015-01-01

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.  

An improved multi-domain convolution tracking algorithm

Science.gov (United States)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

Improving self-regulated learning junior high school students through computer-based learning

Science.gov (United States)

Nurjanah; Dahlan, J. A.

2018-05-01

This study is back grounded by the importance of self-regulated learning as an affective aspect that determines the success of students in learning mathematics. The purpose of this research is to see how the improvement of junior high school students' self-regulated learning through computer based learning is reviewed in whole and school level. This research used a quasi-experimental research method. This is because individual sample subjects are not randomly selected. The research design used is Pretest-and-Posttest Control Group Design. Subjects in this study were students of grade VIII junior high school in Bandung taken from high school (A) and middle school (B). The results of this study showed that the increase of the students' self-regulated learning who obtain learning with computer-based learning is higher than students who obtain conventional learning. School-level factors have a significant effect on increasing of the students' self-regulated learning.

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

An Instructional Design Framework to Improve Student Learning in a First-Year Engineering Class

Directory of Open Access Journals (Sweden)

Kumar Yelamarthi

2016-12-01

Full Text Available Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically enhance traditional flipped methodologies in a first-year engineering course, by using low-cost technology aids and proven pedagogical techniques to enhance student learning. Implemented in a first-year engineering course, this modified flipped model demonstrated an improved student awareness of essential engineering concepts and improved academic performance through collaborative and active learning activities, including flipped learning methodologies, without the need for expensive, formal active learning spaces. These findings have been validated through two studies and have shown similar results confirming that student learning is improved by the implementation of multi-pedagogical strategies in-formed by the use of an instructional design in a traditional classroom setting.

Percutaneous vertebroplasty for multi-level osteoporotic vertebral compression fractures

International Nuclear Information System (INIS)

Wang Gefang; Cheng Yongde; Wu Chungen; Zhang Ji; Gu Yifeng; Li Minghua

2008-01-01

Objective: To prospectively evaluate the clinical efficiency and safety of patients receiving percutaneous vertebroplasty due to multi-level osteoporotic vertebral compression fractures. Methods: A retrospective study was conducted to review eighty-six osteoporotic vertebral compression fracture patients including 23 with three and more levels of vertebroplasty. The outcome was considered carefully by pre and postoperatively the Visual Analogue Scale (VAS)for pain relief, the Oswestry Disability Index (ODI)for the improvement activity of daily life and also the accompanied imaging information. Results: All procedures were performed successfully. Three patients had a transient high blood pressure and dyspnea, and recovered after sublingual nitroglycerin. The VAS and ODI improved from a mean preoperative score of 8.58±1.12 and 81.43 ±12.54 to a mean postoperative score of 3.03±0.98 and 31.04±11.11 one day afterward. Asymptomatic cement leakage rate was 17.8% with no major complications occurred during operation or post-operation. Five patients had new symptomatic vertebral fracture (s) during follow-up in one year. Conclusions: Vertebroplasty with cement to treat multi-level osteoporotic vertebral compression fractures in the elderly is safe and effective, providing immediate and long-term pain relief with improvement in quality of life. Due to the risk of fat embolism, the limitation of three per session must be kept strictly. (authors)

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

Science.gov (United States)

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

2015-10-30

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

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

OpenAIRE

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

2016-01-01

Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

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

KAUST Repository

Wu, Baoyuan

2015-12-07

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.

Predictors of Self-Directed Learning for Low-Qualified Employees: A Multi-Level Analysis

Science.gov (United States)

Raemdonck, Isabel; van der Leeden, Rien; Valcke, Martin; Segers, Mien; Thijssen, Jo

2012-01-01

Purpose: This study aims to examine which variables at the level of the individual employee and at the company level are predictors of self-directed learning in low-qualified employees. Methodology: Results were obtained from a sample of 408 low-qualified employees from 35 different companies. The companies were selected from the energy sector,…

Towards a semantic learning model fostering learning object reusability

OpenAIRE

Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami

2005-01-01

We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

Manifold regularized matrix completion for multi-label learning with ADMM.

Science.gov (United States)

Liu, Bin; Li, Yingming; Xu, Zenglin

2018-05-01

Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, the matrix completion approach has been regarded as a promising approach to transductive multi-label learning. By constructing a joint matrix comprising the feature matrix and the label matrix, the missing labels of test samples are regarded as missing values of the joint matrix. With the low-rank assumption of the constructed joint matrix, the missing labels can be recovered by minimizing its rank. Despite its success, most matrix completion based approaches ignore the smoothness assumption of unlabeled data, i.e., neighboring instances should also share a similar set of labels. Thus they may under exploit the intrinsic structures of data. In addition, the matrix completion problem can be less efficient. To this end, we propose to efficiently solve the multi-label learning problem as an enhanced matrix completion model with manifold regularization, where the graph Laplacian is used to ensure the label smoothness over it. To speed up the convergence of our model, we develop an efficient iterative algorithm, which solves the resulted nuclear norm minimization problem with the alternating direction method of multipliers (ADMM). Experiments on both synthetic and real-world data have shown the promising results of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

Science.gov (United States)

Whitford, Melinda M.

Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games

NARCIS (Netherlands)

Ternier, Stefaan; Klemke, Roland

2012-01-01

Ternier, S., & Klemke, R. (2011). ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games (Version 1.0) [Software Documentation]. Heerlen, The Netherlands: Open Universiteit in the Netherlands.

ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games

NARCIS (Netherlands)

Ternier, Stefaan; Klemke, Roland

2012-01-01

Ternier, S., & Klemke, R. (2011). ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games (Version 1.0) [Computer software]. Heerlen, The Netherlands: Open Universiteit in the Netherlands.

Deep Multi-Task Learning for Tree Genera Classification

Science.gov (United States)

Ko, C.; Kang, J.; Sohn, G.

2018-05-01

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) - Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (Lcd) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7 % to 91.0 % (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

Science.gov (United States)

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.

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

Interactive Approach for Multi-Level Multi-Objective Fractional Programming Problems with Fuzzy Parameters

Directory of Open Access Journals (Sweden)

M.S. Osman

2018-03-01

Full Text Available In this paper, an interactive approach for solving multi-level multi-objective fractional programming (ML-MOFP problems with fuzzy parameters is presented. The proposed interactive approach makes an extended work of Shi and Xia (1997. In the first phase, the numerical crisp model of the ML-MOFP problem has been developed at a confidence level without changing the fuzzy gist of the problem. Then, the linear model for the ML-MOFP problem is formulated. In the second phase, the interactive approach simplifies the linear multi-level multi-objective model by converting it into separate multi-objective programming problems. Also, each separate multi-objective programming problem of the linear model is solved by the ∊-constraint method and the concept of satisfactoriness. Finally, illustrative examples and comparisons with the previous approaches are utilized to evince the feasibility of the proposed approach.

Implementing service improvement projects within pre-registration nursing education: a multi-method case study evaluation.

Science.gov (United States)

Baillie, Lesley; Bromley, Barbara; Walker, Moira; Jones, Rebecca; Mhlanga, Fortune

2014-01-01

Preparing healthcare students for quality and service improvement is important internationally. A United Kingdom (UK) initiative aims to embed service improvement in pre-registration education. A UK university implemented service improvement teaching for all nursing students. In addition, the degree pathway students conducted service improvement projects as the basis for their dissertations. The study aimed to evaluate the implementation of service improvement projects within a pre-registration nursing curriculum. A multi-method case study was conducted, using student questionnaires, focus groups with students and academic staff, and observation of action learning sets. Questionnaire data were analysed using SPSS v19. Qualitative data were analysed using Ritchie and Spencer's (1994) Framework Approach. Students were very positive about service improvement. The degree students, who conducted service improvement projects in practice, felt more knowledgeable than advanced diploma students. Selecting the project focus was a key issue and students encountered some challenges in practice. Support for student service improvement projects came from action learning sets, placement staff, and academic staff. Service improvement projects had a positive effect on students' learning. An effective partnership between the university and partner healthcare organisations, and support for students in practice, is essential. Copyright © 2013 Elsevier Ltd. All rights reserved.

Improving urban African Americans' blood pressure control through multi-level interventions in the Achieving Blood Pressure Control Together (ACT) study: a randomized clinical trial.

Science.gov (United States)

Ephraim, Patti L; Hill-Briggs, Felicia; Roter, Debra L; Bone, Lee R; Wolff, Jennifer L; Lewis-Boyer, LaPricia; Levine, David M; Aboumatar, Hanan J; Cooper, Lisa A; Fitzpatrick, Stephanie J; Gudzune, Kimberly A; Albert, Michael C; Monroe, Dwyan; Simmons, Michelle; Hickman, Debra; Purnell, Leon; Fisher, Annette; Matens, Richard; Noronha, Gary J; Fagan, Peter J; Ramamurthi, Hema C; Ameling, Jessica M; Charlston, Jeanne; Sam, Tanyka S; Carson, Kathryn A; Wang, Nae-Yuh; Crews, Deidra C; Greer, Raquel C; Sneed, Valerie; Flynn, Sarah J; DePasquale, Nicole; Boulware, L Ebony

2014-07-01

Given their high rates of uncontrolled blood pressure, urban African Americans comprise a particularly vulnerable subgroup of persons with hypertension. Substantial evidence has demonstrated the important role of family and community support in improving patients' management of a variety of chronic illnesses. However, studies of multi-level interventions designed specifically to improve urban African American patients' blood pressure self-management by simultaneously leveraging patient, family, and community strengths are lacking. We report the protocol of the Achieving Blood Pressure Control Together (ACT) study, a randomized controlled trial designed to study the effectiveness of interventions that engage patient, family, and community-level resources to facilitate urban African American hypertensive patients' improved hypertension self-management and subsequent hypertension control. African American patients with uncontrolled hypertension receiving health care in an urban primary care clinic will be randomly assigned to receive 1) an educational intervention led by a community health worker alone, 2) the community health worker intervention plus a patient and family communication activation intervention, or 3) the community health worker intervention plus a problem-solving intervention. All participants enrolled in the study will receive and be trained to use a digital home blood pressure machine. The primary outcome of the randomized controlled trial will be patients' blood pressure control at 12months. Results from the ACT study will provide needed evidence on the effectiveness of comprehensive multi-level interventions to improve urban African American patients' hypertension control. Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-Touch Tables and Collaborative Learning

Science.gov (United States)

Higgins, Steve; Mercier, Emma; Burd, Liz; Joyce-Gibbons, Andrew

2012-01-01

The development of multi-touch tables, an emerging technology for classroom learning, offers valuable opportunities to explore how its features can be designed to support effective collaboration in schools. In this study, small groups of 10- to 11-year-old children undertook a history task where they had to connect various pieces of information…

Multi-level damage identification with response reconstruction

Science.gov (United States)

Zhang, Chao-Dong; Xu, You-Lin

2017-10-01

Damage identification through finite element (FE) model updating usually forms an inverse problem. Solving the inverse identification problem for complex civil structures is very challenging since the dimension of potential damage parameters in a complex civil structure is often very large. Aside from enormous computation efforts needed in iterative updating, the ill-condition and non-global identifiability features of the inverse problem probably hinder the realization of model updating based damage identification for large civil structures. Following a divide-and-conquer strategy, a multi-level damage identification method is proposed in this paper. The entire structure is decomposed into several manageable substructures and each substructure is further condensed as a macro element using the component mode synthesis (CMS) technique. The damage identification is performed at two levels: the first is at macro element level to locate the potentially damaged region and the second is over the suspicious substructures to further locate as well as quantify the damage severity. In each level's identification, the damage searching space over which model updating is performed is notably narrowed down, not only reducing the computation amount but also increasing the damage identifiability. Besides, the Kalman filter-based response reconstruction is performed at the second level to reconstruct the response of the suspicious substructure for exact damage quantification. Numerical studies and laboratory tests are both conducted on a simply supported overhanging steel beam for conceptual verification. The results demonstrate that the proposed multi-level damage identification via response reconstruction does improve the identification accuracy of damage localization and quantization considerably.

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Science.gov (United States)

Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

2017-01-01

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

New techniques for multi-level cross section calculation and fitting

International Nuclear Information System (INIS)

Froehner, F.H.

1980-09-01

A number of recent developments in multi-level cross section work are described. A new iteration scheme for the conversion of Reich-Moore resonance parameters to Kapur-Peierls parameters allows application of Turing's method for Gaussian broadening of meromorphic functions directly to multi-level cross section expressions, without recourse to the Voigt profiles psi and chi. This makes calculation of Doppler-broadened Reich-Moore and MLBW cross sections practically as fast as SLBW and Adler-Adler cross section calculations involving the Voigt profiles. A convenient distant-level treatment utilizing average resonance parameters is presented. Apart from effectively dealing with edge effects in resonance fitting work it also leads to a simple prescription for the determination of bound levels which reproduce the thermal cross sections correctly. A brief discussion of improved resonance shape fitting techniques is included, with empahsis on the importance of correlated errors and proper use of prior information by application of Bayes' theorem. (orig.) [de

New techniques for multi-level cross section calculation and fitting

International Nuclear Information System (INIS)

Froehner, F.H.

1981-01-01

A number of recent developments in multi-level cross section work are described. A new iteration scheme for the conversion of Reich-Moore resonance parameters to Kapur-Peierls parameters allows application of Turing's method for Gaussian broadening of meromorphic functions directly to multi-level cross section expressions, without recourse to the Voigt profiles psi and chi. This makes calculation of Doppler-broadened Reich-Moore and MLBW cross sections practically as fast as SLBW and Adler-Adler cross section calculations involving the Voigt profiles. A convenient distant-level treatment utilizing average resonance parameters is presented. Apart from effectively dealing with edge effects in resonance fitting work it also leads to a simple prescription for the determination of bound levels which reproduce the thermal cross sections correctly. A brief discussion of improved resonance shape fitting techniques is included, with emphasis on the importance of correlated errors and proper use of prior information by application of Bayes' theorem

A Cognitive Skill Classification Based On Multi Objective Optimization Using Learning Vector Quantization for Serious Games

Directory of Open Access Journals (Sweden)

Moh. Aries Syufagi

2011-12-01

Full Text Available Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG. CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments using 33 respondent players demonstrates that 61% of players have high trial and error cognitive skill, 21% have high carefully cognitive skill, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the player is emotionally stable. Interests in the players strongly support the procedural learning in a serious game.

Exploring Effects of Multi-Touch Tabletop on Collaborative Fraction Learning and the Relationship of Learning Behavior and Interaction with Learning Achievement

Science.gov (United States)

Hwang, Wu-Yuin; Shadiev, Rustam; Tseng, Chi-Wei; Huang, Yueh-Min

2015-01-01

This study designed a learning system to facilitate elementary school students' fraction learning. An experiment was carried out to investigate how the system, which runs on multi-touch tabletop versus tablet PC, affects fraction learning. Two groups, a control and experimental, were assigned. Control students have learned fraction by using tablet…

Optimal control in microgrid using multi-agent reinforcement learning.

Science.gov (United States)

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

The Moderating Effect of Health-Improving Workplace Environment on Promoting Physical Activity in White-Collar Employees: A Multi-Site Longitudinal Study Using Multi-Level Structural Equation Modeling.

Science.gov (United States)

Watanabe, Kazuhiro; Otsuka, Yasumasa; Shimazu, Akihito; Kawakami, Norito

2016-02-01

This longitudinal study aimed to investigate the moderating effect of health-improving workplace environment on relationships between physical activity, self-efficacy, and psychological distress. Data were collected from 16 worksites and 129 employees at two time-points. Health-improving workplace environment was measured using the Japanese version of the Environmental Assessment Tool. Physical activity, self-efficacy, and psychological distress were also measured. Multi-level structural equation modeling was used to investigate the moderating effect of health-improving workplace environment on relationships between psychological distress, self-efficacy, and physical activity. Psychological distress was negatively associated with physical activity via low self-efficacy. Physical activity was negatively related to psychological distress. Physical activity/fitness facilities in the work environment exaggerated the positive relationship between self-efficacy and physical activity. Physical activity/fitness facilities in the workplace may promote employees' physical activity.

Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

Science.gov (United States)

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2013-01-01

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

Directory of Open Access Journals (Sweden)

Liesje Coertjens

Full Text Available The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

A new system to reduce formaldehyde levels improves safety conditions during gross veterinary anatomy learning.

Science.gov (United States)

Nacher, Víctor; Llombart, Cristina; Carretero, Ana; Navarro, Marc; Ysern, Pere; Calero, Sebastián; Fígols, Enric; Ruberte, Jesús

2007-01-01

Dissection is a very useful method of learning veterinary anatomy. However, formaldehyde, which is widely used to preserve cadavers, is an irritant, and it has recently been classified as a carcinogen. In 1997, the Instituto Nacional de Seguridad e Higiene en el Trabajo [National Institute of Workplace Security and Hygiene] found that the levels of formaldehyde in our dissection room were above the threshold limit values. Unfortunately, no optimal substitute for formaldehyde is currently available. Therefore, we designed a new ventilation system that combines slow propulsion of fresh air from above the dissection table and rapid aspiration of polluted air from the perimeter. Formaldehyde measurements performed in 2004, after the introduction of this new system into our dissection laboratory, showed a dramatic reduction (about tenfold, or 0.03 ppm). A suitable propelling/aspirating air system successfully reduces the concentration of formaldehyde in the dissection room, significantly improving safety conditions for students, instructors, and technical staff during gross anatomy learning.

Developing Multi-Level Institutions from Top-Down Ancestors

Directory of Open Access Journals (Sweden)

Martha Dowsley

2007-11-01

Full Text Available The academic literature contains numerous examples of the failures of both top-down and bottom-up common pool resource management frameworks. Many authors agree that management regimes instead need to utilize a multi-level governance approach to meet diverse objectives in management. However, many currently operating systems do not have that history. This paper explores the conversion of ancestral top-down regimes to complex systems involving multiple scales, levels and objectives through the management of the polar bear (Ursus maritimus in its five range countries. The less successful polar bear management systems continue to struggle with the challenges of developing institutions with the capacity to learn and change, addressing multiple objectives while recognizing the conservation backbone to management, and matching the institutional scale with biophysical, economic and social scales. The comparatively successful institutions incorporate these features, but reveal on-going problems with vertical links that are partially dealt with through the creation of links to other groups.

Multi Car Elevator Control by using Learning Automaton

Science.gov (United States)

Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

Multi-Level Bitmap Indexes for Flash Memory Storage

Energy Technology Data Exchange (ETDEWEB)

Wu, Kesheng; Madduri, Kamesh; Canon, Shane

2010-07-23

Due to their low access latency, high read speed, and power-efficient operation, flash memory storage devices are rapidly emerging as an attractive alternative to traditional magnetic storage devices. However, tests show that the most efficient indexing methods are not able to take advantage of the flash memory storage devices. In this paper, we present a set of multi-level bitmap indexes that can effectively take advantage of flash storage devices. These indexing methods use coarsely binned indexes to answer queries approximately, and then use finely binned indexes to refine the answers. Our new methods read significantly lower volumes of data at the expense of an increased disk access count, thus taking full advantage of the improved read speed and low access latency of flash devices. To demonstrate the advantage of these new indexes, we measure their performance on a number of storage systems using a standard data warehousing benchmark called the Set Query Benchmark. We observe that multi-level strategies on flash drives are up to 3 times faster than traditional indexing strategies on magnetic disk drives.

Are students' impressions of improved learning through active learning methods reflected by improved test scores?

Science.gov (United States)

Everly, Marcee C

2013-02-01

To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

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

OpenAIRE

Fan, Jihong; Liang, Ru-Ze

2016-01-01

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

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

L. DJEROU,

2012-01-01

Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning

KAUST Repository

Alqerm, Ismail

2017-07-25

The multi-tier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multi-tier 5G network structure using a non-cooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.

Optimizing survivability of multi-state systems with multi-level protection by multi-processor genetic algorithm

International Nuclear Information System (INIS)

Levitin, Gregory; Dai Yuanshun; Xie Min; Leng Poh, Kim

2003-01-01

In this paper we consider vulnerable systems which can have different states corresponding to different combinations of available elements composing the system. Each state can be characterized by a performance rate, which is the quantitative measure of a system's ability to perform its task. Both the impact of external factors (stress) and internal causes (failures) affect system survivability, which is determined as probability of meeting a given demand. In order to increase the survivability of the system, a multi-level protection is applied to its subsystems. This means that a subsystem and its inner level of protection are in their turn protected by the protection of an outer level. This double-protected subsystem has its outer protection and so forth. In such systems, the protected subsystems can be destroyed only if all of the levels of their protection are destroyed. Each level of protection can be destroyed only if all of the outer levels of protection are destroyed. We formulate the problem of finding the structure of series-parallel multi-state system (including choice of system elements, choice of structure of multi-level protection and choice of protection methods) in order to achieve a desired level of system survivability by the minimal cost. An algorithm based on the universal generating function method is used for determination of the system survivability. A multi-processor version of genetic algorithm is used as optimization tool in order to solve the structure optimization problem. An application example is presented to illustrate the procedure presented in this paper

Using Hierarchical Machine Learning to Improve Player Satisfaction in a Soccer Videogame

OpenAIRE

Collins, Brian; Rovatsos, Michael

2006-01-01

This paper describes an approach to using a hierarchical machine learning model in a two player 3D physics-based soccer video game to improve human player satisfaction. Learning is accomplished at two layers to form a complete game-playing agent such that higher level strategy learning is dependent on lower-level learning of basic behaviors.Supervised learning is used to train neural networks on human data to model the basic behaviors. The reinforcement learning algorithms Sarsa (λ) and Q(λ) ...

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

Science.gov (United States)

Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

2017-01-01

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

Transfer learning improves supervised image segmentation across imaging protocols.

Science.gov (United States)

van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

2015-05-01

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Development of a prototype interactive learning system using multi-media technology for mission independent training program

Science.gov (United States)

Matson, Jack E.

1992-01-01

The Spacelab Mission Independent Training Program provides an overview of payload operations. Most of the training material is currently presented in workbook form with some lecture sessions to supplement selected topics. The goal of this project was to develop a prototype interactive learning system for one of the Mission Independent Training topics to demonstrate how the learning process can be improved by incorporating multi-media technology into an interactive system. This report documents the development process and some of the problems encountered during the analysis, design, and production phases of this system.

