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.

Ensemble Deep Learning for Biomedical Time Series Classification

Directory of Open Access Journals (Sweden)

Lin-peng Jin

2016-01-01

Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

Directory of Open Access Journals (Sweden)

Guofeng Wang

2014-11-01

Full Text Available Tool condition monitoring (TCM plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM, hidden Markov model (HMM and radius basis function (RBF are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

Online probabilistic learning with an ensemble of forecasts

Science.gov (United States)

Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

2016-04-01

Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

Udvidelsen af relationsfeltets didaktik – med religionsundervisningen som eksempel

DEFF Research Database (Denmark)

Skovmand, Keld

beskrive denne dobbeltbevægelse som en almen fagdidaktik (Nielsen 1998 [1994]) eller – med inspiration fra Klafki – en relationsfeltets didaktik (Nielsen 2012). Dobbeltblikket på didaktikken som et relationsfelt kan kvalificeres ved hjælp af de fire kriterier for indholdsvalg, som er udformet af Frede V...... specificere indhold (DuFour & Marzano 2011). Referencer DuFour, Richard & Marzano, Robert J. 2011: Leaders of learning. How district, school, and classroom leaders improve student achievement. Bloomington, IN: Solution Tree Press. Hattie, John 2009: Visible learning. A synthesis of over 800 meta...

Human resource recommendation algorithm based on ensemble learning and Spark

Science.gov (United States)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Deep SOMs for automated feature extraction and classification from big data streaming

Science.gov (United States)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

Directory of Open Access Journals (Sweden)

Yoonsik Shim

2016-10-01

Full Text Available We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP. The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

Science.gov (United States)

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Science.gov (United States)

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

International Nuclear Information System (INIS)

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

2008-01-01

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

Science.gov (United States)

Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

2000-06-01

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

Improving wave forecasting by integrating ensemble modelling and machine learning

Science.gov (United States)

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

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

2018-01-01

Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

Detection of eardrum abnormalities using ensemble deep learning approaches

Science.gov (United States)

Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yua, Lianbo; Gurcan, Metin N.

2018-02-01

In this study, we proposed an approach to report the condition of the eardrum as "normal" or "abnormal" by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(+/- 12.1%) and 82.6% (+/- 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (+/- 10.3%).

The "Tse Tsa Watle" Speaker Series: An Example of Ensemble Leadership and Generative Adult Learning

Science.gov (United States)

McKendry, Virginia

2017-01-01

This chapter examines an Indigenous speaker series formed to foster intercultural partnerships at a Canadian university. Using ensemble leadership and generative learning theories to make sense of the project, the author argues that ensemble leadership is key to designing the generative learning adult learners need in an era of ambiguity.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

Energy Technology Data Exchange (ETDEWEB)

Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

2011-04-15

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

International Nuclear Information System (INIS)

Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico

2011-01-01

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Induction of aversive learning through thermogenetic activation of Kenyon cell ensembles in Drosophila

Directory of Open Access Journals (Sweden)

David eVasmer

2014-05-01

Full Text Available Drosophila represents a model organism to analyze neuronal mechanisms underlying learning and memory. Kenyon cells of the Drosophila mushroom body are required for associative odor learning and memory retrieval. But is the mushroom body sufficient to acquire and retrieve an associative memory? To answer this question we have conceived an experimental approach to bypass olfactory sensory input and to thermogenetically activate sparse and random ensembles of Kenyon cells directly. We found that if the artifical activation of Kenyon cell ensembles coincides with a salient, aversive stimulus learning was induced The animals adjusted their behavior in a subsequent test situation and actively avoided reactivation of these Kenyon cells. Our results show that Kenyon cell activity in coincidence with a salient aversive stimulus can suffice to form an associative memory. Memory retrieval is characterized by a closed feedback loop between a behavioral action and the reactivation of sparse ensembles of Kenyon cells.

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

Science.gov (United States)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

Online Learning of Commission Avoidant Portfolio Ensembles

OpenAIRE

Uziel, Guy; El-Yaniv, Ran

2016-01-01

We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art.

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

Science.gov (United States)

Stanescu, Ana; Caragea, Doina

2015-01-01

Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

Ensemble Learning or Deep Learning? Application to Default Risk Analysis

Directory of Open Access Journals (Sweden)

Shigeyuki Hamori

2018-03-01

Full Text Available Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout.