Ship Detection Using Transfer Learned Single Shot Multi Box Detector

Directory of Open Access Journals (Sweden)

Nie Gu-Hong

2017-01-01

Full Text Available Ship detection in satellite images is a challenging task. In this paper, we introduce a transfer learned Single Shot MultiBox Detector (SSD for ship detection. To this end, a state-of-the-art object detection model pre-trained from a large number of natural images was transfer learned for ship detection with limited labeled satellite images. To the best of our knowledge, this could be one of the first studies which introduce SSD into ship detection on satellite images. Experiments demonstrated that our method could achieve 87.9% AP at 47 FPS using NVIDIA TITAN X. In comparison with Faster R-CNN, 6.7% AP improvement could be achieved. Effects of the observation resolution has also been studied with the changing input sizes among 300 × 300, 600 × 600 and 900 × 900. It has been noted that the detection accuracy declined sharply with the decreasing resolution that is mainly caused by the missing small ships.

A multi-level surface rebalancing approach for efficient convergence acceleration of 3D full core multi-group fine grid nodal diffusion iterations

International Nuclear Information System (INIS)

Geemert, René van

2014-01-01

Highlights: • New type of multi-level rebalancing approach for nodal transport. • Generally improved and more mesh-independent convergence behavior. • Importance for intended regime of 3D pin-by-pin core computations. - Abstract: A new multi-level surface rebalancing (MLSR) approach has been developed, aimed at enabling an improved non-linear acceleration of nodal flux iteration convergence in 3D steady-state and transient reactor simulation. This development is meant specifically for anticipating computational needs for solving envisaged multi-group diffusion-like SP N calculations with enhanced mesh resolution (i.e. 3D multi-box up to 3D pin-by-pin grid). For the latter grid refinement regime, the previously available multi-level coarse mesh rebalancing (MLCMR) strategy has been observed to become increasingly inefficient with increasing 3D mesh resolution. Furthermore, for very fine 3D grids that feature a very fine axial mesh as well, non-convergence phenomena have been observed to emerge. In the verifications pursued up to now, these problems have been resolved by the new approach. The novelty arises from taking the interface current balance equations defined over all Cartesian box edges, instead of the nodal volume-integrated process-rate balance equation, as an appropriate restriction basis for setting up multi-level acceleration of fine grid interface current iterations. The new restriction strategy calls for the use of a newly derived set of adjoint spectral equations that are needed for computing a limited set of spectral response vectors per node. This enables a straightforward determination of group-condensed interface current spectral coupling operators that are of crucial relevance in the new rebalancing setup. Another novelty in the approach is a new variational method for computing the neutronic eigenvalue. Within this context, the latter is treated as a control parameter for driving another, newly defined and numerically more fundamental

The scientific learning approach using multimedia-based maze game to improve learning outcomes

Science.gov (United States)

Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

2016-02-01

The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).

Transition in governance of river basin management in The Netherlands through multi-level social learning

NARCIS (Netherlands)

Van Herk, S.; Rijke, J.S.; Zevenbergen, C.; Ashley, R.

2012-01-01

This paper presents a case study of a new adaptive, multi-level governance approach that supported a transition in river basin management in the Netherlands. The floods of 1993 and 1995 in the Netherlands triggered a paradigm shift in flood management. The 2.3 billion Euro flood safety programme

Progress and challenges in the development and qualification of multi-level multi-physics coupled methodologies for reactor analysis

International Nuclear Information System (INIS)

Ivanov, K.; Avramova, M.

2007-01-01

Current trends in nuclear power generation and regulation as well as the design of next generation reactor concepts along with the continuing computer technology progress stimulate the development, qualification and application of multi-physics multi-scale coupled code systems. The efforts have been focused on extending the analysis capabilities by coupling models, which simulate different phenomena or system components, as well as on refining the scale and level of detail of the coupling. This paper reviews the progress made in this area and outlines the remaining challenges. The discussion is illustrated with examples based on neutronics/thermohydraulics coupling in the reactor core modeling. In both fields recent advances and developments are towards more physics-based high-fidelity simulations, which require implementation of improved and flexible coupling methodologies. First, the progresses in coupling of different physics codes along with the advances in multi-level techniques for coupled code simulations are discussed. Second, the issues related to the consistent qualification of coupled multi-physics and multi-scale code systems for design and safety evaluation are presented. The increased importance of uncertainty and sensitivity analysis are discussed along with approaches to propagate the uncertainty quantification between the codes. The incoming OECD LWR Uncertainty Analysis in Modeling (UAM) benchmark is the first international activity to address this issue and it is described in the paper. Finally, the remaining challenges with multi-physics coupling are outlined. (authors)

Progress and challenges in the development and qualification of multi-level multi-physics coupled methodologies for reactor analysis

Energy Technology Data Exchange (ETDEWEB)

Ivanov, K.; Avramova, M. [Pennsylvania State Univ., University Park, PA (United States)

2007-07-01

Current trends in nuclear power generation and regulation as well as the design of next generation reactor concepts along with the continuing computer technology progress stimulate the development, qualification and application of multi-physics multi-scale coupled code systems. The efforts have been focused on extending the analysis capabilities by coupling models, which simulate different phenomena or system components, as well as on refining the scale and level of detail of the coupling. This paper reviews the progress made in this area and outlines the remaining challenges. The discussion is illustrated with examples based on neutronics/thermohydraulics coupling in the reactor core modeling. In both fields recent advances and developments are towards more physics-based high-fidelity simulations, which require implementation of improved and flexible coupling methodologies. First, the progresses in coupling of different physics codes along with the advances in multi-level techniques for coupled code simulations are discussed. Second, the issues related to the consistent qualification of coupled multi-physics and multi-scale code systems for design and safety evaluation are presented. The increased importance of uncertainty and sensitivity analysis are discussed along with approaches to propagate the uncertainty quantification between the codes. The incoming OECD LWR Uncertainty Analysis in Modeling (UAM) benchmark is the first international activity to address this issue and it is described in the paper. Finally, the remaining challenges with multi-physics coupling are outlined. (authors)

Multi-level converter with auxiliary resonant-commutated pole

NARCIS (Netherlands)

Dijkhuizen, F.R.; Duarte, J.L.; Groningen, van W.D.H.

1998-01-01

The family of multi-level power converters offers advantages for high-power, high-voltage systems. A multi-level nested-cell structure has the attractive feature of static and dynamic voltage sharing among the switches. This is achieved by using clamping capacitors (floating capacitors) rather than

Fermi level splitting and thermionic current improvement in low-dimensional multi-quantum-well (MQW) p-i-n structures

International Nuclear Information System (INIS)

Varonides, Argyrios C.

2006-01-01

Photo-excitation and subsequent thermionic currents are essential components of photo-excited carrier transport in multi-quantum-well photovoltaic (hetero-PV) structures. p-i-n multi-quantum structures are useful probes for a better understanding of PV device properties. Illumination of the intrinsic region of p-i-n multi-structures causes carrier trapping in any of the quantum wells, and subsequent carrier recombination or thermal escape is possible. At the vicinity of a quantum well, we find that the (quasi) Fermi levels undergo an upward split by a small, but non-negligible, energy amount ΔE F in the order of 12 meV. We conclude this fact by comparing the photo-excited carriers trapped in a quantum well, under illumination, to the carrier concentrations under dark. Based on such a prediction, we subsequently relate thermionic current density dependence on Fermi level splitting, concluding that excess thermal currents may increase by a factor of the order of 2. We conclude that illumination causes (a) Fermi level separation and (b) an apparent increase in thermionic currents

Promoting system-level learning from project-level lessons

Energy Technology Data Exchange (ETDEWEB)

Jong, Amos A. de, E-mail: amosdejong@gmail.com [Innovation Management, Utrecht (Netherlands); Runhaar, Hens A.C., E-mail: h.a.c.runhaar@uu.nl [Section of Environmental Governance, Utrecht University, Utrecht (Netherlands); Runhaar, Piety R., E-mail: piety.runhaar@wur.nl [Organisational Psychology and Human Resource Development, University of Twente, Enschede (Netherlands); Kolhoff, Arend J., E-mail: Akolhoff@eia.nl [The Netherlands Commission for Environmental Assessment, Utrecht (Netherlands); Driessen, Peter P.J., E-mail: p.driessen@geo.uu.nl [Department of Innovation and Environment Sciences, Utrecht University, Utrecht (Netherlands)

2012-02-15

A growing number of low and middle income nations (LMCs) have adopted some sort of system for environmental impact assessment (EIA). However, generally many of these EIA systems are characterised by a low performance in terms of timely information dissemination, monitoring and enforcement after licencing. Donor actors (such as the World Bank) have attempted to contribute to a higher performance of EIA systems in LMCs by intervening at two levels: the project level (e.g. by providing scoping advice or EIS quality review) and the system level (e.g. by advising on EIA legislation or by capacity building). The aims of these interventions are environmental protection in concrete cases and enforcing the institutionalisation of environmental protection, respectively. Learning by actors involved is an important condition for realising these aims. A relatively underexplored form of learning concerns learning at EIA system-level via project level donor interventions. This 'indirect' learning potentially results in system changes that better fit the specific context(s) and hence contribute to higher performances. Our exploratory research in Ghana and the Maldives shows that thus far, 'indirect' learning only occurs incidentally and that donors play a modest role in promoting it. Barriers to indirect learning are related to the institutional context rather than to individual characteristics. Moreover, 'indirect' learning seems to flourish best in large projects where donors achieved a position of influence that they can use to evoke reflection upon system malfunctions. In order to enhance learning at all levels donors should thereby present the outcomes of the intervention elaborately (i.e. discuss the outcomes with a large audience), include practical suggestions about post-EIS activities such as monitoring procedures and enforcement options and stimulate the use of their advisory reports to generate organisational memory and ensure a better

Promoting system-level learning from project-level lessons

International Nuclear Information System (INIS)

Jong, Amos A. de; Runhaar, Hens A.C.; Runhaar, Piety R.; Kolhoff, Arend J.; Driessen, Peter P.J.

2012-01-01

A growing number of low and middle income nations (LMCs) have adopted some sort of system for environmental impact assessment (EIA). However, generally many of these EIA systems are characterised by a low performance in terms of timely information dissemination, monitoring and enforcement after licencing. Donor actors (such as the World Bank) have attempted to contribute to a higher performance of EIA systems in LMCs by intervening at two levels: the project level (e.g. by providing scoping advice or EIS quality review) and the system level (e.g. by advising on EIA legislation or by capacity building). The aims of these interventions are environmental protection in concrete cases and enforcing the institutionalisation of environmental protection, respectively. Learning by actors involved is an important condition for realising these aims. A relatively underexplored form of learning concerns learning at EIA system-level via project level donor interventions. This ‘indirect’ learning potentially results in system changes that better fit the specific context(s) and hence contribute to higher performances. Our exploratory research in Ghana and the Maldives shows that thus far, ‘indirect’ learning only occurs incidentally and that donors play a modest role in promoting it. Barriers to indirect learning are related to the institutional context rather than to individual characteristics. Moreover, ‘indirect’ learning seems to flourish best in large projects where donors achieved a position of influence that they can use to evoke reflection upon system malfunctions. In order to enhance learning at all levels donors should thereby present the outcomes of the intervention elaborately (i.e. discuss the outcomes with a large audience), include practical suggestions about post-EIS activities such as monitoring procedures and enforcement options and stimulate the use of their advisory reports to generate organisational memory and ensure a better information

Multi-level tree analysis of pulmonary artery/vein trees in non-contrast CT images

Science.gov (United States)

Gao, Zhiyun; Grout, Randall W.; Hoffman, Eric A.; Saha, Punam K.

2012-02-01

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

Optimal Multi-Level Lot Sizing for Requirements Planning Systems

OpenAIRE

Earle Steinberg; H. Albert Napier

1980-01-01

The wide spread use of advanced information systems such as Material Requirements Planning (MRP) has significantly altered the practice of dependent demand inventory management. Recent research has focused on development of multi-level lot sizing heuristics for such systems. In this paper, we develop an optimal procedure for the multi-period, multi-product, multi-level lot sizing problem by modeling the system as a constrained generalized network with fixed charge arcs and side constraints. T...

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

KAUST Repository

Fan, Jihong

2016-09-17

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

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

Science.gov (United States)

Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

2017-09-01

Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in

On multi-level thinking and scientific understanding

Science.gov (United States)

McIntyre, Michael Edgeworth

2017-10-01

Professor Duzheng YE's name has been familiar to me ever since my postdoctoral years at MIT with Professors Jule CHARNEY and Norman PHILLIPS, back in the late 1960s. I had the enormous pleasure of meeting Professor YE personally in 1992 in Beijing. His concern to promote the very best science and to use it well, and his thinking on multi-level orderly human activities, reminds me not only of the communication skills we need as scientists but also of the multi-level nature of science itself. Here I want to say something (a) about what science is; (b) about why multi-level thinking—and taking more than one viewpoint—is so important for scientific as well as for other forms of understanding; and (c) about what is meant, at a deep level, by "scientific understanding" and trying to communicate it, not only with lay persons but also across professional disciplines. I hope that Professor YE would approve.

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Directory of Open Access Journals (Sweden)

ZALL, R.

2016-05-01

Full Text Available Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE. SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.

Interconnected levels of Multi-Stage Marketing – A Triadic approach

DEFF Research Database (Denmark)

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

must not only decide in general on the merits of multi-stage marketing for their firm, but must also decide on which level they will engage in multi-stage marketing. The triadic perspective enables a rich and multi-dimensional understanding of how different business relationships influence each other......Multi-stage marketing gains increasing attention as knowledge of and influence on the customer's customer become more critical for the firm's success. Despite this increasing managerial relevance, systematic approaches for analyzing multi-stage marketing are still missing. This paper conceptualizes...... different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. The results from the case study indicate that multi-stage marketing exists on different levels. Thus, managers...

Thermo-mechanical analysis for multi-level HLW repository concept

International Nuclear Information System (INIS)

Kwon, Sang Ki; Choi, Jong Won

2004-01-01

This work aims to investigate the influence of design parameters for the underground high-level nuclear waste repository with multi-level concept. B. Necessity o In order to construct an HLW repository in deep underground, it is required to select a site, which is far from major discontinuities. To dispose the whole spent fuels generated from the Korean nuclear power plants in a repository, the underground area of about 4km 2 is required. This would be a constraints for selecting an adequate repository site. It is recommended to dispose the two different spent fuels, PWR and CANDU, in different areas at the operation efficiency point of view. It is necessary to investigate the influence of parameters, which can affect the stability of multi-level repository. It is also needed to consider the influence of heat generated from the HLW and the high in situ stress in deep location. Therefore, thermo-mechanical coupling analysis should be carried out and the results should be compared with the results from single-level repository concept. Three-dimensional analysis is required to model the disposal tunnel and deposition hole. It is recommended to use the Korean geological condition and actually measured rock properties in Korea in order to achieve reliable modeling results. A FISH routine developed for effective modeling of Thermal-Mechanical coupling was implemented in the modeling using FLAC3D, which is a commercial three-dimensional FDM code. The thermal and mechanical properties of rock and rock mass achieved from Yusung drilling site, were used for the computer modeling. Different parameters such as level distance, waste type disposed on different levels, and time interval between the operation on different levels, were considered in the three-dimensional analysis. From the analysis, it was possible to derive adequate multi-level repository concept. Results and recommendations for application From the thermal-mechanical analysis for the multi-level repository

PELAKSANAAN JUAL BELI MELALUI SISTEM MULTI LEVEL MARKETING PERSPEKTIF HUKUM ISLAM

Directory of Open Access Journals (Sweden)

Ayu Dewi Utami

2016-03-01

Full Text Available Bisnis Multi Level Marketing (MLM cukup berperan dalam menggerakkan roda perekonomian masyarakat. Dalam sejumlah kasus, Multi Level Marketing (MLM kerap dijadikan kedok dari bisnis money game dan mendewakan passive income. Bertolak dari kasus kasus seperti itulah, Majelis Ulama Indonesia (MUI telah menggodok prinsip-prinsip bisnis ini secara syariah termasuk marketing plannya. Tujuannya untuk melindungi pengusaha dan mitra bisnisnya (masyarakat dari praktik bisnis yang haram atau syubhat. Dari prinsip-prinsip yang ditentukan oleh Majelis Ulama Indonesia (MUI, peneliti mengadakan penelitian ini dengan tujuan untuk mengetahui bagaimana mekanisme bisnis Multi Level Marketing (MLM, serta untuk mengetahui bagaimana bisnis Multi Level Marketing (MLM menurut hukum Islam. Metode yang digunakan dalam penelitian ini adalah menggunakan metode pendekatan yuridis normatif, spesifikasi penelitian yang digunakan adalah deskriptif analitis, sedangkan penentuan sampel menggunakan metode Non Random sampling. Alat penelitian meliputi studi kepustakaan dan wawancara. Metode analisis data dilakukan dengan analisis kualitatif. Ada dua aspek untuk menilai apakah bisnis Multi Level Marketing (MLM itu sesuai dengan syariah atau tidak, yaitu aspek produk atau jasa yang dijual dan sistem dari Multi Level Marketing (MLM itu sendiri. Bagaimana sistem pemberian bonus yang terdapat dalam perusahaan Multi Level Marketing (MLM apakah terbebas dari unsus garar maupun maisir. Penelitian ini bertujuan untuk mengkaji lebih dalam tentang Multi Level Marketing (MLM khususnya dalam Hukum Islam. Sisi negatif yang terdapat pada sistem Multi Level Marketing (MLM tidak mewakili keharaman secara keseluruhan terhadap bisnis yang berbasis Multi Level Marketing (MLM lainnya.

Online multi-modal robust non-negative dictionary learning for visual tracking.

Science.gov (United States)

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Improving student learning in calculus through applications

Science.gov (United States)

Young, C. Y.; Georgiopoulos, M.; Hagen, S. C.; Geiger, C. L.; Dagley-Falls, M. A.; Islas, A. L.; Ramsey, P. J.; Lancey, P. M.; Straney, R. A.; Forde, D. S.; Bradbury, E. E.

2011-07-01

Nationally only 40% of the incoming freshmen Science, Technology, Engineering and Mathematics (STEM) majors are successful in earning a STEM degree. The University of Central Florida (UCF) EXCEL programme is a National Science Foundation funded STEM Talent Expansion Programme whose goal is to increase the number of UCF STEM graduates. One of the key requirements for STEM majors is a strong foundation in Calculus. To improve student learning in calculus, the EXCEL programme developed two special courses at the freshman level called Applications of Calculus I (Apps I) and Applications of Calculus II (Apps II). Apps I and II are one-credit classes that are co-requisites for Calculus I and II. These classes are teams taught by science and engineering professors whose goal is to demonstrate to students where the calculus topics they are learning appear in upper level science and engineering classes as well as how faculty use calculus in their STEM research programmes. This article outlines the process used in producing the educational materials for the Apps I and II courses, and it also discusses the assessment results pertaining to this specific EXCEL activity. Pre- and post-tests conducted with experimental and control groups indicate significant improvement in student learning in Calculus II as a direct result of the application courses.

Learning from induced changes in opponent (re)actions in multi-agent games

NARCIS (Netherlands)

P.J. 't Hoen (Pieter Jan); S.M. Bohte (Sander); J.A. La Poutré (Han)

2005-01-01

textabstractMulti-agent learning is a growing area of research. An important topic is to formulate how an agent can learn a good policy in the face of adaptive, competitive opponents. Most research has focused on extensions of single agent learning techniques originally designed for agents in more

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

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

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

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

Science.gov (United States)

Bing, Lu; Wang, Wei

2017-01-01

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

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Science.gov (United States)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Replacing lecture with peer-led workshops improves student learning.

Science.gov (United States)

Preszler, Ralph W

2009-01-01

Peer-facilitated workshops enhanced interactivity in our introductory biology course, which led to increased student engagement and learning. A majority of students preferred attending two lectures and a workshop each week over attending three weekly lectures. In the workshops, students worked in small cooperative groups as they solved challenging problems, evaluated case studies, and participated in activities designed to improve their general learning skills. Students in the workshop version of the course scored higher on exam questions recycled from preworkshop semesters. Grades were higher over three workshop semesters in comparison with the seven preworkshop semesters. Although males and females benefited from workshops, there was a larger improvement of grades and increased retention by female students; although underrepresented minority (URM) and non-URM students benefited from workshops, there was a larger improvement of grades by URM students. As well as improving student performance and retention, the addition of interactive workshops also improved the quality of student learning: Student scores on exam questions that required higher-level thinking increased from preworkshop to workshop semesters.

Robust Online Multi-Task Learning with Correlative and Personalized Structures

KAUST Repository

Yang, Peng

2017-06-29

Multi-Task Learning (MTL) can enhance a classifier\\'s generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline setting and suffers from expensive training cost and poor scalability. To address such issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: the first one captures the low-rank common structure among tasks via a nuclear norm; the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. However, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. Experimental results on a number of real-world applications also verify the efficacy of our approaches.

Robust Online Multi-Task Learning with Correlative and Personalized Structures

KAUST Repository

Yang, Peng; Zhao, Peilin; Gao, Xin

2017-01-01

Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline setting and suffers from expensive training cost and poor scalability. To address such issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: the first one captures the low-rank common structure among tasks via a nuclear norm; the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. However, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. Experimental results on a number of real-world applications also verify the efficacy of our approaches.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

Science.gov (United States)

Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

2010-06-01

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns

Directory of Open Access Journals (Sweden)

M. H. El-Saify

2017-01-01

Full Text Available The distillation process is vital in many fields of chemical industries, such as the two-coupled distillation columns that are usually highly nonlinear Multi-Input Multi-Output (MIMO coupled processes. The control of MIMO process is usually implemented via a decentralized approach using a set of Single-Input Single-Output (SISO loop controllers. Decoupling the MIMO process into group of single loops requires proper input-output pairing and development of decoupling compensator unit. This paper proposes a novel intelligent decoupling approach for MIMO processes based on new MIMO brain emotional learning architecture. A MIMO architecture of Brain Emotional Learning Based Intelligent Controller (BELBIC is developed and applied as a decoupler for 4 input/4 output highly nonlinear coupled distillation columns process. Moreover, the performance of the proposed Brain Emotional Learning Based Intelligent Decoupler (BELBID is enhanced using Particle Swarm Optimization (PSO technique. The performance is compared with the PSO optimized steady state decoupling compensation matrix. Mathematical models of the distillation columns and the decouplers are built and tested in simulation environment by applying the same inputs. The results prove remarkable success of the BELBID in minimizing the loops interactions without degrading the output that every input has been paired with.

Multi-task learning with group information for human action recognition

Science.gov (United States)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Using a NIATx based local learning collaborative for performance improvement.

Science.gov (United States)

Roosa, Mathew; Scripa, Joseph S; Zastowny, Thomas R; Ford, James H

2011-11-01

Local governments play an important role in improving substance abuse and mental health services. The structure of the local learning collaborative requires careful attention to old relationships and challenges local governmental leaders to help move participants from a competitive to collaborative environment. This study describes one county's experience applying the NIATx process improvement model via a local learning collaborative. Local substance abuse and mental health agencies participated in two local learning collaboratives designed to improve client retention in substance abuse treatment and client access to mental health services. Results of changes implemented at the provider level on access and retention are outlined. The process of implementing evidence-based practices by using the Plan-Do-Study-Act rapid-cycle change is a powerful combination for change at the local level. Key lessons include: creating a clear plan and shared vision, recognizing that one size does not fit all, using data can help fuel participant engagement, a long collaborative may benefit from breaking it into smaller segments, and paying providers to offset costs of participation enhances their engagement. The experience gained in Onondaga County, New York, offers insights that serve as a foundation for using the local learning collaborative in other community-based organizations. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Single Rod Multi-modality Multi-interface Level Sensor Using an AC Current Source

Directory of Open Access Journals (Sweden)

Abdulgader Hwili

2008-05-01

Full Text Available Crude oil separation is an important process in the oil industry. To make efficient use of the separators, it is important to know their internal behaviour, and to measure the levels of multi-interfaces between different materials, such as gas-foam, foam-oil, oil-emulsion, emulsion-water and water-solids. A single-rod multi-modality multi-interface level sensor is presented, which has a current source, and electromagnetic modalities. Some key issues have been addressed, including the effect of salt content and temperature i.e. conductivity on the measurement.

Profile of Students’ Mental Model Change on Law Concepts Archimedes as Impact of Multi-Representation Approach

Science.gov (United States)

Taher, M.; Hamidah, I.; Suwarma, I. R.

2017-09-01

This paper outlined the results of an experimental study on the effects of multi-representation approach in learning Archimedes Law on students’ mental model improvement. The multi-representation techniques implemented in the study were verbal, pictorial, mathematical, and graphical representations. Students’ mental model was classified into three levels, i.e. scientific, synthetic, and initial levels, based on the students’ level of understanding. The present study employed the pre-experimental methodology, using one group pretest-posttest design. The subject of the study was 32 eleventh grade students in a Public Senior High School in Riau Province. The research instrument included model mental test on hydrostatic pressure concept, in the form of essay test judged by experts. The findings showed that there was positive change in students’ mental model, indicating that multi-representation approach was effective to improve students’ mental model.

[Curcumin improves learning and memory function through decreasing hippocampal TNF-α and iNOS levels after subarachnoid hemorrhage in rats].

Science.gov (United States)

Qiu, Zhenwei; Yue, Shuangzhu

2016-03-01

To investigate the effect of curcumin on learning and memory function of rats with subarachnoid hemorrhage (SAH) and the possible mechanism. A total of 30 male Sprague-Dawley rats were randomly divided into three groups: Sham group, SAH group and curcumin (Cur) therapy group. Experimental SAH rat models were established by injecting autologous blood into the cisterna magna. Neurological deficits of rats were examined at different time points. Spatial learning and memory abilities were tested by Morris water maze test. The hippocampal tumor necrosis factor-alpha (TNF-α) and inducible nitric oxide synthase (iNOS) were detected by ELISA. RESULTS Experimental SAH rat models were established successfully. Neurological scores of the SAH rats were significantly lower than those of the sham group. Curcumin therapy obviously improved the neurological deficits of rats compared with the SAH rats. Morris water maze test showed that SAH caused significant cognitive impairment with longer escape latency compared with the sham group. After treatment with curcumin for 4 weeks, the escape latency decreased significantly. The levels of TNF-α and iNOS in the curcumin-treated group were significantly lower than those of the SAH group. SAH can cause learning and memory impairment in rats. Curcumin can recover learning and memory function through down-regulating hippocampal TNF-α and iNOS levels.

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

Science.gov (United States)

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

2018-03-01

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

Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes.

Science.gov (United States)

Yu, Rongjie; Abdel-Aty, Mohamed

2013-09-01

This study presents multi-level analyses for single- and multi-vehicle crashes on a mountainous freeway. Data from a 15-mile mountainous freeway section on I-70 were investigated. Both aggregate and disaggregate models for the two crash conditions were developed. Five years of crash data were used in the aggregate investigation, while the disaggregate models utilized one year of crash data along with real-time traffic and weather data. For the aggregate analyses, safety performance functions were developed for the purpose of revealing the contributing factors for each crash type. Two methodologies, a Bayesian bivariate Poisson-lognormal model and a Bayesian hierarchical Poisson model with correlated random effects, were estimated to simultaneously analyze the two crash conditions with consideration of possible correlations. Except for the factors related to geometric characteristics, two exposure parameters (annual average daily traffic and segment length) were included. Two different sets of significant explanatory and exposure variables were identified for the single-vehicle (SV) and multi-vehicle (MV) crashes. It was found that the Bayesian bivariate Poisson-lognormal model is superior to the Bayesian hierarchical Poisson model, the former with a substantially lower DIC and more significant variables. In addition to the aggregate analyses, microscopic real-time crash risk evaluation models were developed for the two crash conditions. Multi-level Bayesian logistic regression models were estimated with the random parameters accounting for seasonal variations, crash-unit-level diversity and segment-level random effects capturing unobserved heterogeneity caused by the geometric characteristics. The model results indicate that the effects of the selected variables on crash occurrence vary across seasons and crash units; and that geometric characteristic variables contribute to the segment variations: the more unobserved heterogeneity have been accounted, the better

Quantum state preparation using multi-level-atom optics

International Nuclear Information System (INIS)

Busch, Th; Deasy, K; Chormaic, S Nic

2007-01-01

One of the most important characteristics for controlling processes on the quantum scale is the fidelity or robustness of the techniques being used. In the case of single atoms localized in micro-traps, it was recently shown that the use of time-dependent tunnelling interactions in a multi-trap setup can be viewed as analogous to the area of multi-level optics. The atom's centre-of-mass can then be controlled with a high fidelity, using a STIRAP-type process. Here, we review previous work that led to the development of multi-level atom optics and present two examples of our most recent work on quantum state preparation

Multi-Level Marketing as a business model

Directory of Open Access Journals (Sweden)

Bogdan Gregor

2013-03-01

Full Text Available Multi Level Marketing is a very popular business model in the Western countries. It is a kind of hybrid of the method of distribution of goods and the method of building a sales network. It is one of the safest (carries a very low risk ways of conducting a business activity. The knowledge about functioning of this business model, both among theoreticians (scanty literature on the subject and practitioners, is still insufficient in Poland. Thus, the presented paper has been prepared as — in the Authors' opinion — it, at least infinitesimally, bridges the gap in the recognition of Multi Level Marketing issues. The aim of the study was, first of all, to describe Multi Level Marketing, to indicate practical benefits of this business model as well as to present basic systems of calculating a commission, which are used in marketing plans of companies. The discussion was based on the study of literature and the knowledge gained in the course of free-form interviews with the leaders of the sector.

Deep learning architectures for multi-label classification of intelligent health risk prediction.

Science.gov (United States)

Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

2017-12-28

Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

Mathematical model comparing of the multi-level economics systems

Science.gov (United States)

Brykalov, S. M.; Kryanev, A. V.

2017-12-01

The mathematical model (scheme) of a multi-level comparison of the economic system, characterized by the system of indices, is worked out. In the mathematical model of the multi-level comparison of the economic systems, the indicators of peer review and forecasting of the economic system under consideration can be used. The model can take into account the uncertainty in the estimated values of the parameters or expert estimations. The model uses the multi-criteria approach based on the Pareto solutions.

Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data.

Science.gov (United States)

Wang, Pu; Ge, Ruiquan; Xiao, Xuan; Cai, Yunpeng; Wang, Guoqing; Zhou, Fengfeng

2017-09-01

Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially when at the late stages, may have more than one disease and require a multi-label diagnosis. The multi-label data mining is much more difficult than a single-label one, and very few algorithms have been developed for this situation. Deep learning is a data mining algorithm with highly dense inner structure and has achieved many successful applications in the other areas. We propose a hypothesis that rectified-linear-unit-based deep learning algorithm may also be good at the clinical questions, by revising the last layer as a multi-label output. The proof-of-concept experimental data support the hypothesis, and the community may be interested in trying more applications.

Kernel learning at the first level of inference.

Science.gov (United States)

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Storytelling in the digital world: achieving higher-level learning objectives.

Science.gov (United States)

Schwartz, Melissa R

2012-01-01

Nursing students are not passive media consumers but instead live in a technology ecosystem where digital is the language they speak. To prepare the next generation of nurses, educators must incorporate multiple technologies to improve higher-order learning. The author discusses the evolution and use of storytelling as part of the digital world and how digital stories can be aligned with Bloom's Taxonomy so that students achieve higher-level learning objectives.

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

OpenAIRE

Tang, Yuan

2016-01-01

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

Multi-Level Formation of Complex Software Systems

Directory of Open Access Journals (Sweden)

Hui Li

2016-05-01

Full Text Available We present a multi-level formation model for complex software systems. The previous works extract the software systems to software networks for further studies, but usually investigate the software networks at the class level. In contrast to these works, our treatment of software systems as multi-level networks is more realistic. In particular, the software networks are organized by three levels of granularity, which represents the modularity and hierarchy in the formation process of real-world software systems. More importantly, simulations based on this model have generated more realistic structural properties of software networks, such as power-law, clustering and modularization. On the basis of this model, how the structure of software systems effects software design principles is then explored, and it could be helpful for understanding software evolution and software engineering practices.

Recalibrating Baseline Evidence in Burundi, Malawi, Senegal and Uganda: Exploring the Potential of Multi-Site, National-Level Stakeholder Engagement in Participatory Evaluation

Science.gov (United States)

Edge, Karen; Marphatia, Akanksha A.

2015-01-01

This paper details our collaborative work on the Improving Learning Outcomes in Primary Schools (ILOPS) project in Burundi, Malawi, Uganda and Senegal. ILOPS set out to establish an innovative template for multi-stakeholder, multinational participatory evaluation (PE) and examine the fundamental roles, relationships and evidence that underpin the…

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

Science.gov (United States)

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

2018-01-01

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

Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

Science.gov (United States)

Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

2016-01-01

This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

Keabsahan Dan Kekuatan Hukum Layanan Multi Level Marketing Di Kota Manado

OpenAIRE

Mandang, Christian Leonardo

2016-01-01

Tujuan dilakukannya penelitian ini adalah untuk mengetahui apa landasan hukum yang mendasari keabsahan layanan Multi Level Marketing dan bagaimana sebuah Perusahaan dapat memenuhi syarat untuk menjalankan sistem Multi Level Marketing. Dengan menggunakan metode penelitian yuridis normatif, maka dapat disimpulkan: 1. Kehadiran Perusahaan dan kegiatan USAha Multi Level Marketing baik secara global maupun secara nasional, khususnya kehadirannya di negara Indonesia berperan untuk membantu berbagai...

Sustainability of healthcare improvement: what can we learn from learning theory?

Directory of Open Access Journals (Sweden)

Hovlid Einar

2012-08-01

Full Text Available Abstract Background Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Methods Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Results Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. Conclusions The improved understanding of

Sustainability of healthcare improvement: what can we learn from learning theory?

Science.gov (United States)

Hovlid, Einar; Bukve, Oddbjørn; Haug, Kjell; Aslaksen, Aslak Bjarne; von Plessen, Christian

2012-08-03

Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. The improved understanding of the clinical system represented a change in mental models of

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

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Badri Padhukasahasram

Full Text Available Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

Interconnected levels of multi-stage marketing: A triadic approach

OpenAIRE

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

Multi-stage marketing gains increasing attention as knowledge of and influence on the customer's customer become more critical for the firm's success. Despite this increasing managerial relevance, systematic approaches for analyzing multi-stage marketing are still missing. This paper conceptualizes different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. ...

Improving education under work-hour restrictions: comparing learning and teaching preferences of faculty, residents, and students.

Science.gov (United States)

Jack, Megan C; Kenkare, Sonya B; Saville, Benjamin R; Beidler, Stephanie K; Saba, Sam C; West, Alisha N; Hanemann, Michael S; van Aalst, John A

2010-01-01

Faced with work-hour restrictions, educators are mandated to improve the efficiency of resident and medical student education. Few studies have assessed learning styles in medicine; none have compared teaching and learning preferences. Validated tools exist to study these deficiencies. Kolb describes 4 learning styles: converging (practical), diverging (imaginative), assimilating (inductive), and accommodating (active). Grasha Teaching Styles are categorized into "clusters": 1 (teacher-centered, knowledge acquisition), 2 (teacher-centered, role modeling), 3 (student-centered, problem-solving), and 4 (student-centered, facilitative). Kolb's Learning Style Inventory (HayGroup, Philadelphia, Pennsylvania) and Grasha-Riechmann's TSS were administered to surgical faculty (n = 61), residents (n = 96), and medical students (n = 183) at a tertiary academic medical center, after informed consent was obtained (IRB # 06-0612). Statistical analysis was performed using χ(2) and Fisher exact tests. Surgical residents preferred active learning (p = 0.053), whereas faculty preferred reflective learning (p style more often than surgical faculty (p = 0.01). Medical students preferred converging learning (42%) and cluster 4 teaching (35%). Statistical significance was unchanged when corrected for gender, resident training level, and subspecialization. Significant differences exist between faculty and residents in both learning and teaching preferences; this finding suggests inefficiency in resident education, as previous research suggests that learning styles parallel teaching styles. Absence of a predominant teaching style in residents suggests these individuals are learning to be teachers. The adaptation of faculty teaching methods to account for variations in resident learning styles may promote a better learning environment and more efficient faculty-resident interaction. Additional, multi-institutional studies using these tools are needed to elucidate these findings fully

Digital case-based learning system in school.

Science.gov (United States)

Gu, Peipei; Guo, Jiayang

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Digital case-based learning system in school.

Directory of Open Access Journals (Sweden)

Peipei Gu

Full Text Available With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Device-Level Models Using Multi-Valley Effective Mass

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Baczewski, Andrew D.; Frees, Adam; Gamble, John King; Gao, Xujiao; Jacobson, N. Tobias; Mitchell, John A.; Montaño, Inès; Muller, Richard P.; Nielsen, Erik

2015-03-01

Continued progress in quantum electronics depends critically on the availability of robust device-level modeling tools that capture a wide range of physics and effective mass theory (EMT) is one means of building such models. Recent developments in multi-valley EMT show quantitative agreement with more detailed atomistic tight-binding calculations of phosphorus donors in silicon (Gamble, et. al., arXiv:1408.3159). Leveraging existing PDE solvers, we are developing a framework in which this multi-valley EMT is coupled to an integrated device-level description of several experimentally active qubit technologies. Device-level simulations of quantum operations will be discussed, as well as the extraction of process matrices at this level of theory. The authors gratefully acknowledge support from the Sandia National Laboratories Truman Fellowship Program, which is funded by the Laboratory Directed Research and Development (LDRD) Program. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Security Administration under contract DE-AC04-94AL85000.

An augmented Lagrangian multi-scale dictionary learning algorithm

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Ye Meng

2011-01-01

Full Text Available Abstract Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL, which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.

Education Isn’t Education: The Creativity Response or How to Improve the Learning Curve in Our Society

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Stefan Brunnhuber

2017-06-01

Full Text Available Despite rising expenditure and general enrolment rates on a global level, educational output is stagnating, if not declining. There is increasing empirical evidence that we need a completely different approach to enhancing the learning curve; this holds true for early childhood, primary education, secondary education and higher education. Most existing educational programs do not tap into the full creative potential of our minds and our brains and often lead to suboptimal outcomes both for the individual and for society as a whole. Findings in clinical psychology, neurobiology and social psychology are not sufficiently considered when setting up appropriate educational programs. It is not the cognitive part of the curriculum that makes a difference, but rather the non-cognitive features (including stress management, impulse control, self-regulation, emotional attachment etc. that improve creativity. A ‘six-pack’ of features, including exercise, nutrition, social contact, mindfulness-based practices, sleeping well, and multi-sensory learning, is introduced as part of a ‘creativity response’. They are simple, affordable, evidence-based and efficient strategies that can be implemented promptly without additional costs, increasing our learning curve.

Multi-Unit Considerations for Human Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

St. Germain, S.; Boring, R.; Banaseanu, G.; Akl, Y.; Chatri, H.

2017-03-01

This paper uses the insights from the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) methodology to help identify human actions currently modeled in the single unit PSA that may need to be modified to account for additional challenges imposed by a multi-unit accident as well as identify possible new human actions that might be modeled to more accurately characterize multi-unit risk. In identifying these potential human action impacts, the use of the SPAR-H strategy to include both errors in diagnosis and errors in action is considered as well as identifying characteristics of a multi-unit accident scenario that may impact the selection of the performance shaping factors (PSFs) used in SPAR-H. The lessons learned from the Fukushima Daiichi reactor accident will be addressed to further help identify areas where improved modeling may be required. While these multi-unit impacts may require modifications to a Level 1 PSA model, it is expected to have much more importance for Level 2 modeling. There is little currently written specifically about multi-unit HRA issues. A review of related published research will be presented. While this paper cannot answer all issues related to multi-unit HRA, it will hopefully serve as a starting point to generate discussion and spark additional ideas towards the proper treatment of HRA in a multi-unit PSA.

The impact of multi-criteria performance measurement on business performance improvement

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Fentahun Moges Kasie

2013-06-01

Full Text Available Purpose: The purpose of this paper is to investigate the relationship between multi-criteria performance measurement (MCPM practice and business performance improvement using the raw data collected from 33 selected manufacturing companies. In addition, it proposes modified MCPM model as an effective approach to improve business performance of manufacturing companies. Design/methodology/approach:Research paper. Primary and secondary data were collected using questionnaire survey, interview and observation of records. The methodology is to evaluate business performances of sampled manufacturing companies and the extent of utilization of crucial non-financial (lagging and non-financial (leading performance measures. The positive correlation between financial business performance and practice of MCPM is clearly shown using Pearson’s correlation coefficient analysis. Findings –This research paper indicates that companies which measure their performance using important financial and non-financial measures achieve better business performance. Even though certain companies are currently using non-financial measures, the researchers have learned that these financial measures were not integrated with each other, financial measures and strategic objectives. Research limitations/implications: The limitation of this paper is that the number of surveyed companies is small to make generalization and they are found in a single country. Further researches which incorporate a large number of companies from various developing nations are suggested to minimize the limitation of this research.Practical Implication: The paper shows that multi-dimensional performance measures with the inclusion of key leading indicator are essential to predict the future environment. But cost-accounting based financial measures are inadequate to do so. These are shown practically using Pearson’s correlation coefficient analysis. Originality/value: The significance of multi

Pracovní motivace v multi-level marketingu

OpenAIRE

Mrázková, Tereza

2015-01-01

The purpose of this work is to analyse the motivation in multi-level marketing company. The thesis introduces basic general marketing tools, but also multi-level marketing and the theory of motivation. The research in practical part was done in the form of electronical survey, which was completed by 71 responders. The responders were employees of specific company. The thesis does not only focus on the motivation in work in general, but also on the difference in motivation between male and fem...

Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

KAUST Repository

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

2018-01-01

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

Improving Multi-Agent Systems Using Jason

DEFF Research Database (Denmark)

Vester, Steen; Boss, Niklas Skamriis; Jensen, Andreas Schmidt

2011-01-01

We describe the approach used to develop the multi-agent system of herders that competed as the Jason-DTU team at the Multi-Agent Programming Contest 2010. We also participated in 2009 with a system developed in the agentoriented programming language Jason which is an extension of AgentSpeak. We ...... used the implementation from 2009 as a foundation and therefore much of the work done this year was on improving that implementation. We present a description which includes design and analysis of the system as well as the main features of our agent team strategy. In addition we discuss...

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Not just another multi-professional course! Part 2: nuts and bolts of designing a transformed curriculum for multi-professional learning.

Science.gov (United States)

Mayers, Pat; Alperstein, Melanie; Duncan, Madeleine; Olckers, Lorna; Gibbs, Trevor

2006-03-01

Multi-professional education has traditionally aimed to develop health professionals who are able to collaborate effectively in comprehensive healthcare delivery. The respective professions learn about their differences in order to work together, rather than developing unity in their commitment to a shared vision of professionalism and service. In this, the second of two papers, the 'nuts and bolts' or practicalities of designing a transformed curriculum for a multi-professional course with a difference is described. Guidelines for the curriculum design process, which seeks to be innovative, grounded in theory and relevant to the learning of the students and the ultimately the health of the patients, include: valuing education; gaining buy-in; securing buy-out; defining of roles; seeking consensus; negotiating difference and expediting decisions. The phases of the design process are described, as well as the educational outcomes envisaged during the process. Reflections of the designers, in particular on what it means to be a multi-professional team, and a reconceptualization of multi-professional education are presented as challenges for educators of health professionals.

Multi-level deep supervised networks for retinal vessel segmentation.

Science.gov (United States)

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Evolution of learning and levels of selection: a lesson from avian parent-offspring communication.

Science.gov (United States)

Lotem, Arnon; Biran-Yoeli, Inbar

2014-02-01

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

Directory of Open Access Journals (Sweden)

Niangang Jiao

2018-05-01

Full Text Available With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs. Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.

Engagement and learning: an exploratory study of situated practice in multi-disciplinary stroke rehabilitation.