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

Science.gov (United States)

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

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.

Quantum ensembles of quantum classifiers.

Science.gov (United States)

Schuld, Maria; Petruccione, Francesco

2018-02-09

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

Diagnosing Coronary Heart Disease using Ensemble Machine Learning

OpenAIRE

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

2016-01-01

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

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

Directory of Open Access Journals (Sweden)

Gang Zhang

2015-01-01

Full Text Available Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT and support vector machine (SVM are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

Evaluering som kvalitetsstøtte og mål som guide

DEFF Research Database (Denmark)

Levinsen, Karin Tweddell; Sørensen, Birgitte Holm

2017-01-01

Dette kapitel omhandler sammenhængen mellem det didaktiske design og evalueringspraksisser som aktivite der understøtter elevernes læring både som proces og produkt. Først gøres der rede for de anvendte begreber, og der argumenteres for, at formative og summative evalueringer kan forstås som...

Studerende som co-creators

DEFF Research Database (Denmark)

Andersen, Line Palle

En kogebog blev udviklet med studerende som co-creators på 3 moduler om innovation, skriftlig kommunikation og praktik med redigering. Healeys model om student-partnerships blev anvendt som fundament......En kogebog blev udviklet med studerende som co-creators på 3 moduler om innovation, skriftlig kommunikation og praktik med redigering. Healeys model om student-partnerships blev anvendt som fundament...

An ensemble machine learning approach to predict survival in breast cancer.

Science.gov (United States)

Djebbari, Amira; Liu, Ziying; Phan, Sieu; Famili, Fazel

2008-01-01

Current breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performance. We propose an ensemble machine learning approach which consists of choosing feature subsets and learning predictive models from them. We then combine models based on certain model fusion criteria and we also introduce a tuning parameter to control sensitivity. Our method significantly improves classification performance with a particular emphasis on sensitivity which is critical to avoid misclassifying poor prognosis patients as good prognosis.

Mobning som socialt begreb

DEFF Research Database (Denmark)

Schott, Robin May

2009-01-01

Artiklen analyserer mobning i skolen som et socialt fænomen snarere end som en relation mellem individuelle mobbere og deres ofre. Denne tilgang afspejler nyere forskning, der har fokuseret på mobningens sociale dynamik, og står derved i modsætning til den individualistisk tilgang, der ellers har...... domineret feltet. Jeg undersøger tre definition af mobning: 1) mobning som en form for individuel aggression, 2) mobning som en slags samfundsmæssig vold og 3) mobning som en form for dysfunktionel gruppedynamik og kommer med et forslag til en ny definition....

On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

Science.gov (United States)

Abdelsamea, Mohammed M.; Gnecco, Giorgio; Gaber, Mohamed Medhat; Elyan, Eyad

2015-01-01

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. PMID:25960736

Leg som ustyrlig deltagelseskultur

DEFF Research Database (Denmark)

Toft, Herdis

2017-01-01

- og spilteoretikere Johan Huizinga og Roger Caillois. Deres teorier og begrebsdannelser har været brugt til at påpege leg dels som et æstetisk baseret betydningssystem, dels som et affektivt og stemningsbaseret oplevelsessystem samt endelig som et socialt baseret relationssystem. I artiklen vælger vi...... at fokusere på leg som et socialt baseret relationssystem og yderligere zoome ind på et af legens systemiske væsenstræk, nemlig brugen af regulerbare regelsæt, som legerne uden ’politi’ forhandler sig frem til før, under og efter legen. Fælles for Huizinga og Caillois er, at de knytter leg uløseligt sammen...

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

Directory of Open Access Journals (Sweden)

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.

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

Science.gov (United States)

Zhang, Li; Ai, Haixin; Chen, Wen; Yin, Zimo; Hu, Huan; Zhu, Junfeng; Zhao, Jian; Zhao, Qi; Liu, Hongsheng

2017-05-18

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

A Prediction Method of Airport Noise Based on Hybrid Ensemble Learning

Directory of Open Access Journals (Sweden)

Tao XU

2014-05-01

Full Text Available Using monitoring history data to build and to train a prediction model for airport noise is a normal method in recent years. However, the single model built in different ways has various performances in the storage, efficiency and accuracy. In order to predict the noise accurately in some complex environment around airport, this paper presents a prediction method based on hybrid ensemble learning. The proposed method ensembles three algorithms: artificial neural network as an active learner, nearest neighbor as a passive leaner and nonlinear regression as a synthesized learner. The experimental results show that the three learners can meet forecast demands respectively in on- line, near-line and off-line. And the accuracy of prediction is improved by integrating these three learners’ results.