Science.gov (United States)

Horton, Simon; Howell, Alison; Humby, Kate; Ross, Alexandra

2011-01-01

Active participation is considered to be a key factor in stroke rehabilitation. Patient engagement in learning is an important part of this process. This study sets out to explore how active participation and engagement are 'produced' in the course of day-to-day multi-disciplinary stroke rehabilitation. Ethnographic observation, analytic concepts drawn from discourse analysis (DA) and the perspective and methods of conversation analysis (CA) were applied to videotaped data from three sessions of rehabilitation therapy each for two patients with communication impairments (dysarthria, aphasia). Engagement was facilitated (and hindered) through the interactional work of patients and healthcare professionals. An institutional ethos of 'right practice' was evidenced in the working practices of therapists and aligned with or resisted by patients; therapeutic activity type (impairment, activity or functional focus) impacted on the ways in which patient engagement was developed and sustained. This exploration of multi-disciplinary rehabilitation practice adds a new dimension to our understanding of the barriers and facilitators to patient engagement in the learning process and provides scope for further research. Harmonising the rehabilitation process across disciplines through more focused attention to ways in which patient participation is enhanced may help improve the consistency and quality of patient engagement.

ADVANTAGES, DISADVANTAGES, AND LESSONS LEARNED FROM MULTI-REACTOR DECOMMISSIONING PROJECTS

International Nuclear Information System (INIS)

Morton, M.R.; Nielson, R.R.; Trevino, R.A.

2003-01-01

This paper discusses the Reactor Interim Safe Storage (ISS) Project within the decommissioning projects at the Hanford Site and reviews the lessons learned from performing four large reactor decommissioning projects sequentially. The advantages and disadvantages of this multi-reactor decommissioning project are highlighted

Improving the Accuracy of Cloud Detection Using Machine Learning

Science.gov (United States)

Craddock, M. E.; Alliss, R. J.; Mason, M.

2017-12-01

Cloud detection from geostationary satellite imagery has long been accomplished through multi-spectral channel differencing in comparison to the Earth's surface. The distinction of clear/cloud is then determined by comparing these differences to empirical thresholds. Using this methodology, the probability of detecting clouds exceeds 90% but performance varies seasonally, regionally and temporally. The Cloud Mask Generator (CMG) database developed under this effort, consists of 20 years of 4 km, 15minute clear/cloud images based on GOES data over CONUS and Hawaii. The algorithms to determine cloudy pixels in the imagery are based on well-known multi-spectral techniques and defined thresholds. These thresholds were produced by manually studying thousands of images and thousands of man-hours to determine the success and failure of the algorithms to fine tune the thresholds. This study aims to investigate the potential of improving cloud detection by using Random Forest (RF) ensemble classification. RF is the ideal methodology to employ for cloud detection as it runs efficiently on large datasets, is robust to outliers and noise and is able to deal with highly correlated predictors, such as multi-spectral satellite imagery. The RF code was developed using Python in about 4 weeks. The region of focus selected was Hawaii and includes the use of visible and infrared imagery, topography and multi-spectral image products as predictors. The development of the cloud detection technique is realized in three steps. First, tuning of the RF models is completed to identify the optimal values of the number of trees and number of predictors to employ for both day and night scenes. Second, the RF models are trained using the optimal number of trees and a select number of random predictors identified during the tuning phase. Lastly, the model is used to predict clouds for an independent time period than used during training and compared to truth, the CMG cloud mask. Initial results

Nonlocal atlas-guided multi-channel forest learning for human brain labeling.

Science.gov (United States)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-02-01

It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient

Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine

Science.gov (United States)

Farda, N. M.

2017-12-01

Coastal wetlands provide ecosystem services essential to people and the environment. Changes in coastal wetlands, especially on land use, are important to monitor by utilizing multi-temporal imagery. The Google Earth Engine (GEE) provides many machine learning algorithms (10 algorithms) that are very useful for extracting land use from imagery. The research objective is to explore machine learning in Google Earth Engine and its accuracy for multi-temporal land use mapping of coastal wetland area. Landsat 3 MSS (1978), Landsat 5 TM (1991), Landsat 7 ETM+ (2001), and Landsat 8 OLI (2014) images located in Segara Anakan lagoon are selected to represent multi temporal images. The input for machine learning are visible and near infrared bands, PCA band, invers PCA bands, bare soil index, vegetation index, wetness index, elevation from ASTER GDEM, and GLCM (Harralick) texture, and also polygon samples in 140 locations. There are 10 machine learning algorithms applied to extract coastal wetlands land use from Landsat imagery. The algorithms are Fast Naive Bayes, CART (Classification and Regression Tree), Random Forests, GMO Max Entropy, Perceptron (Multi Class Perceptron), Winnow, Voting SVM, Margin SVM, Pegasos (Primal Estimated sub-GrAdient SOlver for Svm), IKPamir (Intersection Kernel Passive Aggressive Method for Information Retrieval, SVM). Machine learning in Google Earth Engine are very helpful in multi-temporal land use mapping, the highest accuracy for land use mapping of coastal wetland is CART with 96.98 % Overall Accuracy using K-Fold Cross Validation (K = 10). GEE is particularly useful for multi-temporal land use mapping with ready used image and classification algorithms, and also very challenging for other applications.

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Science.gov (United States)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

Multi criteria wrapper improvements to naive bayes learning

OpenAIRE

Cortizo Pérez, José Carlos; Giráldez Betrón, Juan Ignacio

2006-01-01

Feature subset selection using a wrapper means to perform a search for an optimal set of attributes using the Machine Learning Algorithm as a black box. The Naive Bayes Classifier is based on the assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease when the attributes are interdependent. We present FBL, a wrapper that uses information about dependencies to guide the search for the optimal subset of features and we us...

Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

Directory of Open Access Journals (Sweden)

Qiaoning Yang

2015-10-01

Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

Three essays on multi-level optimization models and applications

Science.gov (United States)

Rahdar, Mohammad

The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation

Crocin Improved Learning and Memory Impairments in Streptozotocin-Induced Diabetic Rats

Directory of Open Access Journals (Sweden)

Esmaeal Tamaddonfard

2013-01-01

Full Text Available Objective(s: Crocin influences many biological functions including memory and learning. The present study was aimed to investigate the effects of crocin on learning and memory impairments in streptozotocine-induced diabetic rats. Materials and Methods: Diabetes was induced by intraperitoneal (IP injection of streptozotocin (STZ, 45 mg/kg. Transfer latency (TL paradigm in elevated plus-maze (EPM was used as an index of learning and memory. Plasma levels of total antioxidant capacity (TAC and malondialdehyde (MDA, blood levels of glucose, and serum concentrations of insulin were measured. The number of hippocampal neurons was also counted. Results: STZ increased acquisition transfer latency (TL1 and retention transfer latency (TL2, and MDA, decreased transfer latency shortening (TLs and TCA, produced hyperglycemia and hypoinsulinemia, and reduced the number of neurons in the hippocampus. Learning and memory impairments and blood TCA, MDA, glucose, and insulin changes induced by streptozotocin were improved with long-term IP injection of crocin at doses of 15 and 30 mg/kg. Crocin prevented hippocampal neurons number loss in diabetic rats. Conclusion: The results indicate that oxidative stress, hyperglycemia, hypoinsulinemia, and reduction of hippocampal neurons may be involved in learning and memory impairments in STZ-induced diabetic rats. Antioxidant, antihyperglycemic, antihypoinsulinemic, and neuroprotective activities of crocin might be involved in improving learning and memory impairments.

Teaching Strategies to Improve Algebra Learning

Science.gov (United States)

Zbiek, Rose Mary; Larson, Matthew R.

2015-01-01

Improving student learning is the primary goal of every teacher of algebra. Teachers seek strategies to help all students learn important algebra content and develop mathematical practices. The new Institute of Education Sciences[IES] practice guide, "Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students"…

Multi-level iteration optimization for diffusive critical calculation

International Nuclear Information System (INIS)

Li Yunzhao; Wu Hongchun; Cao Liangzhi; Zheng Youqi

2013-01-01

In nuclear reactor core neutron diffusion calculation, there are usually at least three levels of iterations, namely the fission source iteration, the multi-group scattering source iteration and the within-group iteration. Unnecessary calculations occur if the inner iterations are converged extremely tight. But the convergence of the outer iteration may be affected if the inner ones are converged insufficiently tight. Thus, a common scheme suit for most of the problems was proposed in this work to automatically find the optimized settings. The basic idea is to optimize the relative error tolerance of the inner iteration based on the corresponding convergence rate of the outer iteration. Numerical results of a typical thermal neutron reactor core problem and a fast neutron reactor core problem demonstrate the effectiveness of this algorithm in the variational nodal method code NODAL with the Gauss-Seidel left preconditioned multi-group GMRES algorithm. The multi-level iteration optimization scheme reduces the number of multi-group and within-group iterations respectively by a factor of about 1-2 and 5-21. (authors)

STATE LEVEL MECHANISMS FOR LEARNING FROM WHISTLEBLOWING CASES AT INSTITUTIONS OF HIGHER EDUCATION IN THE UNITED STATES

Directory of Open Access Journals (Sweden)

Christopher R. Schmidt

2016-06-01

Full Text Available State level mechanisms for soliciting, validating, and learning from whistleblower claims of fraud, theft, or misconduct against public colleges and universities are explored in four US states: California, Massachusetts, Michigan, and Ohio. Sequential public information requests were used to understand the methods that were used in each state, the types of claims that each state experienced, and to understand their processes for learning from such claims. The types of claims, breadth of scope that the claims span, and disposition of the claims is used to characterize each state’s approach and compare and contrast results with other states in the sample. There was a wide variation in responses and approaches used in each state. Varying from no information solicited or maintained (Michigan to full histories that include case level detail (Ohio, excellent multi-year case tracking and reporting (California to the voluminous tracking of every property loss or damage in every institution (Massachusetts. An organic rubric is developed and used to compare and contrast the responses and service level provided by each of the states. Although anonymous whistleblower claims are essential to the governance and administration of higher education, state level mechanisms vary widely in their approaches to administering this process and ensuring better future outcomes. Establishing a standard based upon best practices would ensure that institutions are making the best use of all information available to them to improve their immunity from employee fraud and theft and misconduct.

Leveraging Competency Framework to Improve Teaching and Learning: A Methodological Approach

Science.gov (United States)

Shankararaman, Venky; Ducrot, Joelle

2016-01-01

A number of engineering education programs have defined learning outcomes and course-level competencies, and conducted assessments at the program level to determine areas for continuous improvement. However, many of these programs have not implemented a comprehensive competency framework to support the actual delivery and assessment of an…

Creatures of habit (and control: a multi-level learning perspective on the modulation of congruency effects

Directory of Open Access Journals (Sweden)

Tobias eEgner

2014-11-01

Full Text Available The congruency sequence effect (CSE describes the finding that congruency effects in classic probes of selective attention (like the Stroop, Simon, and flanker tasks are smaller following an incongruent than following a congruent trial. The past two decades have generated a large literature on determinants and boundary conditions for the CSE and similar, congruency-proportion based modulations of congruency effects. A prolonged and heated theoretical discussion has been guided primarily by a historically motivated dichotomy between top-down control versus associative bottom-up explanations for these effects. In the present article, I attempt to integrate and contextualize the major empirical findings in this field by arguing that CSEs (and related effects are best understood as reflecting a composite of multiple levels of learning that differ in their level of abstraction. Specifically, learning does not only involve the trial-by-trial encoding, binding, and cued retrieval of specific stimulus-response associations, but also of more abstract trial features, including the spatial and temporal context in which a stimulus occurs, as well as internal states, like the experience of difficulty, and the attentional control settings that were employed in dealing with the stimulus. From this perspective, top-down control and bottom-up priming processes work in concert rather than in opposition. They represent different levels of abstraction in the same learning scheme and they serve a single, common goal: forming memory ensembles that will facilitate fast and appropriate responding to recurring stimuli or events in the environment.

Position-aware deep multi-task learning for drug-drug interaction extraction.

Science.gov (United States)

Zhou, Deyu; Miao, Lei; He, Yulan

2018-05-01

A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-level methods and approximating distribution functions

International Nuclear Information System (INIS)

Wilson, D.; Baker, R. E.

2016-01-01

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparable to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.

Multi-level methods and approximating distribution functions

Energy Technology Data Exchange (ETDEWEB)

Wilson, D., E-mail: daniel.wilson@dtc.ox.ac.uk; Baker, R. E. [Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom)

2016-07-15

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparable to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.

Improving Flood Plain Management through Adaptive Learning ...

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

This project will explore how an adaptive learning approach can improve CBO governance ... for improving resource sustainability and productivity, and facilitate learning and an exchange ... Middlesex University Higher Education Corporation.

A single-phase multi-level D-STATCOM inverter using modular multi-level converter (MMC) topology for renewable energy sources

Science.gov (United States)

Sotoodeh, Pedram

This dissertation presents the design of a novel multi-level inverter with FACTS capability for small to mid-size (10-20kW) permanent-magnet wind installations using modular multi-level converter (MMC) topology. The aim of the work is to design a new type of inverter with D-STATCOM option to provide utilities with more control on active and reactive power transfer of distribution lines. The inverter is placed between the renewable energy source, specifically a wind turbine, and the distribution grid in order to fix the power factor of the grid at a target value, regardless of wind speed, by regulating active and reactive power required by the grid. The inverter is capable of controlling active and reactive power by controlling the phase angle and modulation index, respectively. The unique contribution of the proposed work is to combine the two concepts of inverter and D-STATCOM using a novel voltage source converter (VSC) multi-level topology in a single unit without additional cost. Simulations of the proposed inverter, with 5 and 11 levels, have been conducted in MATLAB/Simulink for two systems including 20 kW/kVAR and 250 W/VAR. To validate the simulation results, a scaled version (250 kW/kVAR) of the proposed inverter with 5 and 11 levels has been built and tested in the laboratory. Experimental results show that the reduced-scale 5- and 11-level inverter is able to fix PF of the grid as well as being compatible with IEEE standards. Furthermore, total cost of the prototype models, which is one of the major objectives of this research, is comparable with market prices.

On decoding of multi-level MPSK modulation codes

Science.gov (United States)

Lin, Shu; Gupta, Alok Kumar

1990-01-01

The decoding problem of multi-level block modulation codes is investigated. The hardware design of soft-decision Viterbi decoder for some short length 8-PSK block modulation codes is presented. An effective way to reduce the hardware complexity of the decoder by reducing the branch metric and path metric, using a non-uniform floating-point to integer mapping scheme, is proposed and discussed. The simulation results of the design are presented. The multi-stage decoding (MSD) of multi-level modulation codes is also investigated. The cases of soft-decision and hard-decision MSD are considered and their performance are evaluated for several codes of different lengths and different minimum squared Euclidean distances. It is shown that the soft-decision MSD reduces the decoding complexity drastically and it is suboptimum. The hard-decision MSD further simplifies the decoding while still maintaining a reasonable coding gain over the uncoded system, if the component codes are chosen properly. Finally, some basic 3-level 8-PSK modulation codes using BCH codes as component codes are constructed and their coding gains are found for hard decision multistage decoding.

Multi-level governance in EU climate law

NARCIS (Netherlands)

Vedder, Hans; Woerdman, Edwin; Roggenkamp, Martha; Holwerda, Marijn

2015-01-01

This chapter analyses the multi-level governance in EU climate law; it connects the international arena, with EU and national decision-making and relates climate change considerations to competitiveness concerns.

The multi-level perspective analysis: Indonesia geothermal energy transition study

Science.gov (United States)

Wisaksono, A.; Murphy, J.; Sharp, J. H.; Younger, P. L.

2018-01-01

The study adopts a multi-level perspective in technology transition to analyse how the transition process in the development of geothermal energy in Indonesia is able to compete against the incumbent fossil-fuelled energy sources. Three levels of multi-level perspective are socio-technical landscape (ST-landscape), socio-technical regime (ST-regime) and niche innovations in Indonesia geothermal development. The identification, mapping and analysis of the dynamic relationship between each level are the important pillars of the multi-level perspective framework. The analysis considers the set of rules, actors and controversies that may arise in the technological transition process. The identified geothermal resource risks are the basis of the emerging geothermal technological innovations in Indonesian geothermal. The analysis of this study reveals the transition pathway, which yields a forecast for the Indonesian geothermal technology transition in the form of scenarios and probable impacts.

Experimental researches and comparison on aerodynamic parameters and cleaning efficiency of multi-level multi-channel cyclone

Directory of Open Access Journals (Sweden)

Aleksandras Chlebnikovas

2015-10-01

Full Text Available Multi-level multi-channel cyclone – the lately designed air cleaning device that can remove ultra-fine 20 μm particulatematter (PM from dusted air and reach over 95% of the overall cleaning efficiency. Multi-channel cyclone technology is based on centrifugal forces and has the resulting additional filtering process operation. Multi-level structure of cyclone allows to achieve higher air flow cleaning capacity at the same dimensions of the device, thus saving installation space required for the job, production and operating costs. Studies have examined the air flow parameters change in one–, two– and three–levels multichannel cyclone. These constructions differ according to the productivity of cleaned air under the constant peripheral and transitional (50/50 case air flow relations. Accordance with the results of air flow dynamics – velocity distribution of multi-channel cyclone, aerodynamic resistance and efficiency can be judged on the flow turbulence, the flow channel cross-section and select the most appropriate application. Cleaning efficiency studies were carried out using fine granite and wood ashes PM. The maximum cleaning efficiency was 93.3%, at an average of 4.5 g/m3, the aerodynamic resistance was equal to 1525 Pa.

ENVIRONMENTAL LEARNING APPROACHES IN IMPROVING LEARNING OUTCOMES IN ACID-BASE SUBJECT

Directory of Open Access Journals (Sweden)

Rachmat Sahputra

2016-03-01

Full Text Available Learning in the understanding of acid-base chemistry in schools needs to be improved so research to determine differences in learning outcomes between students taught using environmental approaches and methods lectures in class XI SMA on acid-base subject needs to be done. In this study, using a quasi-experimental method using a data collection tool achievement test essay form. The test statistic results of the post-test learning has been obtained Asymp value. Sig (2-tailed 0,026 that showed the differences between students' learning outcomes with a control experimental class with effect size of 0.63 or much influence difference with the percentage 23.57% which indicated that the learning environment approach can improve learning outcomes of high school students.

Code-specific learning rules improve action selection by populations of spiking neurons.

Science.gov (United States)

Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

2014-08-01

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

Science.gov (United States)

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for

The influence of learning in collaborative improvement

DEFF Research Database (Denmark)

Nielsen, Jacob S.; Boer, Harry; Gertsen, Frank

2008-01-01

Collaborative improvement is a purposeful inter-company interactive process that focuses on continuous incremental innovation aimed at enhancing the partnership's overall performance. Considering that in such an environment the capability to learn jointly and individually is crucial, this paper...... takes a learning perspective on collaborative improvement and addresses the question: How do organisational learning and collaboration interplay and affect improvement performance? Based on an analysis of three dyads of the same Extended Manufacturing Enterprise, this paper concludes that a robust...

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Science.gov (United States)

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Roles of Technology in Student Learning of University Level Biostatistics

Science.gov (United States)

Xu, Weili; Zhang, Yuchen; Su, Cheng; Cui, Zhuang; Qi, Xiuying

2014-01-01

This study explored threshold concepts and areas of troublesome knowledge among students enrolled in a basic biostatistics course at the university level. The main area of troublesome knowledge among students was targeted by using technology to improve student learning. A total of 102 undergraduate students who responded to structured…

Improvement of Control Infrastructure and High Level Application for KOMAC LINAC

Energy Technology Data Exchange (ETDEWEB)

Song, Young-Gi; Kim, Jae-Ha; Ahn, Tae-Sung; Kwon, Hyeok-Jung; Cho, Yong-Sub [Korea Atomic Energy Research Institute, Gyeongju (Korea, Republic of)

2015-10-15

The Korea multi-purpose accelerator complex (KOMAC) has two beam extraction points at 20 and 100 MeV for proton beam utilization. There are about 70 control systems for controlling the KOMAC subsystems, such as the ion source, the radio frequency, the diagnostic devices, the magnet power supply, and the cooling system. The infrastructure which includes network system, local controllers, and control system environment was required to be changed to process increasing process variables without fail. Experimental Physics and Industrial Control System (EPICS) based high level control environment which includes alarm, data archiving was changed to support the improved infrastructure of KOMAC control system. In this paper, we will describe the improvement of infrastructures for the KOMAC control system and EPICS based high level application. We improved the control network environment and EPCIS based high level application for enhancement of the KOMAC control system.

Cloud Computing and Multi Agent System to improve Learning Object Paradigm

Directory of Open Access Journals (Sweden)

Ana B. Gil

2015-05-01

Full Text Available The paradigm of Learning Object provides Educators and Learners with the ability to access an extensive number of learning resources. To do so, this paradigm provides different technologies and tools, such as federated search platforms and storage repositories, in order to obtain information ubiquitously and on demand. However, the vast amount and variety of educational content, which is distributed among several repositories, and the existence of various and incompatible standards, technologies and interoperability layers among repositories, constitutes a real problem for the expansion of this paradigm. This study presents an agent-based architecture that uses the advantages provided by Cloud Computing platforms to deal with the open issues on the Learning Object paradigm.

Georgia - Improved Learning Environment

Data.gov (United States)

Millennium Challenge Corporation — The school rehabilitation activity seeks to decrease student and teacher absenteeism, increase students’ time on task, and, ultimately, improve learning and labor...

Problem Based Learning as a Cultural Tool for Health and Safety Learning in a Multi-national Company

DEFF Research Database (Denmark)

Adam, Henrik; Petersson, Eva

2013-01-01

The general background of this study is an interest in how cultural tools contribute to structuring learning activities. The specific interest is to explore how such tools co-determine employees’ problem solving actions in health, safety and environment (HSE) training activities in a multi...... learn to organise HSE actions in the context of using Problem Based Learning (PBL) applied as a cultural tool. More specifically, our interest is in how PBL promotes adult learning by drawing on learners’ experience and involving them in reflective and social processes in the given context......-national company context. Theoretically, the research takes its point of departure in a socio-cultural perspective on the role of cultural tools in learning, and in a complementary interest in the role of communicative framing of learning activities. In the research reported here, the focus is on how employees...