Musik som analogi og metafor

DEFF Research Database (Denmark)

Bonde, Lars Ole

2014-01-01

Indeholder underkapitlerne: 2.5.1 Musik som analogi 2.5.2 Musik som metafor 2.5.3 Musikkens psykologiske funktioner - en taxonomi og metaforisk lytning til fire baroksatser......Indeholder underkapitlerne: 2.5.1 Musik som analogi 2.5.2 Musik som metafor 2.5.3 Musikkens psykologiske funktioner - en taxonomi og metaforisk lytning til fire baroksatser...

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels.

Science.gov (United States)

Kaneko, Hiromasa

2018-02-26

To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.

Kulturen som krænkelsens virkelighed

DEFF Research Database (Denmark)

Brock, Steen

2006-01-01

En analyse af forholdet mellem natur og kultur i relation til en opfattelse af kultur, som har sin oprindelse i Cicero, og som har sin genklang i moderne tænkere som Goethe, Wittgenstein, Cavell og E. Wolfgang Orth.......En analyse af forholdet mellem natur og kultur i relation til en opfattelse af kultur, som har sin oprindelse i Cicero, og som har sin genklang i moderne tænkere som Goethe, Wittgenstein, Cavell og E. Wolfgang Orth....

E-tiviteter som eksamensform

Directory of Open Access Journals (Sweden)

Anette Grønning

2011-12-01

Full Text Available Med udgangspunkt i faget "Digital kommunikation på arbejde" præsenterer og diskuterer denne artikel en række anvendte digitale eksamensformer (e-tiviteter. E-tiviteterne er udarbejdet på baggrund af Gilly Salmons 5-fase-model og e-tivitetskoncept. Den anvendte e-læringsplatform er Blackboard, som de studerende og underviseren kender i forvejen. I artiklen præsenteres fagets e-tiviteter og undervisningsforløbet med e-tiviteter som eksamensform diskuteres. En række fordele og ulemper ved at veksle mellem offline- og online-aktiviteter (blended learning bliver behandlet. Det konkluderes, at de studerendes engagement og motivation kan øges ved at koble e-tiviteter sammen med eksamensformen "80% tilstedeværelse og aktiv deltagelse". Desuden fremhæves det, at aktivitetsniveauet mellem lektionerne og forberedelsesomfaget øges, ligesom de studerende motiveres til higher order thinking ved fx at tage ansvar for egen læring, udføre analyse og indgå i processen med kritisk refleksion. Endelig muliggør online-forløbet, at hele processen dokumenteres i faget til gavn for både underviser og studerende.

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

Directory of Open Access Journals (Sweden)

Sun Xun

2016-12-01

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

Ensemble learned vaccination uptake prediction using web search queries

DEFF Research Database (Denmark)

Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre

2016-01-01

We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...

DUT som didaktisk felt

DEFF Research Database (Denmark)

Qvortrup, Ane; Keiding, Tina B.

2015-01-01

undervisningsformer. [Abstract in English] The paper presents a systematic categorization of 115 papers published in the Danish Journal for Teaching and Learning in Higher Education. (DUT) from 2006 to 2013. The aim is to explore which didactic topics the journal deals with and how it contributes to the Danish...... the scholarship of teaching and learning. The categorization shows that most of the contributions deal with teaching methods. A subsequent analysis shows that the contributions almost exclusively deal with active and social teaching methods (teaching as experience and teaching as interaction)......(In Danish and English) Artiklen foretager en systematisk kategorisering og analyse af de bidrag, som har været bragt i Dansk Universitetspædagogisk Tidsskrift (DUT) fra 2006-2013. Formålet er at undersøge, hvilke didaktiske temaer tidskriftet har været optaget af og at diskutere mulige...

Boken som medium

OpenAIRE

Pelle, Snickars

2012-01-01

I slutet av september 2011 presenterade Amazon ett ny slags surfplatta, Kindle Fire. Till skillnad från företagets tidigare läsplattor, som (nästan) enbart handlat om läsning på skärm, var detta en Kindle som behärskade hela det samtidsmediala spektrat. Under ledning av Amazons VD Jeff Bezos var själva lanseringen av Kindle Fire visserligen en ganska platt historia. Bezos är ingen estradör och en föga karismatisk företagsledare med än mindre publikreaktioner gör inte någon succé – eller som e...

Gipskroppe som performance

DEFF Research Database (Denmark)

Holm, Henrik

2010-01-01

Analyse af den Kgl. Afstøbningssamlings nøgne gipsfigurer og samlingens nedtur igennem historien, med Judith Butler og andres performativitetsteorietiske begrebsapparat som metode.......Analyse af den Kgl. Afstøbningssamlings nøgne gipsfigurer og samlingens nedtur igennem historien, med Judith Butler og andres performativitetsteorietiske begrebsapparat som metode....

Proposal of a novel ensemble learning based segmentation with a shape prior and its application to spleen segmentation from a 3D abdominal CT volume

International Nuclear Information System (INIS)

Shindo, Kiyo; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru; Shinozaki, Kenji

2010-01-01

An organ segmentation learned by a conventional ensemble learning algorithm suffers from unnatural errors because each voxel is classified independently in the segmentation process. This paper proposes a novel ensemble learning algorithm that can take into account global shape and location of organs. It estimates the shape and location of an organ from a given image by combining an intermediate segmentation result with a statistical shape model. Once an ensemble learning algorithm could not improve the segmentation performance in the iterative learning process, it estimates the shape and location by finding an optimal model parameter set with maximum degree of correspondence between a statistical shape model and the intermediate segmentation result. Novel weak classifiers are generated based on a signed distance from a boundary of the estimated shape and a distance from a barycenter of the intermediate segmentation result. Subsequently it continues the learning process with the novel weak classifiers. This paper presents experimental results where the proposed ensemble learning algorithm generates a segmentation process that can extract a spleen from a 3D CT image more precisely than a conventional one. (author)

Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

Science.gov (United States)

Yu, Jianbo

2016-11-01

The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

Diversity in random subspacing ensembles

NARCIS (Netherlands)

Tsymbal, A.; Pechenizkiy, M.; Cunningham, P.; Kambayashi, Y.; Mohania, M.K.; Wöß, W.

2004-01-01

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are

A Novel Ensemble Learning Approach for Corporate Financial Distress Forecasting in Fashion and Textiles Supply Chains

Directory of Open Access Journals (Sweden)

Gang Xie

2013-01-01

Full Text Available This paper proposes a novel ensemble learning approach based on logistic regression (LR and artificial intelligence tool, that is, support vector machine (SVM and back-propagation neural networks (BPNN, for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM, and BPNN are introduced. Then, the forecasting results by LR are introduced into the SVM and BPNN techniques which can recognize the forecasting errors in fitness by LR. Moreover, empirical analysis of Chinese listed companies in fashion and textile sector is implemented for the comparison of the methods, and some related issues are discussed. The results suggest that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual models.

Supervision som undervisningsform i voksenspecialundervisningen

DEFF Research Database (Denmark)

Kristensen, René

2000-01-01

Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne.......Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne....

Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

Directory of Open Access Journals (Sweden)

Nikola Simidjievski

Full Text Available Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting, significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

Komponist som kirkefader?

DEFF Research Database (Denmark)

Petersen, Nils Holger

2016-01-01

En diskussion primært af Karl Barths anvendelse Mozart in sin Kirchliche Dogmatik under læren om "das Nichtige", som en slags løsning på theodicé-problemet.......En diskussion primært af Karl Barths anvendelse Mozart in sin Kirchliche Dogmatik under læren om "das Nichtige", som en slags løsning på theodicé-problemet....

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

Science.gov (United States)

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

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

Spam comments prediction using stacking with ensemble learning

Science.gov (United States)

Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang

2018-01-01

Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.

Forskere som nano-arkitekter

DEFF Research Database (Denmark)

Nielsen, Morten Muhlig; Andersen, Ebbe Sloth

2009-01-01

Dna er et fantastisk byggemateriale, som ud fra enkle principper kan samle sig selv til umådeligt komplicerede strukturer. Forskerne udnytter nu disse egenskaber til at lave deres egne, hjemmedesignede dna-strukturer, som kan tænkes anvendt i mange forskellige sammenhænge. Udgivelsesdato: juli...

Intelligent design som videnskab?