Minimum Information Loss Based Multi-kernel Learning for Flagellar Protein Recognition in Trypanosoma Brucei

KAUST Repository

Wang, Jim Jing-Yan

2014-12-01

Trypanosma brucei (T. Brucei) is an important pathogen agent of African trypanosomiasis. The flagellum is an essential and multifunctional organelle of T. Brucei, thus it is very important to recognize the flagellar proteins from T. Brucei proteins for the purposes of both biological research and drug design. In this paper, we investigate computationally recognizing flagellar proteins in T. Brucei by pattern recognition methods. It is argued that an optimal decision function can be obtained as the difference of probability functions of flagella protein and the non-flagellar protein for the purpose of flagella protein recognition. We propose to learn a multi-kernel classification function to approximate this optimal decision function, by minimizing the information loss of such approximation which is measured by the Kull back-Leibler (KL) divergence. An iterative multi-kernel classifier learning algorithm is developed to minimize the KL divergence for the problem of T. Brucei flagella protein recognition, experiments show its advantage over other T. Brucei flagellar protein recognition and multi-kernel learning methods. © 2014 IEEE.

Multi-level Reconfigurable Self-organization in Overlay Services

NARCIS (Netherlands)

Pournaras, E.

2013-01-01

Large-scale decentralized systems organized in overlay networks are complex to manage. Such systems integrate organizational complexity in the application-level resulting in low abstraction and modularity in their services. This thesis introduces a multi-level conceptual architecture for overlay

A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids

International Nuclear Information System (INIS)

Xi, Lei; Yu, Tao; Yang, Bo; Zhang, Xiaoshun

2015-01-01

Highlights: • Proposing a decentralized smart generation control scheme for the automatic generation control coordination. • A novel multi-agent learning algorithm is developed to resolve stochastic control problems in power systems. • A variable learning rate are introduced base on the framework of stochastic games. • A simulation platform is developed to test the performance of different algorithms. - Abstract: This paper proposes a multi-agent smart generation control scheme for the automatic generation control coordination in interconnected complex power systems. A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm is developed, which can effectively identify the optimal average policies via a variable learning rate under various operation conditions. Based on control performance standards, the proposed approach is implemented in a flexible multi-agent stochastic dynamic game-based smart generation control simulation platform. Based on the mixed strategy and average policy, it is highly adaptive in stochastic non-Markov environments and large time-delay systems, which can fulfill automatic generation control coordination in interconnected complex power systems in the presence of increasing penetration of decentralized renewable energy. Two case studies on both a two-area load–frequency control power system and the China Southern Power Grid model have been done. Simulation results verify that multi-agent smart generation control scheme based on the proposed approach can obtain optimal average policies thus improve the closed-loop system performances, and can achieve a fast convergence rate with significant robustness compared with other methods

Contextualizing learning to improve care using collaborative communities of practices.

Science.gov (United States)

Jeffs, Lianne; McShane, Julie; Flintoft, Virginia; White, Peggy; Indar, Alyssa; Maione, Maria; Lopez, A J; Bookey-Bassett, Sue; Scavuzzo, Lauren

2016-09-02

The use of interorganizational, collaborative approaches to build capacity in quality improvement (QI) in health care is showing promise as a useful model for scaling up and accelerating the implementation of interventions that bridge the "know-do" gap to improve clinical care and provider outcomes. Fundamental to a collaborative approach is interorganizational learning whereby organizations acquire, share, and combine knowledge with other organizations and have the opportunity to learn from their respective successes and challenges in improvement areas. This learning approach aims to create the conditions for collaborative, reflective, and innovative experiential systems that enable collective discussions regarding daily practice issues and finding solutions for improvement. The concepts associated with interorganizational learning and deliberate learning activities within a collaborative 'Communities-of-practice'(CoP) approach formed the foundation of the of an interactive QI knowledge translation initiative entitled PERFORM KT. Nine teams participated including seven teams from two acute care hospitals, one from a long term care center, and one from a mental health sciences center. Six monthly CoP learning sessions were held and teams, with the support of an assigned mentor, implemented a QI project and monitored their results which were presented at an end of project symposium. 47 individuals participated in either a focus group or a personal interview. Interviews were transcribed and analyzed using an iterative content analysis. Four key themes emerged from the narrative dataset around experiences and perceptions associated with the PERFORM KT initiative: 1) being successful and taking it to other levels by being systematic, structured, and mentored; 2) taking it outside the comfort zone by being exposed to new concepts and learning together; 3) hearing feedback, exchanging stories, and getting new ideas; and 4) having a pragmatic and accommodating approach to

Improving the power of an efficacy study of a social and emotional learning program: application of generalizability theory to the measurement of classroom-level outcomes.

Science.gov (United States)

Mashburn, Andrew J; Downer, Jason T; Rivers, Susan E; Brackett, Marc A; Martinez, Andres

2014-04-01

Social and emotional learning programs are designed to improve the quality of social interactions in schools and classrooms in order to positively affect students' social, emotional, and academic development. The statistical power of group randomized trials to detect effects of social and emotional learning programs and other preventive interventions on setting-level outcomes is influenced by the reliability of the outcome measure. In this paper, we apply generalizability theory to an observational measure of the quality of classroom interactions that is an outcome in a study of the efficacy of a social and emotional learning program called The Recognizing, Understanding, Labeling, Expressing, and Regulating emotions Approach. We estimate multiple sources of error variance in the setting-level outcome and identify observation procedures to use in the efficacy study that most efficiently reduce these sources of error. We then discuss the implications of using different observation procedures on both the statistical power and the monetary costs of conducting the efficacy study.

Team-based learning to improve learning outcomes in a therapeutics course sequence.

Science.gov (United States)

Bleske, Barry E; Remington, Tami L; Wells, Trisha D; Dorsch, Michael P; Guthrie, Sally K; Stumpf, Janice L; Alaniz, Marissa C; Ellingrod, Vicki L; Tingen, Jeffrey M

2014-02-12

To compare the effectiveness of team-based learning (TBL) to that of traditional lectures on learning outcomes in a therapeutics course sequence. A revised TBL curriculum was implemented in a therapeutic course sequence. Multiple choice and essay questions identical to those used to test third-year students (P3) taught using a traditional lecture format were administered to the second-year pharmacy students (P2) taught using the new TBL format. One hundred thirty-one multiple-choice questions were evaluated; 79 tested recall of knowledge and 52 tested higher level, application of knowledge. For the recall questions, students taught through traditional lectures scored significantly higher compared to the TBL students (88%±12% vs. 82%±16%, p=0.01). For the questions assessing application of knowledge, no differences were seen between teaching pedagogies (81%±16% vs. 77%±20%, p=0.24). Scores on essay questions and the number of students who achieved 100% were also similar between groups. Transition to a TBL format from a traditional lecture-based pedagogy allowed P2 students to perform at a similar level as students with an additional year of pharmacy education on application of knowledge type questions. However, P3 students outperformed P2 students regarding recall type questions and overall. Further assessment of long-term learning outcomes is needed to determine if TBL produces more persistent learning and improved application in clinical settings.

Middle Level Learning.

Science.gov (United States)

Wyman, Richard M., Jr.; Young, Katherine A.; Sliger, Bruce; Kafi, Patricia; Singer, Alan; Lamme, Linda Leonard

1998-01-01

Presents five brief articles related to middle-level learning. The articles are, "Using Children's Diaries to Teach the Oregon Trail"; "Living the Geography of Joseph and Temperance Brown"; "The ABCs of Small Grant Acquisition for Social Studies"; "Isomo Loruko: The Yoruba Naming Ceremony"; and "Child…

The integrated learning management using the STEM education for improve learning achievement and creativity in the topic of force and motion at the 9th grade level

Science.gov (United States)

Kakarndee, Nampetch; Kudthalang, Nukool; Jansawang, Natchanok

2018-01-01

The aims of this research study were to investigate and analyze the processing performances and the performance results (E1/E2) efficiency at the determining criteria for planning students' improvements to their learning processes toward their scientific knowledge were investigated, carry out the investigations, gathering evidence, and proposing explanations were developed and predicted. Students' engagements to their needs in unambiguous and clearly content of science teaching onto the instructional processes were attempted for establishing a national approach with the STEM education instructional method were strategized. Research administrations were designed to a sample size consisted of 40 secondary students in science class at the 9th grade level in Borabu School with the purposive sampling technique was selected. Using the STEM Education instructional innovation's lesson plans were managed learning activities. Students' learning achievements were assessed with the Pre-Test and Post-Test designs of 30 items. Students' creative thinking abilities were determined of their perceptions that obtained of the 3-item Creative Thinking Ability Test. The results for the effectiveness of the innovative instructional lesson plans based on the STEM Education Method, the lessoning effectiveness (E1/E2) evidences of 78.95/76.58 over the threshold setting is 75/75. Pretest-posttest designs for assessing students' learning achievements that impact a student's ability to achieve and explains with the STEM education instructional method were differences, significantly (ρ<.001) and the posttest of the 3-item Creative Thinking Ability Test designs for assessing Students' creative thinking abilities that impact a student's ability to have a good skill level in originality, fluency and flexibility thinking with the STEM education instructional method were differences, significantly (ρ<.001).

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

International Nuclear Information System (INIS)

Ren, X; Gao, H; Sharp, G

2016-01-01

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature B is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

Energy Technology Data Exchange (ETDEWEB)

Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

2016-06-15

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature B is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao

Analysing barriers to service improvement using a multi-level theory of innovation: the case of glaucoma outpatient clinics.

Science.gov (United States)

Turner, Simon; Vasilakis, Christos; Utley, Martin; Foster, Paul; Kotecha, Aachal; Fulop, Naomi J

2018-05-01

The development and implementation of innovation by healthcare providers is understood as a multi-determinant and multi-level process. Theories at different analytical levels (i.e. micro and organisational) are needed to capture the processes that influence innovation by providers. This article combines a micro theory of innovation, actor-network theory, with organisational level processes using the 'resource based view of the firm'. It examines the influence of, and interplay between, innovation-seeking teams (micro) and underlying organisational capabilities (meso) during innovation processes. We used ethnographic methods to study service innovations in relation to ophthalmology services run by a specialist English NHS Trust at multiple locations. Operational research techniques were used to support the ethnographic methods by mapping the care process in the existing and redesigned clinics. Deficiencies in organisational capabilities for supporting innovation were identified, including manager-clinician relations and organisation-wide resources. The article concludes that actor-network theory can be combined with the resource-based view to highlight the influence of organisational capabilities on the management of innovation. Equally, actor-network theory helps to address the lack of theory in the resource-based view on the micro practices of implementing change. © 2018 The Authors. Sociology of Health & Illness published by John Wiley & Sons Ltd on behalf of Foundation for SHIL.

Improved SVR Model for Multi-Layer Buildup Factor Calculation

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2006-01-01

The accuracy of point kernel method applied in gamma ray dose rate calculations in shielding design and radiation safety analysis is limited by the accuracy of buildup factors used in calculations. Although buildup factors for single-layer shields are well defined and understood, buildup factors for stratified shields represent a complex physical problem that is hard to express in mathematical terms. The traditional approach for expressing buildup factors of multi-layer shields is through semi-empirical formulas obtained by fitting the results of transport theory or Monte Carlo calculations. Such an approach requires an ad-hoc definition of the fitting function and often results with numerous and usually inadequately explained and defined correction factors added to the final empirical formula. Even more, finally obtained formulas are generally limited to a small number of predefined combinations of materials within relatively small range of gamma ray energies and shield thicknesses. Recently, a new approach has been suggested by the authors involving one of machine learning techniques called Support Vector Machines, i.e., Support Vector Regression (SVR). Preliminary investigations performed for double-layer shields revealed great potential of the method, but also pointed out some drawbacks of the developed model, mostly related to the selection of one of the parameters describing the problem (material atomic number), and the method in which the model was designed to evolve during the learning process. It is the aim of this paper to introduce a new parameter (single material buildup factor) that is to replace the existing material atomic number as an input parameter. The comparison of two models generated by different input parameters has been performed. The second goal is to improve the evolution process of learning, i.e., the experimental computational procedure that provides a framework for automated construction of complex regression models of predefined

Mobile learning to improve mathematics teachers mathematical competencies

Science.gov (United States)

Hendrayana, A.; Wahyudin

2018-01-01

The role of teachers is crucial to the success of mathematics learning. One of the learning indicator is characterized by the students’ improved mathematical proficiency. In order to increase that, it is necessary to improve the teacher’s mathematical skills first. For that, it needs an innovative way to get teachers close to easily accessible learning resources through technology. The technology can facilitate teachers to access learning resources anytime and anywhere. The appropriate information technology is mobile learning. Innovations that can make teachers easy to access learning resources are mobile applications that can be accessed anytime and anywhere either online or offline. The research method was research development method. In preliminary analysis, subjects consist of teachers and lecturers in professional teacher education program. The results that the teachers ready to adopt mobile-learning for the improvement of their skills.

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

Directory of Open Access Journals (Sweden)

Qinli Yang

2015-03-01

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

Deep learning for classification of islanding and grid disturbance based on multi-resolution singular spectrum entropy

Science.gov (United States)

Li, Tie; He, Xiaoyang; Tang, Junci; Zeng, Hui; Zhou, Chunying; Zhang, Nan; Liu, Hui; Lu, Zhuoxin; Kong, Xiangrui; Yan, Zheng

2018-02-01

Forasmuch as the distinguishment of islanding is easy to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of photovoltaic out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, deep learning is utilized to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the photovoltaic system mistakenly withdrawing from power grids can be avoided.

Financial Learning: Is It The Effective Way to Improve Financial Literacy among Accounting Students?

Directory of Open Access Journals (Sweden)

Herawati Nyoman Trisna

2018-01-01

Full Text Available The aims of this study are to determine: the difference of financial literacy level between students who have had experience in financial learning and who have not had experience in financial learning. The data for this study was collected through financial literacy test and questionnaire which was distributed through randomized sampling method. A total of 173 completed and usable questionnaire have been collected. The result shows that the level of financial literacy among accounting students comes under below optimal standard category. Students who have had financial learning experience have a higher level of financial literacy than students who have not. This study provides means to improve financial learning for accounting students in preparation for creating a prosperous future.

Discriminative Multi-View Interactive Image Re-Ranking.

Science.gov (United States)

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

Energy Technology Data Exchange (ETDEWEB)

Cui, Yonggang

2018-05-07

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

Multi-floor cascading ferroelectric nanostructures: multiple data writing-based multi-level non-volatile memory devices

Science.gov (United States)

Hyun, Seung; Kwon, Owoong; Lee, Bom-Yi; Seol, Daehee; Park, Beomjin; Lee, Jae Yong; Lee, Ju Hyun; Kim, Yunseok; Kim, Jin Kon

2016-01-01

Multiple data writing-based multi-level non-volatile memory has gained strong attention for next-generation memory devices to quickly accommodate an extremely large number of data bits because it is capable of storing multiple data bits in a single memory cell at once. However, all previously reported devices have failed to store a large number of data bits due to the macroscale cell size and have not allowed fast access to the stored data due to slow single data writing. Here, we introduce a novel three-dimensional multi-floor cascading polymeric ferroelectric nanostructure, successfully operating as an individual cell. In one cell, each floor has its own piezoresponse and the piezoresponse of one floor can be modulated by the bias voltage applied to the other floor, which means simultaneously written data bits in both floors can be identified. This could achieve multi-level memory through a multiple data writing process.Multiple data writing-based multi-level non-volatile memory has gained strong attention for next-generation memory devices to quickly accommodate an extremely large number of data bits because it is capable of storing multiple data bits in a single memory cell at once. However, all previously reported devices have failed to store a large number of data bits due to the macroscale cell size and have not allowed fast access to the stored data due to slow single data writing. Here, we introduce a novel three-dimensional multi-floor cascading polymeric ferroelectric nanostructure, successfully operating as an individual cell. In one cell, each floor has its own piezoresponse and the piezoresponse of one floor can be modulated by the bias voltage applied to the other floor, which means simultaneously written data bits in both floors can be identified. This could achieve multi-level memory through a multiple data writing process. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr07377d

A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

Directory of Open Access Journals (Sweden)

Shifen Cheng

2018-06-01

Full Text Available Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

Energy Technology Data Exchange (ETDEWEB)

Ma, Guangkai [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China and Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Wu, Guorong [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Wu, Ligang [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841 (Korea, Republic of)

2016-02-15

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

International Nuclear Information System (INIS)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the

Improving Information Technology Curriculum Learning Outcomes

Directory of Open Access Journals (Sweden)

Derrick L Anderson

2017-06-01

The case study research methodology has been selected to conduct the inquiry into this phenomenon. This empirical inquiry facilitates exploration of a contemporary phenomenon in depth within its real-life context using a variety of data sources. The subject of analysis will be two Information Technology classes composed of a combination of second year and third year students; both classes have six students, the same six students. Contribution It is the purpose of this research to show that the use of improved approaches to learning will produce more desirable learning outcomes. Findings The results of this inquiry clearly show that the use of the traditional behaviorist based pedagogic model to achieve college and university IT program learning outcomes is not as effective as a more constructivist based andragogic model. Recommendations Instruction based purely on either of these does a disservice to the typical college and university level learner. The correct approach lies somewhere in between them; the most successful outcome attainment would be the product of incorporating the best of both. Impact on Society Instructional strategies produce learning outcomes; learning outcomes demonstrate what knowledge has been acquired. Acquired knowledge is used by students as they pursue professional careers and other ventures in life. Future Research Learning and teaching approaches are not “one-size-fits-all” propositions; different strategies are appropriate for different circumstances and situations. Additional research should seek to introduce vehicles that will move learners away from one the traditional methodology that has been used throughout much of their educational careers to an approach that is better suited to equip them with the skills necessary to meet the challenges awaiting them in the professional world.

A collision dynamics model of a multi-level train

Science.gov (United States)

2006-11-05

In train collisions, multi-level rail passenger vehicles can deform in modes that are different from the behavior of single level cars. The deformation in single level cars usually occurs at the front end during a collision. In one particular inciden...

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

Science.gov (United States)

Weiss, Jeremy; Kuusisto, Finn; Boyd, Kendrick; Liu, Jie; Page, David

2015-01-01

Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the individualized treatment effect (ITE), which has better applicability to new patients. We compare ATE-estimation using randomized and observational analysis methods against ITE-estimation using machine learning, and describe how the ITE theoretically generalizes to new population distributions, whereas the ATE may not. On a synthetic data set of statin use and myocardial infarction (MI), we show that a learned ITE model improves true ITE estimation and outperforms the ATE. We additionally argue that ITE models should be learned with a consistent, nonparametric algorithm from unweighted examples and show experiments in favor of our argument using our synthetic data model and a real data set of D-penicillamine use for primary biliary cirrhosis.

The Rhetoric of Multi-Display Learning Spaces: exploratory experiences in visual art disciplines

Directory of Open Access Journals (Sweden)

Brett Bligh

2010-11-01

Full Text Available Multi-Display Learning Spaces (MD-LS comprise technologies to allow the viewing of multiple simultaneous visual materials, modes of learning which encourage critical reflection upon these materials, and spatial configurations which afford interaction between learners and the materials in orchestrated ways. In this paper we provide an argument for the benefits of Multi-Display Learning Spaces in supporting complex, disciplinary reasoning within learning, focussing upon our experiences within postgraduate visual arts education. The importance of considering the affordances of the physical environment within education has been acknowledged by the recent attention given to Learning Spaces, yet within visual art disciplines the perception of visual material within a given space has long been seen as a key methodological consideration with implications for the identity of the discipline itself. We analyse the methodological, technological and spatial affordances of MD-LS to support learning, and discuss comparative viewing as a disciplinary method to structure visual analysis within the space which benefits from the simultaneous display of multiple partitions of visual evidence. We offer an analysis of the role of the teacher in authoring and orchestration and conclude by proposing a more general structure for what we term ‘multiple perspective learning’, in which the presentation of multiple pieces of visual evidence creates the conditions for complex argumentation within Higher Education.

Improvement of global and regional mean sea level derived from satellite altimetry multi missions

Science.gov (United States)

Ablain, M.; Faugere, Y.; Larnicol, G.; Picot, N.; Cazenave, A.; Benveniste, J.

2012-04-01

With the satellite altimetry missions, the global mean sea level (GMSL) has been calculated on a continual basis since January 1993. 'Verification' phases, during which the satellites follow each other in close succession (Topex/Poseidon--Jason-1, then Jason-1--Jason-2), help to link up these different missions by precisely determining any bias between them. Envisat, ERS-1 and ERS-2 are also used, after being adjusted on these reference missions, in order to compute Mean Sea Level at high latitudes (higher than 66°N and S), and also to improve spatial resolution by combining all these missions together. The global mean sea level (MSL) deduced from TOPEX/Poseidon, Jason-1 and Jason-2 provide a global rate of 3.2 mm from 1993 to 2010 applying the post glacial rebound (MSL aviso website http://www.jason.oceanobs.com/msl). Besides, the regional sea level trends bring out an inhomogeneous repartition of the ocean elevation with local MSL slopes ranging from + 8 mm/yr to - 8 mm/year. A study published in 2009 [Ablain et al., 2009] has shown that the global MSL trend unceratainty was estimated at +/-0.6 mm/year with a confidence interval of 90%. The main sources of errors at global and regional scales are due to the orbit calculation and the wet troposphere correction. But others sea-level components have also a significant impact on the long-term stability of MSL as for instance the stability of instrumental parameters and the atmospheric corrections. Thanks to recent studies performed in the frame of the SALP project (supported by CNES) and Sea-level Climate Change Initiative project (supported by ESA), strong improvements have been provided for the estimation of the global and regional MSL trends. In this paper, we propose to describe them; they concern the orbit calculation thanks to new gravity fields, the atmospheric corrections thanks to ERA-interim reanalyses, the wet troposphere corrections thanks to the stability improvement, and also empirical corrections

Improving STEM Undergraduate Education with Efficient Learning Design

DEFF Research Database (Denmark)

Godsk, Mikkel

2018-01-01

The project investigates the potential of Learning Design for efficiently improving STEM undergraduate education with technology. In order to investigate this potential, the project consists of two main studies at Aarhus University: a study of the perspectives of the main stakeholders on Learning...... Design uptake. The project concludes that it is possible to improve STEM undergraduate education with Learning Design for technology-enhanced learning efficiently and that Efficient Learning Design provides a useful concept for qualifying educational decisions....... provided by technology-enhanced learning based on Learning Design, and in particular students’ learning was of a high common interest. However, only the educators were directly interested in Learning Design and its support for design, reuse in their practice and to inform pedagogy. A holistic concept...