DEFF Research Database (Denmark)

Klausen, Søren Harnow

2007-01-01

Diskuterer hvorvidt intelligent design kan betegnes som videnskab; argumenterer for at dette grundet fraværet af klare demarkationskriterier næppe kan afvises.......Diskuterer hvorvidt intelligent design kan betegnes som videnskab; argumenterer for at dette grundet fraværet af klare demarkationskriterier næppe kan afvises....

Arbejdspladsen som læringsmiljø

DEFF Research Database (Denmark)

Andersen, Vibeke; Hoyrup Pedersen, Steen

Læring på arbejdspladsen er kommet i fokus både nationalt og internationalt som et stadigt vigtigere svar på videnssamfundets hurtigt eskalerende behov for kompetenceudvikling. Selvfølgelig kan arbejdspladslæring ikke erstatte de formaliserede erhvervs- og efteruddannelser, men arbejdspladsen er...... Workplace Learning. Konsortiet gennemfører i perioden 2001-2004 et større forskningsprogram, der udforsker læreprocesser i arbejdslivet og i samspillet mellem arbejdspladser og det formelle uddannelsessystem. Forskningsprogrammet er finansieret af Undervisningsministeriet og fokuserer primært på de...

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

Science.gov (United States)

Coop, Robert; Mishtal, Aaron; Arel, Itamar

2013-10-01

Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.

Studerendes caseundersøgelser som omdrejningspunkt for forskningsbaseret holdundervisning

Directory of Open Access Journals (Sweden)

Nina Bonderup Dohn

2013-03-01

Full Text Available Artiklen indledes med en diskussion af, hvori forskningsbaseret undervisning (FU består. Vi bestemmer FU som undervisning, hvor studerende involveres som aktive deltagere i processen med at producere ny viden ved brug af forskningsmetode. Vores overordnede forskningsspørgsmål lyder: Hvilke muligheder giver et didaktisk design baseret på, at de studerende inddrages som bidragydere til forskningsprocessen, for at realisere de forskellige begrundelser for forskningsbaseret undervisning? Hvilke problemstillinger viser der sig i forsøget herpå? Spørgsmålet undersøges med fokus på læringsteoretiske begrundelser og filosofiske begrundelser for FU. Vi rapporterer resultater fra et følgeforskningsprojekt i et undervisningsforløb, designet ud fra vores bestemmelse af FU. I diskussionen argumenterer vi for, at de læringsteoretiske begrundelser har rimelige muligheder for at realiseres med det didaktiske design, mens de filosofiske begrundelser kræver mere understøttelse. Resultaterne peger på en række problemstillinger og barrierer for realiseringen af FU (i en bestemmelse som vores og af begrundelserne for FU. Blandt disse er, at det didaktiske design indebærer en uvant arbejds- og studieform for de studerende, at der kan være en spænding mellem studieordningens faglige mål og FU (i en bestemmelse som vores, og at studerendes instrumentalistiske, erhvervsrettede mål kan være en barriere for realisering af de filosofiske begrundelser for FU. The article starts with a short discussion of different views on research-based teaching (RT. We characterize RT as teaching where students are involved as active participants in the process of producing new knowledge through the use of research methods. Our overall research question is: What possibilities does a pedagogical design based on involving students as contributors to the research process offer for realizing the different reasons for RT? What problems can arise? The research question

Yoga som terapeutisk aktivitet

DEFF Research Database (Denmark)

Broge, Julie Wolf

2014-01-01

Artiklen præsenterer yoga som terapeutisk aktivitet til børn og unge med særlige behov. Den gavnlige virkning af yoga uddybes.......Artiklen præsenterer yoga som terapeutisk aktivitet til børn og unge med særlige behov. Den gavnlige virkning af yoga uddybes....

Auto-fotografi som metode

DEFF Research Database (Denmark)

Mogensen, Mette

2014-01-01

Artiklen sætter fokus på auto-fotografi som metode i arbejdsmiljøforskningen. Den organisationsæstetiske tilgang, som metoden ofte forbindes med, udfordres med afsæt i en performativ og aktørnetværks-teoretisk position. Gennem en analyse af et enkelt auto-fotografi vises hvordan en artikulation af...

Værk som handling

DEFF Research Database (Denmark)

Jalving, Camilla

, er det stadig kun ved at blive en del af kunsthistoriens metode. Bogen afdækker derfor først begrebets skiftende betydninger fra J.L. Austins sprogfilosofi til Judith Butlers kønsteori og afprøver derefter, hvordan det kan bruges som metode i kunsthistorien og spores som tema i billedkunsten. Det...