Multi-level cascaded DC/DC converters for PV applications

Directory of Open Access Journals (Sweden)

Ahmed A.A. Hafez

2015-12-01

Full Text Available A robust multi-level cascaded DC/DC system for Photovoltaic (PV application is advised in this article. There are three PV generators, each is coupled to a half-bridge buck cell. Each PV-generator–buck-converter channel is controlled such that maximum power is captured independently under different irradiation and temperature levels. The system operation under normal and abnormal conditions was comprehensively investigated. Internal Model Control (IMC technique was adopted for tuning the controllers. An elaborate switching modulation strategy was used to reduce the current ripple and inductor size, while maintaining high efficiency. Annotative, simple and robust remedial strategies were proposed to mitigate different anticipated faults. Comprehensive simulation results in Matlab environment were illustrated for corroborating the performance of the advised cascaded DC/DC system under normal/abnormal conditions. The proposed system enjoys the merits of independency, reduced volumetric dimensions and improved efficiency. Furthermore, the system is inherently fault-tolerant.

Perceptual learning improves visual performance in juvenile amblyopia.

Science.gov (United States)

Li, Roger W; Young, Karen G; Hoenig, Pia; Levi, Dennis M

2005-09-01

To determine whether practicing a position-discrimination task improves visual performance in children with amblyopia and to determine the mechanism(s) of improvement. Five children (age range, 7-10 years) with amblyopia practiced a positional acuity task in which they had to judge which of three pairs of lines was misaligned. Positional noise was produced by distributing the individual patches of each line segment according to a Gaussian probability function. Observers were trained at three noise levels (including 0), with each observer performing between 3000 and 4000 responses in 7 to 10 sessions. Trial-by-trial feedback was provided. Four of the five observers showed significant improvement in positional acuity. In those four observers, on average, positional acuity with no noise improved by approximately 32% and with high noise by approximately 26%. A position-averaging model was used to parse the improvement into an increase in efficiency or a decrease in equivalent input noise. Two observers showed increased efficiency (51% and 117% improvements) with no significant change in equivalent input noise across sessions. The other two observers showed both a decrease in equivalent input noise (18% and 29%) and an increase in efficiency (17% and 71%). All five observers showed substantial improvement in Snellen acuity (approximately 26%) after practice. Perceptual learning can improve visual performance in amblyopic children. The improvement can be parsed into two important factors: decreased equivalent input noise and increased efficiency. Perceptual learning techniques may add an effective new method to the armamentarium of amblyopia treatments.

Multi-model predictive control method for nuclear steam generator water level

International Nuclear Information System (INIS)

Hu Ke; Yuan Jingqi

2008-01-01

The dynamics of a nuclear steam generator (SG) is very different according to the power levels and changes as time goes on. Therefore, it is an intractable as well as challenging task to improve the water level control system of the SG. In this paper, a robust model predictive control (RMPC) method is developed for the level control problem. Based on a multi-model framework, a combination of a local nominal model with a polytopic uncertain linear parameter varying (LPV) model is built to approximate the system's non-linear behavior. The optimization problem solved here is based on a receding horizon scheme involving the linear matrix inequality (LMI) technique. Closed loop stability and constraints satisfaction in the entire operating range are guaranteed by the feasibility of the optimization problem. Finally, simulation results show the effectiveness and the good performance of the proposed method

Tetrahedral Mesh Improvement Using Multi-face Retriangulation

DEFF Research Database (Denmark)

Misztal, Marek Krzysztof; Bærentzen, Jakob Andreas; Anton, François

2009-01-01

the algorithm is completely general with regard to quality criterion, we target improvement of the dihedral angle. The central idea in our algorithm is the introduction of a new local operation called multi-face retriangulation (MFRT) which supplements other known local operations. Like in many previous papers...

Developing Multi-Dimensional Evaluation Criteria for English Learning Websites with University Students and Professors

Science.gov (United States)

Liu, Gi-Zen; Liu, Zih-Hui; Hwang, Gwo-Jen

2011-01-01

Many English learning websites have been developed worldwide, but little research has been conducted concerning the development of comprehensive evaluation criteria. The main purpose of this study is thus to construct a multi-dimensional set of criteria to help learners and teachers evaluate the quality of English learning websites. These…

Big data privacy protection model based on multi-level trusted system

Science.gov (United States)

Zhang, Nan; Liu, Zehua; Han, Hongfeng

2018-05-01

This paper introduces and inherit the multi-level trusted system model that solves the Trojan virus by encrypting the privacy of user data, and achieve the principle: "not to read the high priority hierarchy, not to write the hierarchy with low priority". Thus ensuring that the low-priority data privacy leak does not affect the disclosure of high-priority data privacy. This paper inherits the multi-level trustworthy system model of Trojan horse and divides seven different risk levels. The priority level 1˜7 represent the low to high value of user data privacy, and realize seven kinds of encryption with different execution efficiency Algorithm, the higher the priority, the greater the value of user data privacy, at the expense of efficiency under the premise of choosing a more encrypted encryption algorithm to ensure data security. For enterprises, the price point is determined by the unit equipment users to decide the length of time. The higher the risk sub-group algorithm, the longer the encryption time. The model assumes that users prefer the lower priority encryption algorithm to ensure efficiency. This paper proposes a privacy cost model for each of the seven risk subgroups. Among them, the higher the privacy cost, the higher the priority of the risk sub-group, the higher the price the user needs to pay to ensure the privacy of the data. Furthermore, by introducing the existing pricing model of economics and the human traffic model proposed by this paper and fluctuating with the market demand, this paper improves the price of unit products when the market demand is low. On the other hand, when the market demand increases, the profit of the enterprise will be guaranteed under the guidance of the government by reducing the price per unit of product. Then, this paper introduces the dynamic factors of consumers' mood and age to optimize. At the same time, seven algorithms are selected from symmetric and asymmetric encryption algorithms to define the enterprise

THE DOMAINS FOR THE MULTI-CRITERIA DECISIONS ABOUT E-LEARNING SYSTEMS

Directory of Open Access Journals (Sweden)

Murat Pasa UYSAL

2012-04-01

Full Text Available Developments in computer and information technologies continue to give opportunities for designing advanced E-learning systems while entailing objective and technical evaluation methodologies. Design and development of E-learning systems require time-consuming and labor-intensive processes; therefore any decision about these systems and their analysis needs systematic and structured guidance to lead to better decisions. Multi-Criteria Decision Analysis (MCDA techniques are applicable in instructional technology-related research areas as well as in other academic disciplines. In this study, a conceptual domain model and a decision activity framework is proposed for E-learning systems. Instructional, technological, and administrative decision domains are included in this model. Finally, an illustrative example is given to show that AHP is an effective MCDA method for E-learning-related decisions.

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

Science.gov (United States)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

The learning organization and the level of consciousness

OpenAIRE

Chiva Gómez, Ricardo

2017-01-01

Purpose – The purpose of this paper is to analyze learning organization by comparing with other types of organizations. This typology is based on the levels of consciousness and relates each type of organization with a level of learning and an organizational structure. Design/methodology/approach – This is a conceptual paper based on the concept of levels of consciousness. Findings – The paper proposes that learning organization requires the highest level of consciousness. O...

Improving learning of anatomy with reusable learning objects

Directory of Open Access Journals (Sweden)

P Rad

2015-12-01

Full Text Available Introduction: The use of modern educational technologies is useful for learning, durability, sociability, and upgrading professionalism. The aim of this study was evaluating the effect of reusable learning objects on improving learning of anatomy. Methods: This was a quasi-experimental study. Fourteen (reusable learning objects RLO from different parts of anatomy of human body including thorax, abdomen, and pelvis were prepared for medical student in Yasuj University of Medical Sciences in 2009. The length of the time for RLO was between 11-22 min. Because their capacities were low, so they were easy to use with cell phone or MP4. These materials were available to the students before the classes. The mean scores of students in anatomy of human body group were compared to the medical students who were not used this method and entered the university in 2008. A questionnaire was designed by the researcher to evaluate the effect of RLO and on, content, interest and motivation, participation, preparation and attitude. Result: The mean scores of anatomy of human body of medical student who were entered the university in 2009 have been increased compare to the students in 2008, but this difference was not significant. Based on the questionnaire data, it was shown that the RLO had a positive effect on improving learning anatomy of human body (75.5% and the effective relationship (60.6%. The students were interested in using RLO (74.6%, some students (54.2% believed that this method should be replaced by lecture. Conclusion: The use of RLO could promote interests and effective communication among the students and led to increasing self-learning motivation.

IMPROVING TRUST THROUGH ETHICAL LEADERSHIP: MOVING BEYOND THE SOCIAL LEARNING THEORY TO A HISTORICAL LEARNING APPROACH

Directory of Open Access Journals (Sweden)

Omoregie Charles Osifo

2016-12-01

Full Text Available The complex nature of trust and its evolving relative concepts require a more idealistic and simpler review. Ethical leadership is related to trust, honesty, transparency, compassion, empathy, results-orientedness, and many other behavioral attributes. Ethical leadership and good leadership are the same, because they represent practicing what one preaches or showing a way to the accomplishment of set goals. The outcomes and findings of many research papers on trust and ethical leadership report positive correlations between ethical leadership and trust. Improving trust from different rational standpoints requires moving and looking beyond the popular theoretical framework through which most results are derived in order to create a new thinking perspective. Social learning theory strongly emphasizes modelling while the new historical learning approach, proposed by the author, is defined as an approach that creates unique historical awareness among individuals, groups, institutions, societies, and nations to use previous experience(s or occurrence(s as a guide in developing positive opinion(s and framework(s in order to tackle the problems and issues of today and tomorrow. Social learning theory is seen as limited from the perspectives of balancing the equation between leadership and trust, the non-compatibility of the values of different generations at work, and other approaches and methods that support the historical approach. This paper is argumentative, adopts a writer´s perspective, and employs a logical analysis of the literature. The main contention is that a historical learning approach can inform an independent-learning to improve trust and its relatives (e.g. motivation and performance, because independent learning can positively shape the value of integrity, which is an integral part of ethical leadership. Historical learning can positively shape leadership in every perspective, because good leadership can develop based on history and

The Co-creation, Connectivism and Collaboration Jigsaw; assembling the puzzle pieces for a successful multi-disciplinary student learning experience

OpenAIRE

Bassford, Marie; O'Sullivan, Angela; Bacon, Joanne; Crisp, Annette; Nichols-Drew, L.; Fowler, Mark R.

2017-01-01

CrashEd is a multi-disciplinary, cross-Faculty, University project that arose from five academics’ collaborative commitment to develop a car crash scenario as a widening participation activity. The success of the outreach project culminated in the inspiration to develop more academically challenging forensic scenarios for study at Higher Education level. The ethos of the Forensic Investigation module is on realistic, scenario-based learning and assessment methods, and involves subject special...

An e-learning course in medical immunology: does it improve learning outcome?

Science.gov (United States)

Boye, Sondre; Moen, Torolf; Vik, Torstein

2012-01-01

E-learning is used by most medical students almost daily and several studies have shown e-learning to improve learning outcome in small-scale interventions. However, few studies have explored the effects of e-learning in immunology. To study the effect of an e-learning package in immunology on learning outcomes in a written integrated examination and to examine student satisfaction with the e-learning package. All second-year students at a Norwegian medical school were offered an animated e-learning package in basic immunology as a supplement to the regular teaching. Each student's log-on-time was recorded and linked with the student's score on multiple choice questions included in an integrated end-of-the-year written examination. Student satisfaction was assessed through a questionnaire. The intermediate-range students (interquartile range) on average scored 3.6% better on the immunology part of the examination per hour they had used the e-learning package (p = 0.0046) and log-on-time explained 17% of the variance in immunology score. The best and the less skilled students' examination outcomes were not affected by the e-learning. The e-learning was well appreciated among the students. Use of an e-learning package in immunology in addition to regular teaching improved learning outcomes for intermediate-range students.

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

KAUST Repository

Wu, Baoyuan; Lyu, Siwei; Ghanem, Bernard

2015-01-01

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

Multi-focal Vision and Gaze Control Improve Navigation Performance

Directory of Open Access Journals (Sweden)

Kolja Kuehnlenz

2008-11-01

Full Text Available Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

Multi-level analysis in information systems research: the case of enterprise resource planning system usage in China

Science.gov (United States)

Sun, Yuan; Bhattacherjee, Anol

2011-11-01

Information technology (IT) usage within organisations is a multi-level phenomenon that is influenced by individual-level and organisational-level variables. Yet, current theories, such as the unified theory of acceptance and use of technology, describe IT usage as solely an individual-level phenomenon. This article postulates a model of organisational IT usage that integrates salient organisational-level variables such as user training, top management support and technical support within an individual-level model to postulate a multi-level model of IT usage. The multi-level model was then empirically validated using multi-level data collected from 128 end users and 26 managers in 26 firms in China regarding their use of enterprise resource planning systems and analysed using the multi-level structural equation modelling (MSEM) technique. We demonstrate the utility of MSEM analysis of multi-level data relative to the more common structural equation modelling analysis of single-level data and show how single-level data can be aggregated to approximate multi-level analysis when multi-level data collection is not possible. We hope that this article will motivate future scholars to employ multi-level data and multi-level analysis for understanding organisational phenomena that are truly multi-level in nature.

Learning and teaching in the regional learning environment : enabling students and teachers to cross boundaries in multi-stakeholder practices

NARCIS (Netherlands)

Oonk, Carla

2016-01-01

Finding solutions for complex societal problems requires cross-boundary collaboration between multiple stakeholders who represent various practices, disciplines and perspectives. The authentic, multi-stakeholder Regional Learning Environment (RLE) is expected to develop higher education students’

Learning in engineered multi-agent systems

Science.gov (United States)

Menon, Anup

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power---as is the case in present-day wind farms---does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed

Multi-Level Security Cannot Realise NEC Objectives

NARCIS (Netherlands)

Schotanus, H.A.; Hartog, T.; Verkoelen, C.A.A.

2012-01-01

Multi-Level Security (MLS) is often viewed as the holy grail of information security, especially in those environments where information of different classifications is being processed. In this paper we argue that MLS cannot facilitate the right balance between need-to-protect and duty-to-share as

Multi-valley effective mass theory for device-level modeling of open quantum dynamics

Science.gov (United States)

Jacobson, N. Tobias; Baczewski, Andrew D.; Frees, Adam; Gamble, John King; Montano, Ines; Moussa, Jonathan E.; Muller, Richard P.; Nielsen, Erik

2015-03-01

Simple models for semiconductor-based quantum information processors can provide useful qualitative descriptions of device behavior. However, as experimental implementations have matured, more specific guidance from theory has become necessary, particularly in the form of quantitatively reliable yet computationally efficient modeling. Besides modeling static device properties, improved characterization of noisy gate operations requires a more sophisticated description of device dynamics. Making use of recent developments in multi-valley effective mass theory, we discuss device-level simulations of the open system quantum dynamics of a qubit interacting with phonons and other noise sources. Sandia is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy National Nuclear Security Administration under Contract No. DE-AC04-94AL85000.

Improved field emission from indium decorated multi-walled carbon nanotubes

Energy Technology Data Exchange (ETDEWEB)

Sreekanth, M.; Ghosh, S., E-mail: santanu1@physics.iitd.ernet.in; Biswas, P.; Kumar, S.; Srivastava, P.

2016-10-15

Graphical abstract: Improved field emission properties have been achieved for Indium (In) decorated MWCNTs and are shown using the schematic of field emission set up with In/CNT cathode, and a plot of J-E characteristics for pristine and In decorated CNTs. - Highlights: • Field emission (FE) properties have been studied for the first time from Indium (In) decorated MWCNT films. • Observed increased density of states near the Fermi level for In decorated films. • Superior field emission properties have been achieved for In decorated CNT films. - Abstract: Multi-walled carbon nanotube (MWCNT) films were grown using thermal chemical vapor deposition (T-CVD) process and were decorated with indium metal particles by thermal evaporation technique. The In metal particles are found to get oxidized. The In decorated films show 250% enhancement in the FE current density, lower turn-on and threshold fields, and better temporal stability as compared to their undecorated counterpart. This improvement in field emission properties is primarily attributed to increased density of states near the Fermi level. The presence of O 2p states along with a small contribution from In 5s states results in the enhancement of density of states in the vicinity of the Fermi level.

A Multi-Modal Digital Game-Based Learning Environment for Hospitalized Children with Chronic Illnesses.

Science.gov (United States)

Chin, Jui-Chih; Tsuei, Mengping

2014-01-01

The aim of this study was to explore the digital game-based learning for children with chronic illnesses in the hospital settings. The design-based research and qualitative methods were applied. Three eight-year-old children with leukemia participated in this study. In the first phase, the multi-user game-based learning system was developed and…

An improved segmentation-based HMM learning method for Condition-based Maintenance

International Nuclear Information System (INIS)

Liu, T; Lemeire, J; Cartella, F; Meganck, S

2012-01-01

In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.

Multi-phase flow monitoring with electrical impedance tomography using level set based method

International Nuclear Information System (INIS)

Liu, Dong; Khambampati, Anil Kumar; Kim, Sin; Kim, Kyung Youn

2015-01-01

Highlights: • LSM has been used for shape reconstruction to monitor multi-phase flow using EIT. • Multi-phase level set model for conductivity is represented by two level set functions. • LSM handles topological merging and breaking naturally during evolution process. • To reduce the computational time, a narrowband technique was applied. • Use of narrowband and optimization approach results in efficient and fast method. - Abstract: In this paper, a level set-based reconstruction scheme is applied to multi-phase flow monitoring using electrical impedance tomography (EIT). The proposed scheme involves applying a narrowband level set method to solve the inverse problem of finding the interface between the regions having different conductivity values. The multi-phase level set model for the conductivity distribution inside the domain is represented by two level set functions. The key principle of the level set-based method is to implicitly represent the shape of interface as the zero level set of higher dimensional function and then solve a set of partial differential equations. The level set-based scheme handles topological merging and breaking naturally during the evolution process. It also offers several advantages compared to traditional pixel-based approach. Level set-based method for multi-phase flow is tested with numerical and experimental data. It is found that level set-based method has better reconstruction performance when compared to pixel-based method

LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.

Science.gov (United States)

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang

2015-03-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. Copyright © 2014 Elsevier Inc. All rights reserved.

Multi-level and Multi-component Bitmap Encoding for Efficient Search Operations

Directory of Open Access Journals (Sweden)

Madhu BHAN, Department of Computer Applications

2012-12-01

Full Text Available The growing interest in data warehousing for decision makers is becoming more and more crucial to make faster and efficient decisions. On-line decision needs short response times. Many indexing techniques have been created to achieve this goal in read only environments. Indexing technique that has attracted attention in multidimensional databases is Bitmap Indexing. The paper discusses the various existing bitmap indexing techniques along with their performance characteristics. The paper proposes two new bitmap indexing techniques in the class of multi-level and multi-component encoding schemes and prove that the two techniques have better space–time performance than some of the existing techniques used for range queries. We provide an analytical model for comparing the performance of our proposed encoding schemes with that of the existing ones.

Improved hydrogen combustion model for multi-compartment analysis

International Nuclear Information System (INIS)

Ogino, Masao; Hashimoto, Takashi

2000-01-01

NUPEC has been improving a hydrogen combustion model in MELCOR code for severe accident analysis. In the proposed combustion model, the flame velocity in a node was predicted using six different flame front shapes of fireball, prism, bubble, spherical jet, plane jet, and parallelepiped. A verification study of the proposed model was carried out using the NUPEC large-scale combustion test results following the previous work in which the GRS/Battelle multi-compartment combustion test results had been used. The selected test cases for the study were the premixed test and the scenario-oriented test which simulated the severe accident sequences of an actual plant. The improved MELCOR code replaced by the proposed model could predict sufficiently both results of the premixed test and the scenario-oriented test of NUPEC large-scale test. The improved MELCOR code was confirmed to simulate the combustion behavior in the multi-compartment containment vessel during a severe accident with acceptable degree of accuracy. Application of the new model to the LWR severe accident analysis will be continued. (author)

Multi-scale learning based segmentation of glands in digital colonrectal pathology images.

Science.gov (United States)

Gao, Yi; Liu, William; Arjun, Shipra; Zhu, Liangjia; Ratner, Vadim; Kurc, Tahsin; Saltz, Joel; Tannenbaum, Allen

2016-02-01

Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.

The application of language-game theory to the analysis of science learning: Developing an interpretive classroom-level learning framework

Science.gov (United States)

Ahmadibasir, Mohammad

In this study an interpretive learning framework that aims to measure learning on the classroom level is introduced. In order to develop and evaluate the value of the framework, a theoretical/empirical study is designed. The researcher attempted to illustrate how the proposed framework provides insights on the problem of classroom-level learning. The framework is developed by construction of connections between the current literature on science learning and Wittgenstein's language-game theory. In this framework learning is defined as change of classroom language-game or discourse. In the proposed framework, learning is measured by analysis of classroom discourse. The empirical explanation power of the framework is evaluated by applying the framework in the analysis of learning in a fifth-grade science classroom. The researcher attempted to analyze how students' colloquial discourse changed to a discourse that bears more resemblance to science discourse. The results of the empirical part of the investigation are presented in three parts: first, the gap between what students did and what they were supposed to do was reported. The gap showed that students during the classroom inquiry wanted to do simple comparisons by direct observation, while they were supposed to do tool-assisted observation and procedural manipulation for a complete comparison. Second, it was illustrated that the first attempt to connect the colloquial to science discourse was done by what was immediately intelligible for students and then the teacher negotiated with students in order to help them to connect the old to the new language-game more purposefully. The researcher suggested that these two events in the science classroom are critical in discourse change. Third, it was illustrated that through the academic year, the way that students did the act of comparison was improved and by the end of the year more accurate causal inferences were observable in classroom communication. At the end of the

Inquiry-based learning to improve student engagement in a large first year topic

Directory of Open Access Journals (Sweden)

Masha Smallhorn

2015-08-01

Full Text Available Increasing the opportunity for students to be involved in inquiry-based activities can improve engagement with content and assist in the development of analysis and critical thinking skills. The science laboratory has traditionally been used as a platform to apply the content gained through the lecture series. These activities have exposed students to experiments which test the concepts taught but which often result in a predicted outcome. To improve the engagement and learning outcomes of our large first year biology cohort, the laboratories were redeveloped. Superlabs were run with 100 students attending weekly sessions increasing the amount of contact time from previous years. Laboratories were redeveloped into guided-inquiry and educators facilitated teams of students to design and carry out an experiment. To analyse the impact of the redevelopment on student satisfaction and learning outcomes, students were surveyed and multiple choice exam data was compared before and after the redevelopment. Results suggest high levels of student satisfaction and a significant improvement in student learning outcomes. All disciplines should consider including inquiry-based activities as a methodology to improve student engagement and learning outcome as it fosters the development of independent learners. 

Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method

International Nuclear Information System (INIS)

Yu, Shiwei; Zhang, Junjie; Zheng, Shuhong; Sun, Han

2015-01-01

This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO 2 emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method. - Highlights: • A PSO–GA-optimized multi-factor environmental learning curve method is proposed. • The carbon intensity abatement potentials of the 30 Chinese provinces are estimated by

How Residents Learn From Patient Feedback: A Multi-Institutional Qualitative Study of Pediatrics Residents' Perspectives.

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Bogetz, Alyssa L; Orlov, Nicola; Blankenburg, Rebecca; Bhavaraju, Vasudha; McQueen, Alisa; Rassbach, Caroline

2018-04-01

Residents may view feedback from patients and their families with greater skepticism than feedback from supervisors and peers. While discussing patient and family feedback with faculty may improve residents' acceptance of feedback and learning, specific strategies have not been identified. We explored pediatrics residents' perspectives of patient feedback and identified strategies that promote residents' reflection on and learning from feedback. In this multi-institutional, qualitative study conducted in June and July 2016, we conducted focus groups with a purposive sample of pediatrics residents after their participation in a randomized controlled trial in which they received written patient feedback and either discussed it with faculty or reviewed it independently. Focus group transcripts were audiorecorded, transcribed, and analyzed for themes using the constant comparative approach associated with grounded theory. Thirty-six of 92 (39%) residents participated in 7 focus groups. Four themes emerged: (1) residents valued patient feedback but felt it may lack the specificity they desire; (2) discussing feedback with a trusted faculty member was helpful for self-reflection; (3) residents identified 5 strategies faculty used to facilitate their openness to and acceptance of patient feedback (eg, help resident overcome emotional responses to feedback and situate feedback in the context of lifelong learning); and (4) residents' perceptions of feedback credibility improved when faculty observed patient encounters and solicited feedback on the resident's behalf prior to discussions. Discussing patient feedback with faculty provided important scaffolding to enhance residents' openness to and reflection on patient feedback.

IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING

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National Aeronautics and Space Administration — IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG Abstract. Active learning has been successfully applied to many natural language...

Improved Extreme Learning Machine based on the Sensitivity Analysis

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Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

2018-03-01

Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

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Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

2017-12-01

Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

Adjusting to Social Change - A Multi-Level Analysis in Three Cultures

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2013-08-01

example, coping may be more collective in collectivist , compared to individualist , societies (Chang & Sivam, 2004). Some cultures have a greater sense...AFRL-AFOSR-UK-TR-2013-0041 Adjusting to Social Change - A multi-level Analysis in three cultures Prof Robin Goodwin...COVERED (From – To) 23 May 2012 – 22 May 2013 4. TITLE AND SUBTITLE Adjusting to Social Change - A multi-level Analysis in three cultures

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

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Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Directory of Open Access Journals (Sweden)

Rodolfo S. Simões

2018-02-01

Full Text Available Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Radix Achyranthis Bidentatae improves learning and memory capabilities in ovariectomized rats

Institute of Scientific and Technical Information of China (English)

Yuefen Wang; Ya Xu; Yanshu Pan; Weihong Li; Wei Zhang; Yang Liu; Jing Jia; Pengtao Li

2013-01-01

Kidney-tonifying recipe can reduce the accumulation of advanced glycation end products, prevent neuronal degeneration and improve cognitive functions in ovariectomized rats. Radix Achyranthis Bidentatae alcohol extracts may dose-dependently inhibit non-enzymatic saccharification in vitro. This study aimed to examine the effect of Radix Achyranthis Bidentatae on advanced glycation end products and on learning and memory capabilities in ovariectomized rats. Ovariectomized rats were treated with Radix Achyranthis Bidentatae alcohol extracts (containing 1.5 g/kg crude drug) or 0.1% aminoguanidine for 12 weeks and behavioral testing was performed with the Y-electrical maze. This test revealed that Radix Achyranthis Bidentatae and aminoguanidine could improve the learning and memory capabilities of ovariectomized rats. Results of competitive enzyme-linked immunosorbent assay showed that treatment with Radix Achyranthis Bidentatae or aminoguanidine reduced the accumulation of advanced glycation end products in the frontal cortex of ovariectomized rats, while increasing content in the blood and urine. Biochemical tests showed that treatment with Radix Achyranthis Bidentatae or aminoguanidine decreased superoxide dismutase activity in the serum and frontal cortex, and increased serum levels of glutathione peroxidase in ovariectomized rats. In addition, there was no apparent effect on malondialdehyde levels. These experimental findings indicate that Radix Achyranthis Bidentatae inhibits production of advanced glycation end products and its accumulation in brain tissue, and improves learning and memory capabilities in ovariectomized rats. These effects may be associated with an anti-oxidative action of the extract.

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

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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.

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

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Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

2016-07-01

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

Language Learning Strategy Use across Proficiency Levels

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Zarei, Abbas, Ali; Baharestani, Nooshin

2014-01-01

To investigate the use of language learning strategies (LLS) by Iranian EFL learners across proficiency levels, a total of 180 Iranian adult female EFL learners were selected and divided into three different proficiency level groups. To collect data, Oxford's (1990) Strategy Inventory for Language Learning (SILL) was used. One-way ANOVA procedures…

The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry

Science.gov (United States)

sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.

2018-04-01

The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

Using a collaborative Mobile Augmented Reality learning application (CoMARLA) to improve Improve Student Learning

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Hanafi, Hafizul Fahri bin; Soh Said, Che; Hanee Ariffin, Asma; Azlan Zainuddin, Nur; Samsuddin, Khairulanuar

2016-11-01

This study was carried out to improve student learning in ICT course using a collaborative mobile augmented reality learning application (CoMARLA). This learning application was developed based on the constructivist framework that would engender collaborative learning environment, in which students could learn collaboratively using their mobile phones. The research design was based on the pretest posttest control group design. The dependent variable was students’ learning performance after learning, and the independent variables were learning method and gender. Students’ learning performance before learning was treated as the covariate. The sample of the study comprised 120 non-IT (non-technical) undergraduates, with the mean age of 19.5. They were randomized into two groups, namely the experimental and control group. The experimental group used CoMARLA to learn one of the topics of the ICT Literacy course, namely Computer System; whereas the control group learned using the conventional approach. The research instrument used was a set of multiple-choice questions pertaining to the above topic. Pretesting was carried out before the learning sessions, and posttesting was performed after 6 hours of learning. Using the SPSS, Analysis of Covariance (ANCOVA) was performed on the data. The analysis showed that there were main effects attributed to the learning method and gender. The experimental group outperformed the control group by almost 9%, and male students outstripped their opposite counterparts by as much as 3%. Furthermore, an interaction effect was also observed showing differential performances of male students based on the learning methods, which did not occur among female students. Hence, the tool can be used to help undergraduates learn with greater efficacy when contextualized in an appropriate setting.

Improving health care quality and safety: the role of collective learning.

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Singer, Sara J; Benzer, Justin K; Hamdan, Sami U

2015-01-01

Despite decades of effort to improve quality and safety in health care, this goal feels increasingly elusive. Successful examples of improvement are infrequently replicated. This scoping review synthesizes 76 empirical or conceptual studies (out of 1208 originally screened) addressing learning in quality or safety improvement, that were published in selected health care and management journals between January 2000 and December 2014 to deepen understanding of the role that collective learning plays in quality and safety improvement. We categorize learning activities using a theoretical model that shows how leadership and environmental factors support collective learning processes and practices, and in turn team and organizational improvement outcomes. By focusing on quality and safety improvement, our review elaborates the premise of learning theory that leadership, environment, and processes combine to create conditions that promote learning. Specifically, we found that learning for quality and safety improvement includes experimentation (including deliberate experimentation, improvisation, learning from failures, exploration, and exploitation), internal and external knowledge acquisition, performance monitoring and comparison, and training. Supportive learning environments are characterized by team characteristics like psychological safety, appreciation of differences, openness to new ideas social motivation, and team autonomy; team contextual factors including learning resources like time for reflection, access to knowledge, organizational capabilities; incentives; and organizational culture, strategy, and structure; and external environmental factors including institutional pressures, environmental dynamism and competitiveness and learning collaboratives. Lastly learning in the context of quality and safety improvement requires leadership that reinforces learning through actions and behaviors that affect people, such as coaching and trust building, and through

From Continuous Improvement to Organisational Learning: Developmental Theory.

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Murray, Peter; Chapman, Ross

2003-01-01

Explores continuous improvement methods, which underlie total quality management, finding barriers to implementation in practice that are related to a one-dimensional approach. Suggests a multiple, unbounded learning cycle, a holistic approach that includes adaptive learning, learning styles, generative learning, and capability development.…

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

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Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

Improved multi-objective clustering algorithm using particle swarm optimization.

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Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

2017-01-01

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Multi-representation based on scientific investigation for enhancing students’ representation skills

Science.gov (United States)

Siswanto, J.; Susantini, E.; Jatmiko, B.

2018-03-01

This research aims to implementation learning physics with multi-representation based on the scientific investigation for enhancing students’ representation skills, especially on the magnetic field subject. The research design is one group pretest-posttest. This research was conducted in the department of mathematics education, Universitas PGRI Semarang, with the sample is students of class 2F who take basic physics courses. The data were obtained by representation skills test and documentation of multi-representation worksheet. The Results show gain analysis value of .64 which means some medium improvements. The result of t-test (α = .05) is shows p-value = .001. This learning significantly improves students representation skills.

Learning to improve path planning performance

International Nuclear Information System (INIS)

Chen, Pang C.

1995-04-01

In robotics, path planning refers to finding a short. collision-free path from an initial robot configuration to a desired configuratioin. It has to be fast to support real-time task-level robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To remedy this situation, we present and analyze a learning algorithm that uses past experience to increase future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a speedup-learning framework in which a slow but capable planner may be improved both cost-wise and capability-wise by a faster but less capable planner coupled with experience. The basic algorithm is suitable for stationary environments, and can be extended to accommodate changing environments with on-demand experience repair and object-attached experience abstraction. To analyze the algorithm, we characterize the situations in which the adaptive planner is useful, provide quantitative bounds to predict its behavior, and confirm our theoretical results with experiments in path planning of manipulators. Our algorithm and analysis are sufficiently, general that they may also be applied to other planning domains in which experience is useful

Improvement of multi-level resistive switching characteristics in solution-processed AlO x -based non-volatile resistive memory using microwave irradiation

Science.gov (United States)

Kim, Seung-Tae; Cho, Won-Ju

2018-01-01

We fabricated a resistive random access memory (ReRAM) device on a Ti/AlO x /Pt structure with solution-processed AlO x switching layer using microwave irradiation (MWI), and demonstrated multi-level cell (MLC) operation. To investigate the effect of MWI power on the MLC characteristics, post-deposition annealing was performed at 600-3000 W after AlO x switching layer deposition, and the MLC operation was compared with as-deposited (as-dep) and conventional thermally annealing (CTA) treated devices. All solution-processed AlO x -based ReRAM devices exhibited bipolar resistive switching (BRS) behavior. We found that these devices have four-resistance states (2 bits) of MLC operation according to the modulation of the high-resistance state (HRSs) through reset voltage control. Particularly, compared to the as-dep and CTA ReRAM devices, the MWI-treated ReRAM devices showed a significant increase in the memory window and stable endurance for multi-level operation. Moreover, as the MWI power increased, excellent MLC characteristics were exhibited because the resistance ratio between each resistance state was increased. In addition, it exhibited reliable retention characteristics without deterioration at 25 °C and 85 °C for 10 000 s. Finally, the relationship between the chemical characteristics of the solution-processed AlO x switching layer and BRS-based multi-level operation according to the annealing method and MWI power was investigated using x-ray photoelectron spectroscopy.

Improvement in the distribution of services in multi-agent systems with SCODA

Directory of Open Access Journals (Sweden)

Jesús Ángel ROMÁN GALLEGO

2016-06-01

Full Text Available The distribution of services on multi-agent systems allows it to reduce to the agents their computational load. The functionality of the system does not reside in the agents themselves, however it is ubiquitously distributed so that allows you to perform tasks in parallel avoiding an additional computational cost to the elements in the system. The distribution of services that offers SCODA (Distributed and Specialized Agent Communities allows an intelligent management of these services provided by agents of the system and the parallel execution of threads that allow to respond to requests asynchronously, which implies an improvement in the performance of the system at both the computational level as the level of quality of service in the control of these services. The comparison carried out in the case of study that is presented in this paper demonstrates the existing improvement in the distribution of services on systems based on SCODA.

Improved cognition after control of risk factors for multi-infarct dementia

International Nuclear Information System (INIS)

Meyer, J.S.; Judd, B.W.; Tawaklna, T.; Rogers, R.L.; Mortel, K.F.

1986-01-01

A cohort of 52 patients (30 men and 22 women) with multi-infarct dementia (MID) has been followed up prospectively for a mean interval of 22.2 months. Clinical course has been documented by serial history taking and interviews and neurological, medical, and psychological examinations, and correlated with measurements of cerebral blood flow. The clinical course and cognitive performance have been compared with those of age-matched normal volunteers and patients with Alzheimer's disease. Patients with MID were subdivided into hypertensive and normotensive groups, and also into those displaying stabilized or improved cognition and those whose condition deteriorated. Among hypertensive patients with MID, improved cognition and clinical course correlated with control of systolic blood pressure within upper limits of normalf (135 to 150 mm Hg), but if systolic blood pressure was reduced below this level, patients with MID deteriorated. Among normotensive patients with MID, improved cognition was associated with cessation of smoking cigarettes

Multi-level programming paradigm for extreme computing

International Nuclear Information System (INIS)

Petiton, S.; Sato, M.; Emad, N.; Calvin, C.; Tsuji, M.; Dandouna, M.

2013-01-01

In order to propose a framework and programming paradigms for post peta-scale computing, on the road to exa-scale computing and beyond, we introduced new languages, associated with a hierarchical multi-level programming paradigm, allowing scientific end-users and developers to program highly hierarchical architectures designed for extreme computing. In this paper, we explain the interest of such hierarchical multi-level programming paradigm for extreme computing and its well adaptation to several large computational science applications, such as for linear algebra solvers used for reactor core physic. We describe the YML language and framework allowing describing graphs of parallel components, which may be developed using PGAS-like language such as XMP, scheduled and computed on supercomputers. Then, we propose experimentations on supercomputers (such as the 'K' and 'Hooper' ones) of the hybrid method MERAM (Multiple Explicitly Restarted Arnoldi Method) as a case study for iterative methods manipulating sparse matrices, and the block Gauss-Jordan method as a case study for direct method manipulating dense matrices. We conclude proposing evolutions for this programming paradigm. (authors)

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

Science.gov (United States)

Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

FGP Approach for Solving Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy parameters

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m. s. osman

2017-09-01

Full Text Available In this paper, we consider fuzzy goal programming (FGP approach for solving multi-level multi-objective quadratic fractional programming (ML-MOQFP problem with fuzzy parameters in the constraints. Firstly, the concept of the ?-cut approach is applied to transform the set of fuzzy constraints into a common deterministic one. Then, the quadratic fractional objective functions in each level are transformed into quadratic objective functions based on a proposed transformation. Secondly, the FGP approach is utilized to obtain a compromise solution for the ML-MOQFP problem by minimizing the sum of the negative deviational variables. Finally, an illustrative numerical example is given to demonstrate the applicability and performance of the proposed approach.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

Science.gov (United States)

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

Directory of Open Access Journals (Sweden)

Dawyndt Peter

2010-01-01

Full Text Available Abstract Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

Science.gov (United States)

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial

How social policies can improve financial accessibility of healthcare: a multi-level analysis of unmet medical need in European countries.

Science.gov (United States)

Israel, Sabine

2016-03-05

The article explores in how far financial accessibility of healthcare (FAH) is restricted for low-income groups and identifies social protection policies that can supplement health policies in guaranteeing universal access to healthcare. The article is aimed to advance the literature on comparative European social epidemiology by focussing on income-related barriers of healthcare take-up. The research is carried out on the basis of multi-level cross-sectional analyses using 2012 EU-SILC data for 30 European countries. The social policy data stems from EU-SILC beneficiary information. It is argued that unmet medical needs are a reality for many individuals within Europe - not only due to direct user fees but also due to indirect costs such as waiting time, travel costs, time not spent working. Moreover, low FAH affects not only the lowest income quintile but also the lower middle income class. The study observes that social allowance increases the purchasing power of both household types, thereby helping them to overcome financial barriers to healthcare uptake. Alongside healthcare system reform aimed at improving the pro-poor availability of healthcare facilities and financing, policies directed at improving FAH should aim at providing a minimum income base to the low-income quintile. Moreover, categorical policies should address households exposed to debt which form the key vulnerable group within the low-income classes.

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

Science.gov (United States)

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

2017-01-01

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

Using Information Technology in the Navy Lessons Learned System to Improve Organizational Learning

National Research Council Canada - National Science Library

Garvey, Michael

2001-01-01

... to support or enhance organizational learning in the Navy. The research concludes that NLLS has improved organizational learning but has not attained as widespread use as is possible. Recommendations are provide to improve the program and increase NLLS exposure to the fleet and to the potential users of the system.

Transfer learning improves supervised image segmentation across imaging protocols

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

2015-01-01

with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

THE INTENTIONAL USE OF LEARNING TECHNOLOGIES TO IMPROVE LEARNING OUTCOMES IN STUDIO.

Directory of Open Access Journals (Sweden)

Andrew MacKenzie

2017-01-01

Full Text Available At the University of Canberra, Australia, the design and architecture faculty are trialling a range of approaches to incorporating learning technologies in the first year foundation studio to improve student learning outcomes. For this study researchers collected information on students’ access to their assignment information and feedback from the learning management system (LMS to discover how the students engaged in the design process. The studio curriculum was designed to encourage students to engage in a convergence, divergence dynamic (Brown, 2009; Thomas, Billsberry, Ambrosini, & Barton, 2014 in developing their own understanding of the design process. The staff tailored around points of convergence, online instruction, assessment tools and feedback in studio. We argue that using learning technologies in this way can improve intentionality at the beginning of semester, enhance students understanding of feedback and facilitate a more iterative approach to problem based learning in studio practice.

Evaluation of the molecular level visualisation approach for teaching and learning chemistry in Thailand

Science.gov (United States)

Phenglengdi, Butsari

This research evaluates the use of a molecular level visualisation approach in Thai secondary schools. The goal is to obtain insights about the usefulness of this approach, and to examine possible improvements in how the approach might be applied in the future. The methodology used for this research used both qualitative and quantitative approaches. Data were collected in the form of pre- and post-intervention multiple choice questions, open-ended-questions, drawing exercises, one-to-one interviews and video recordings of class activity. The research was conducted in two phases, involving a total of 261 students from the 11th Grade in Thailand. The use of VisChem animations in three studies was evaluated in Phase I. Study 1 was a pilot study exploring the benefits of incorporating VisChem animations to portray the molecular level. Study 2 compared test results between students exposed to these animations of molecular level events, and those not. Finally, in Study 3, test results were gathered from different types of schools (a rural school, a city school, and a university school). The results showed that students (and teachers) had misconceptions at the molecular level, and VisChem animations could help students understand chemistry concepts at the molecular level across all three types of schools. While the animation treatment group had a better score on the topic of states of water, the non-animation treatment group had a better score on the topic of dissolving sodium chloride in water than the animation group. The molecular level visualisation approach as a learning design was evaluated in Phase II. This approach involved a combination of VisChem animations, pictures, and diagrams together with the seven-step VisChem learning design. The study involved three classes of students, each with a different treatment, described as Class A - Traditional approach; Class B - VisChem animations with traditional approach; and Class C - Molecular level visualisation approach

Personalized Multi-Student Improvement Based on Bayesian Cybernetics

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Kaburlasos, Vassilis G.; Marinagi, Catherine C.; Tsoukalas, Vassilis Th.

2008-01-01

This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely "Module for Adaptive Assessment of Students" (or, "MAAS" for short), implements the proposed (feedback) techniques. In conclusion, a pilot…

Behavior Self-Organization in Multi-Agent Learning

National Research Council Canada - National Science Library

Bay, John

1999-01-01

There are four primary results of the first year of the project: It was discovered that clustering algorithms for pre-sorting high-dimensional datasets was not effective in improving subsequent processing by reinforcement learning methods...

A Big Data and Learning Analytics Approach to Process-Level Feedback in Cognitive Simulations.

Science.gov (United States)

Pecaric, Martin; Boutis, Kathy; Beckstead, Jason; Pusic, Martin

2017-02-01

Collecting and analyzing large amounts of process data for the purposes of education can be considered a big data/learning analytics (BD/LA) approach to improving learning. However, in the education of health care professionals, the application of BD/LA is limited to date. The authors discuss the potential advantages of the BD/LA approach for the process of learning via cognitive simulations. Using the lens of a cognitive model of radiograph interpretation with four phases (orientation, searching/scanning, feature detection, and decision making), they reanalyzed process data from a cognitive simulation of pediatric ankle radiography where 46 practitioners from three expertise levels classified 234 cases online. To illustrate the big data component, they highlight the data available in a digital environment (time-stamped, click-level process data). Learning analytics were illustrated using algorithmic computer-enabled approaches to process-level feedback.For each phase, the authors were able to identify examples of potentially useful BD/LA measures. For orientation, the trackable behavior of re-reviewing the clinical history was associated with increased diagnostic accuracy. For searching/scanning, evidence of skipping views was associated with an increased false-negative rate. For feature detection, heat maps overlaid on the radiograph can provide a metacognitive visualization of common novice errors. For decision making, the measured influence of sequence effects can reflect susceptibility to bias, whereas computer-generated path maps can provide insights into learners' diagnostic strategies.In conclusion, the augmented collection and dynamic analysis of learning process data within a cognitive simulation can improve feedback and prompt more precise reflection on a novice clinician's skill development.

Multi-Level Analysis of Peer Support, Internet Self-Efficacy and E-Learning Outcomes--The Contextual Effects of Collectivism and Group Potency

Science.gov (United States)

Chu, Regina Juchun; Chu, Anita Zichun

2010-01-01

The present study intends to explore the role of collectivism and group potency at group level in predicting individual Internet self-efficacy (ISE) and individual e-learning outcomes for people aged over 45. Group learning has been widely discussed in the research into online formats. However, less study has been carried out about how…

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

Directory of Open Access Journals (Sweden)

Ling-Li Zeng

2018-04-01

Full Text Available Background: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. Methods: A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Findings: Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. Interpretation: The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Keywords: Schizophrenia, Deep learning, Connectome, f

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

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Edi Nurcahyo

2018-03-01

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

Improved multi-objective clustering algorithm using particle swarm optimization.

Directory of Open Access Journals (Sweden)

Congcong Gong

Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Development of a Multi-Domain Assessment Tool for Quality Improvement Projects.

Science.gov (United States)

Rosenbluth, Glenn; Burman, Natalie J; Ranji, Sumant R; Boscardin, Christy K

2017-08-01

Improving the quality of health care and education has become a mandate at all levels within the medical profession. While several published quality improvement (QI) assessment tools exist, all have limitations in addressing the range of QI projects undertaken by learners in undergraduate medical education, graduate medical education, and continuing medical education. We developed and validated a tool to assess QI projects with learner engagement across the educational continuum. After reviewing existing tools, we interviewed local faculty who taught QI to understand how learners were engaged and what these faculty wanted in an ideal assessment tool. We then developed a list of competencies associated with QI, established items linked to these competencies, revised the items using an iterative process, and collected validity evidence for the tool. The resulting Multi-Domain Assessment of Quality Improvement Projects (MAQIP) rating tool contains 9 items, with criteria that may be completely fulfilled, partially fulfilled, or not fulfilled. Interrater reliability was 0.77. Untrained local faculty were able to use the tool with minimal guidance. The MAQIP is a 9-item, user-friendly tool that can be used to assess QI projects at various stages and to provide formative and summative feedback to learners at all levels.

Multi-level and hybrid modelling approaches for systems biology.

Science.gov (United States)

Bardini, R; Politano, G; Benso, A; Di Carlo, S

2017-01-01

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

Improving multi-tasking ability through action videogames.

Science.gov (United States)

Chiappe, Dan; Conger, Mark; Liao, Janet; Caldwell, J Lynn; Vu, Kim-Phuong L

2013-03-01

The present study examined whether action videogames can improve multi-tasking in high workload environments. Two groups with no action videogame experience were pre-tested using the Multi-Attribute Task Battery (MATB). It consists of two primary tasks; tracking and fuel management, and two secondary tasks; systems monitoring and communication. One group served as a control group, while a second played action videogames a minimum of 5 h a week for 10 weeks. Both groups returned for a post-assessment on the MATB. We found the videogame treatment enhanced performance on secondary tasks, without interfering with the primary tasks. Our results demonstrate action videogames can increase people's ability to take on additional tasks by increasing attentional capacity. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Creating and improving multi-threaded Geant4

CERN Document Server

Dong, Xin; Apostolakis, John; Jarp, Sverre; Nowak, Andrzej; Asai, Makoto; Brandt, Daniel

2012-01-01

We document the methods used to create the multi-threaded prototype Geant4MT from a sequential version of Geant4. We cover the Source-to-Source transformations applied, and discuss the process of verifying the correctness of the Geant4MT toolkit and applications based on it. Tools to ensure that the results of a transformed multi-threaded application are exactly equal to the original sequential version are under development. Stand-alone or simple applications can be adapted within 1-2 working days. Geant4MT is shown to scale linearly on an 80-core computer. In the special case of a single worker thread on one core, 30% overhead has been observed. We explain the reasons for this and the improvements introduced to reduce this overhead.

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

Science.gov (United States)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Cities, Europeanization and Multi-level Governance: Governing Climate Change through Transnational Municipal Networks

NARCIS (Netherlands)

Kern, K.; Bulkeley, H.

2009-01-01

This article focuses on a variant of multi-level governance and Europeanization, i.e. the transnational networking of local authorities. Focusing on local climate change policy, the article examines how transnational municipal networks (TMNs) govern in the context of multi-level European governance.

How to Improve Learning when Going Online Using POPBL

DEFF Research Database (Denmark)

Borch, Ole; Helbo, Jan; Madsen, Per Printz

2007-01-01

, Pedagogical and Technological (DPT) methods must be selected and used properly to ensure progress in the learning process. Although it has never been proven that PBL increases learning, there are many observations indicating improved learning, e.g. the students are able to learn more beyond required...... objectives within the defined time slot. The remote online education Master of Industrial Information Technology (MII) at Aalborg University (AAU), Denmark, is using collaborative Project Organized PBL (POPBL) and is using new DPT resulting in very high motivation and in remarkable learning results......It is accepted worldwide; that Problem Based Learning (PBL) is a very fine method to improve learning motivation and to satisfy the students being more innovative and creative. Progress in learning is supported by teaching, individual and team reflections and collaborative project work. On...

An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

Directory of Open Access Journals (Sweden)

Yingchang Xiu

2017-11-01

Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

Improving Learning Analytics--Combining Observational and Self-Report Data on Student Learning

Science.gov (United States)

Ellis, Robert A.; Han, Feifei; Pardo, Abelardo

2017-01-01

The field of education technology is embracing a use of learning analytics to improve student experiences of learning. Along with exponential growth in this area is an increasing concern of the interpretability of the analytics from the student experience and what they can tell us about learning. This study offers a way to address some of the…

Formal Framework to improve the reliability of concurrent and collaborative learning games

Directory of Open Access Journals (Sweden)

Mounier

2014-05-01

Full Text Available Multi-player learning games are complex software applications resulting from a costly and complex engineering process, and involving multiple stakeholders (domain experts, teachers, game designers, programmers, testers, etc.. Moreover, they are dynamic systems that evolve over time and implement complex interactions between objects and players. Usually, once a learning game is developed, testing activities are conducted by humans who explore the possible executions of the game’s scenario to detect bugs. The complexity and the dynamic nature of multiplayer learning games enforces the complexity of testing activities. Indeed, it is impracticable to explore manually all possible executions due to their huge number. Moreover, the test cannot verify some properties on multi-player and collaborative scenarios, such as paths leading to deadlock between learners or prevent learners to meet all objectives and win the game. This type of properties should be verified at the design stage. We propose a framework enabling a formal modeling of game scenarios and an associated automatic verification of learning game’s scenario at the design stage of the development process.We use Symmetric Petri nets as a modeling language and choose to verify properties by means of model checkers. This paper discusses the possibilities offered by this framework to verify learning game’s properties before the programming stage.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning.

Directory of Open Access Journals (Sweden)

Borja Fernandez-Gauna

Full Text Available Multi-Agent Reinforcement Learning (MARL algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Coupling biomechanics to a cellular level model: an approach to patient-specific image driven multi-scale and multi-physics tumor simulation.

Science.gov (United States)

May, Christian P; Kolokotroni, Eleni; Stamatakos, Georgios S; Büchler, Philippe

2011-10-01

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning. Copyright © 2011 Elsevier Ltd. All rights reserved.

Teaching EBP Using Game-Based Learning: Improving the Student Experience.

Science.gov (United States)

Davidson, Sandra J; Candy, Laurie

2016-08-01

Evidence-based practice (EBP) is considered a key entry to practice competency for nurses. However, many baccalaureate nursing programs continue to teach "traditional" nursing research courses that fail to address many of the critical knowledge, skills, and attitudes that foster EBP. Traditional classroom teaching strategies do little to promote the development of competencies critical for engaging in EBP in clinical contexts. The purpose of this work was to develop, implement, and evaluate an innovative teaching strategy aimed at improving student learning, engagement and satisfaction in an online EBP course. The goals of this paper are to: (1) describe the process of course development, (2) describe the innovative teaching strategy, and (3) discuss the outcomes of the pilot course offered using game-based learning. A midterm course-specific survey and standard institutional end of course evaluations were used to evaluate student satisfaction. Game platform analytics and thematic analysis of narrative comments in the midterm and end of course surveys were used to evaluate students' level of engagement. Student learning was evaluated using the end of course letter grade. Students indicated a high satisfaction with the course. Student engagement was also maintained throughout the course. The majority of students (87%, 26/30) continued to complete learning quests in the game after achieving the minimum amount of points to earn an A. Seven students completed every learning quest available in the game platform. Of the 30 students enrolled in the course, 17 students earned a final course grade of A+ and 13 earned an A. Provide students with timely, individualized feedback to enable mastery learning. Create student choice and customization of learning. Integrate the use of badges (game mechanics) to increase engagement and motivation. Level learning activities to build on each other and create flow. © 2016 Sigma Theta Tau International.

Improving Students’ Learning in Software Engineering Education through Multi-Level Assignments

NARCIS (Netherlands)

Dr. Leo Pruijt; Christian Köppe

2014-01-01

Author supplied: DOI : http://dx.doi.org/10.1145/2691352.2691357 Assignments and exercises are an essential part of software engineering education. It usually requires a variety of these assignments to cover a desired wide range of educational objectives as defined in the revised Bloom's taxonomy.

Improving the space surveillance telescope's performance using multi-hypothesis testing

Energy Technology Data Exchange (ETDEWEB)

Chris Zingarelli, J.; Cain, Stephen [Air Force Institute of Technology, 2950 Hobson Way, Bldg 641, Wright Patterson AFB, OH 45433 (United States); Pearce, Eric; Lambour, Richard [Lincoln Labratory, Massachusetts Institute of Technology, 244 Wood Street, Lexington, MA 02421 (United States); Blake, Travis [Defense Advanced Research Projects Agency, 675 North Randolph Street Arlington, VA 22203 (United States); Peterson, Curtis J. R., E-mail: John.Zingarelli@afit.edu [United States Air Force, 1690 Air Force Pentagon, Washington, DC 20330 (United States)

2014-05-01

The Space Surveillance Telescope (SST) is a Defense Advanced Research Projects Agency program designed to detect objects in space like near Earth asteroids and space debris in the geosynchronous Earth orbit (GEO) belt. Binary hypothesis test (BHT) methods have historically been used to facilitate the detection of new objects in space. In this paper a multi-hypothesis detection strategy is introduced to improve the detection performance of SST. In this context, the multi-hypothesis testing (MHT) determines if an unresolvable point source is in either the center, a corner, or a side of a pixel in contrast to BHT, which only tests whether an object is in the pixel or not. The images recorded by SST are undersampled such as to cause aliasing, which degrades the performance of traditional detection schemes. The equations for the MHT are derived in terms of signal-to-noise ratio (S/N), which is computed by subtracting the background light level around the pixel being tested and dividing by the standard deviation of the noise. A new method for determining the local noise statistics that rejects outliers is introduced in combination with the MHT. An experiment using observations of a known GEO satellite are used to demonstrate the improved detection performance of the new algorithm over algorithms previously reported in the literature. The results show a significant improvement in the probability of detection by as much as 50% over existing algorithms. In addition to detection, the S/N results prove to be linearly related to the least-squares estimates of point source irradiance, thus improving photometric accuracy.

Transferring an educational board game to a multi-user mobile learning game to increase shared situational awareness

NARCIS (Netherlands)

Klemke, Roland; Kurapati, Shalini; Kolfschoten, Gwendolyn

2013-01-01

Klemke, R., Kurapati, S., & Kolfschoten, G. (2013, 6 June). Transferring an educational board game to a multi-user mobile learning game to increase shared situational awareness. Presentation at the 3rd Irish Symposium on Game Based Learning, Dublin, Ireland. Please see also

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Science.gov (United States)

Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

2018-03-23

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging

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

Science.gov (United States)

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.

β-asarone improves learning and memory and reduces Acetyl Cholinesterase and Beta-amyloid 42 levels in APP/PS1 transgenic mice by regulating Beclin-1-dependent autophagy.

Science.gov (United States)

Deng, Minzhen; Huang, Liping; Ning, Baile; Wang, Nanbu; Zhang, Qinxin; Zhu, Caixia; Fang, Yongqi

2016-12-01

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly, and studies have suggested that β-asarone has pharmacological effects on beta-amyloid (Aβ) injected in the rat hippocampus. However, the effect of β-asarone on autophagy in the APP/PS1 transgenic mouse is unreported. APP/PS1 transgenic mice were randomly divided into six groups (n=10/group): an untreated group, an Aricept-treated group, a 3-MA-treated group, a rapamycin-treated group, an LY294002-treated group, a β-asarone-treated group. The control group consisted of wild-type C57BL/6 mice. All treatments were administered to the mice for 30 days. Spatial learning and memory were assessed by water maze, passive avoidance, and step-down tests. AChE and Aβ 42 levels in the hippocampus were determined by ELISA. p-Akt, p-mTOR, and LC3B expression were detected by flow cytometry. The expression of p-Akt, p-mTOR, Beclin-1, and p62 proteins was assessed by western blot. Changes in autophagy were viewed using a transmission electron microscope. APP and Beclin-1 mRNA levels were measured by Real-Time PCR. The learning and memory of APP/PS1 transgenic mice were improved significantly after β-asarone treatment compared with the untreated group. In addition, β-asarone treatment reduced AChE and Aβ 42 levels, increased p-mTOR and p62 expression, decreased p-Akt, Beclin-1, and LC3B expression, decreased the number of autophagosomes and reduced APP mRNA and Beclin-1 mRNA levels compared with the untreated group. That is, β-asarone treatment can improve the learning and memory abilities of APP/PS1 transgenic mouse by inhibiting Beclin-1-dependent autophagy via the PI3K/Akt/mTOR pathway. Copyright © 2016 Elsevier B.V. All rights reserved.

Examining multi-level effects on corporate social responsibility and irresponsibility

Directory of Open Access Journals (Sweden)

Mazzei Matthew J.

2015-10-01

Full Text Available What influences firms to engage in socially responsible (irresponsible activities? Corporate social responsibility (CSR, the efforts of firms to create a positive and desirable impact on society, and corporate social irresponsibility (CSI, contrary actions of unethical behavior that negatively influence society, have become an important focus of discussion for both corporations and scholars. Despite this interest, our understanding of organizations’ socially responsible (irresponsible actions and their antecedents is still developing. A dearth of knowledge about the multi-level nature of the drivers of CSR and CSI continues to exist. Utilizing a longitudinal sample composed of 899 firms in 66 industries, we follow a prominent model to empirically examine industry-, firm-, and individual-level effects on CSR and CSI. Employing variance decomposition analysis, our results confirm that all three levels of investigation do indeed influence CSR and CSI. More substantively, our analysis estimates the magnitude of the effects attributable to each of the three levels for both CSR and CSI. We also compare multi-level influences on two separate CSR strategies, those targeting primary stakeholders (strategic CSR and those targeting secondary stakeholders (social CSR. We find greater industry- and firmlevel effects on social CSR, and higher individual-level effects on strategic CSR. Our results build on the conceptual work of previous authors by providing empirical analyses to confirm multilevel influences on CSR and extending prior multi-level theory to the concept of CSI. Further, we add to the emerging literature regarding stakeholder demands by examining the various influences on CSR strategies targeting different stakeholder groups.

The Haptic Bracelets: Learning Multi-Limb Rhythm Skills from Haptic Stimuli While Reading

NARCIS (Netherlands)

Bouwer, A.; Holland, S.; Dalgleish, M.; Holland, S.; Wilkie, K.; Mulholland, P.; Seago, A.

2013-01-01

The Haptic Bracelets are a system designed to help people learn multi-limbed rhythms (which involve multiple simultaneous rhythmic patterns) while they carry out other tasks. The Haptic Bracelets consist of vibrotactiles attached to each wrist and ankle, together with a computer system to control

A unique, culture-aware, personalized learning environment

Directory of Open Access Journals (Sweden)

Tillman Swinke

2012-11-01

Full Text Available This paper examines what current learning systems offer towards the idea of a multi- dimensional learning system. It will show the requirements for a multi-dimensional learning system and that no current system is able to meet them. Therefore a new model is proposed that is not only capable of fulfilling the requirements for cultural diversity but also of satisfying the rising demand for personalization that has been rising in the course of the last twenty years. This new model will enable systems, which bring the personalization of e- learning to the next level.

Motivation to Improve Work through Learning: A Conceptual Model

Directory of Open Access Journals (Sweden)

Kueh Hua Ng

2014-12-01

Full Text Available This study aims to enhance our current understanding of the transfer of training by proposing a conceptual model that supports the mediating role of motivation to improve work through learning about the relationship between social support and the transfer of training. The examination of motivation to improve work through motivation to improve work through a learning construct offers a holistic view pertaining to a learner's profile in a workplace setting, which emphasizes learning for the improvement of work performance. The proposed conceptual model is expected to benefit human resource development theory building, as well as field practitioners by emphasizing the motivational aspects crucial for successful transfer of training.

Multi-level barriers to LTBI treatment: a research note.

Science.gov (United States)

Hill, Linda; Blumberg, Elaine; Sipan, Carol; Schmitz, Katharine; West, Joshua; Kelley, Norma; Hovell, Melbourne

2010-08-01

This study describes the barriers to effective and timely LTBI treatment encountered in a research study on INH adherence in Latino adolescents. Participant study logs were reviewed, results of continuing medical education pretests for medical providers were examined, and participating medical facilities were contacted in order to construct a profile of multi-level barriers to LTBI treatment. A total of 285 TST positive Latino (96%) high school students were recruited into the trial. We encountered a lack of understanding of the gravity of tuberculosis infection among both the public and providers of health care. Parents and adolescents cited competing priorities, transportation problems and financial constraints as reasons for non-compliance. Improved education of the public and physicians is needed regarding the gravity of the disease and the value of treatment, as well as public and financial support for LTBI treatment by both the government and the medical community.

DS-CDMA system outer loop power control and improvement for multi-service

Institute of Scientific and Technical Information of China (English)

Guan Mingxiang; Guo Qing; Li Xing

2008-01-01

When a new user accesses the CDMA system, the load will change drastically, and therefore, the advanced outer loop power control (OLPC) technology has to be adopted to enrich the target signal interference ratio (SIR) and improve the system performance. The existing problems about DS-CDMA outer loop power control for multi-service are introduced and the power control theoretical model is analyzed. System simulation is adopted on how to obtain the theoretical performance and parameter optimization of the power control algorithm. The OLPC algorithm is improved and the performance comparisons between the old algorithm and the improved algorithm are given. The results show good performance of the improved OLPC algorithm and prove the validity of the improved method for multi-service.

Multi-level approach for parametric roll analysis

Science.gov (United States)

Kim, Taeyoung; Kim, Yonghwan

2011-03-01

The present study considers multi-level approach for the analysis of parametric roll phenomena. Three kinds of computation method, GM variation, impulse response function (IRF), and Rankine panel method, are applied for the multi-level approach. IRF and Rankine panel method are based on the weakly nonlinear formulation which includes nonlinear Froude- Krylov and restoring forces. In the computation result of parametric roll occurrence test in regular waves, IRF and Rankine panel method show similar tendency. Although the GM variation approach predicts the occurrence of parametric roll at twice roll natural frequency, its frequency criteria shows a little difference. Nonlinear roll motion in bichromatic wave is also considered in this study. To prove the unstable roll motion in bichromatic waves, theoretical and numerical approaches are applied. The occurrence of parametric roll is theoretically examined by introducing the quasi-periodic Mathieu equation. Instability criteria are well predicted from stability analysis in theoretical approach. From the Fourier analysis, it has been verified that difference-frequency effects create the unstable roll motion. The occurrence of unstable roll motion in bichromatic wave is also observed in the experiment.

Production Practice During Language Learning Improves Comprehension.

Science.gov (United States)

Hopman, Elise W M; MacDonald, Maryellen C

2018-04-01

Language learners often spend more time comprehending than producing a new language. However, memory research suggests reasons to suspect that production practice might provide a stronger learning experience than comprehension practice. We tested the benefits of production during language learning and the degree to which this learning transfers to comprehension skill. We taught participants an artificial language containing multiple linguistic dependencies. Participants were randomly assigned to either a production- or a comprehension-learning condition, with conditions designed to balance attention demands and other known production-comprehension differences. After training, production-learning participants outperformed comprehension-learning participants on vocabulary comprehension and on comprehension tests of grammatical dependencies, even when we controlled for individual differences in vocabulary learning. This result shows that producing a language during learning can improve subsequent comprehension, which has implications for theories of memory and learning, language representations, and educational practices.

FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

Directory of Open Access Journals (Sweden)

Ni Putu Wulan Purnama Sari

2017-08-01

Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

Approximate multi-state reliability expressions using a new machine learning technique

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

Learning bridge tool to improve student learning, preceptor training, and faculty teamwork.

Science.gov (United States)

Karimi, Reza; Cawley, Pauline; Arendt, Cassandra S

2011-04-11

To implement a Learning Bridge tool to improve educational outcomes for pharmacy students as well as for preceptors and faculty members. Pharmacy faculty members collaborated to write 9 case-based assignments that first-year pharmacy (P1) students worked with preceptors to complete while at experiential sites. Students, faculty members, and preceptors were surveyed about their perceptions of the Learning Bridge process. As in our pilot study,(1) the Learning Bridge process promoted student learning. Additionally, the Learning Bridge assignments familiarized preceptors with the school's P1 curriculum and its content. Faculty teamwork also was increased through collaborating on the assignments. The Learning Bridge assignments provided a compelling learning environment and benefited students, preceptors, and faculty members.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

Science.gov (United States)

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Improving the quality of learning in science through optimization of lesson study for learning community

Science.gov (United States)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

Increasing dopamine levels in the brain improves feedback-based procedural learning: An artificial grammar learning experiment

NARCIS (Netherlands)

de Vries, M.H.; Ulte, C.; Zwitserlood, P.; Szymanski, B.; Knecht, S.

2010-01-01

Recently, an increasing number of studies have suggested a role for the basal ganglia and related dopamine inputs in procedural learning, specifically when learning occurs through trial-by-trial feedback (Shohamy, Myers, Kalanithi, & Gluck. (2008). Basal ganglia and dopamine contributions to

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.