Extensions and applications of ensemble-of-trees methods in machine learning

Science.gov (United States)

Bleich, Justin

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of

Ensemble methods for handwritten digit recognition

DEFF Research Database (Denmark)

Hansen, Lars Kai; Liisberg, Christian; Salamon, P.

1992-01-01

Neural network ensembles are applied to handwritten digit recognition. The individual networks of the ensemble are combinations of sparse look-up tables (LUTs) with random receptive fields. It is shown that the consensus of a group of networks outperforms the best individual of the ensemble....... It is further shown that it is possible to estimate the ensemble performance as well as the learning curve on a medium-size database. In addition the authors present preliminary analysis of experiments on a large database and show that state-of-the-art performance can be obtained using the ensemble approach...... by optimizing the receptive fields. It is concluded that it is possible to improve performance significantly by introducing moderate-size ensembles; in particular, a 20-25% improvement has been found. The ensemble random LUTs, when trained on a medium-size database, reach a performance (without rejects) of 94...

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

Science.gov (United States)

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The

An ensemble training scheme for machine-learning classification of Hyperion satellite imagery with independent hyperspectral libraries

Science.gov (United States)

Friedel, Michael; Buscema, Massimo

2016-04-01

A training scheme is proposed for the real-time classification of soil and vegetation (landscape) components in EO-1 Hyperion hyperspectral images. First, an auto-contractive map is used to compute connectivity of reflectance values for spectral bands (N=200) from independent laboratory spectral library components. Second, a minimum spanning tree is used to identify optimal grouping of training components from connectivity values. Third, the reflectance values for optimal landscape component signatures are sorted. Fourth, empirical distribution functions (EDF) are computed for each landscape component. Fifth, the Monte-Carlo technique is used to generate realizations (N=30) for each landscape EDF. The correspondence of component realizations to original signatures validates the stochastic procedure. Presentation of the realizations to the self-organizing map (SOM) is done using three different map sizes: 14x10, 28x20, and 40 x 30. In each case, the SOM training proceeds first with a rough phase (20 iterations using a Gaussian neighborhood with an initial and final radius of 11 units and 3 units) and then fine phase (400 iterations using a Gaussian neighborhood with an initial and final radius of 3 units and 1 unit). The initial and final learning rates of 0.5 and 0.05 decay linearly down to 10-5, and the Gaussian neighborhood function decreases exponentially (decay rate of 10-3 iteration-1) providing reasonable convergence. Following training of the three networks, each corresponding SOM is used to independently classify the original spectral library signatures. In comparing the different SOM networks, the 28x20 map size is chosen for independent reproducibility and processing speed. The corresponding universal distance matrix reveals separation of the seven component classes for this map size thereby supporting it use as a Hyperion classifier.

Statistikeren skiftede spor som 49-årig

DEFF Research Database (Denmark)

Sølund, Sune; Rootzén, Helle

2010-01-01

En uddannelse til coach har ændret Helle Rototzens liv. Som 49-årig forlod hun et forskerliv på deltid til fordel for en karriere som DTU's eneste kvindelige institutdirektør.......En uddannelse til coach har ændret Helle Rototzens liv. Som 49-årig forlod hun et forskerliv på deltid til fordel for en karriere som DTU's eneste kvindelige institutdirektør....

Ensemble Data Mining Methods

Science.gov (United States)

Oza, Nikunj C.

2004-01-01

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

Straf som penibel ledelsesform

DEFF Research Database (Denmark)

Knudsen, Hanne; Bjerg, Helle

2014-01-01

Kan klasseledelse fungere uden muligheden for at straffe? Kan man have en skole uden straf? Hvis man ser tilbage på skolens historie, ser svaret ud til at være nej. Skolen som institution ser ud til at være uløseligt forbundet med disciplinering af eleverne til at gøre noget, som de ikke nødvendi...

Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

Science.gov (United States)

Lee, Soojeong; Chang, Joon-Hyuk

2017-11-01

This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

Science.gov (United States)

Liu, Jing; Zhao, Songzheng; Wang, Gang

2018-01-01

With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Utilising Tree-Based Ensemble Learning for Speaker Segmentation

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

2014-01-01

In audio and speech processing, accurate detection of the changing points between multiple speakers in speech segments is an important stage for several applications such as speaker identification and tracking. Bayesian Information Criteria (BIC)-based approaches are the most traditionally used...... for a certain condition, the model becomes biased to the data used for training limiting the model’s generalisation ability. In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each...... tree is trained using a sampled version of the speech segment. During the tree construction process, a set of randomly selected points in the input sequence is examined as potential segmentation points. The point that yields the highest ΔBIC is chosen and the same process is repeated for the resultant...

Ensemble Data Mining Methods

Data.gov (United States)

National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

Lannister som statsminister

DEFF Research Database (Denmark)

Konzack, Lars

2014-01-01

Der er fuld skrue på intriger, sex og død i Game of Thrones. Det er stærkt underholdende og kan også ses som et billede på de moderne politiske spil. Hvem er seriens Putin? Og hvilken politisk leder ligner Prins Joffrey?......Der er fuld skrue på intriger, sex og død i Game of Thrones. Det er stærkt underholdende og kan også ses som et billede på de moderne politiske spil. Hvem er seriens Putin? Og hvilken politisk leder ligner Prins Joffrey?...

Diagnoser som styringshybrider

DEFF Research Database (Denmark)

Bossen, Claus; Danholt, Peter; Ubbesen, Morten Bonde

2016-01-01

- Relaterede Grupper (DRG). DRG er et internationalt udbredt system til at knytte patienter og deres behandlingsomkostninger sammen i faste kategorier med henblik på at måle hospitalers produktivitet. Med afsæt i Science-Technology-Studies (STS)-feltet analyserer artiklen, hvorledes diagnoser overskrider deres......, hvordan DRG-systemet alternativt kan anskues som en samfundsudviklende infrastruktur, idet det forsamler og skaber gensidigt involverende interaktioner imellem politiske, administrative og sundhedsprofessionelle domæner. En sådan indsigt bidrager til en udvidet forståelse af infrastrukturers roller som...

Børnetegninger som orienteringsspor og forskningsdata

DEFF Research Database (Denmark)

Nielsen, Anne Maj

2012-01-01

orientation and to discuss how children’s art and drawings can be approached as research data. The construct of children’s art and drawings are comprised of a double approach: Children’s drawings are conceptualized as articulations of not yet thematized emotions, experiences and meaningfulness...... hvordan de er blevet anvendt i forskning samt diskuterer forskellige perspektiver på tegninger som forskningsmateriale og anvendelse af tegninger i forskning. The article aims to illustrate how children’s art and drawings can produce knowledge of children’s personal, experience-based and gendered...... and the articulations include use and learning of visual socio-cultural codes and symbols. The construct comprise an existential-phenomenological and a socio-cultural learning oriented approach. A variation of examples is presented to illustrate the use of children’s drawings in research and to discuss the introduced...

Filosofisk terapi som social atilpasning

DEFF Research Database (Denmark)

Sørensen, Anders Dræby

Den dominerende psykopatologi forstår psykiske lidelser som udtryk for en patologisk utilpassethed, og psykoterapiens mål bliver normalisering gennem social og psykisk gentilpasning. Eksistentiel fænomenologi forstår lidelsen som etisk vildfarelse i det normale, og terapiens knytter an begreber o...

E-learning paradigmer og e-learning strategiudvikling

DEFF Research Database (Denmark)

Duus, Henrik Johannsen

2003-01-01

E-learning området er meget varieret hvad angår produkter, holdninger ogmeninger, og indeholder også en del 'støj' og mytedannelser, som afspejles i såvel denakademisk-videnskabelige som den journalistisk-offentlige debat om området. Dennevariation i såvel produkter som udtrykte meninger søges...... systematiseret og ordnet i fireidealtypiske paradigmer. Det vises, hvorledes disse fire paradigmer har hver sinebestemte karakteristika og udviklingsgrænser. Dette har afgørende strategisk betydningfor virksomheders og læreanstalters udvikling af e-learning, idet forkerte paradigmevalgvil hæmme udviklingen....

Fantasy som religion

DEFF Research Database (Denmark)

Davidsen, Markus

2010-01-01

Artiklen redegør for George Lucas' religionspædagogiske projekt med Star Wars og jediismens brug af Star Wars som religiøs tekst i en fantasybaseret religion. Afslutningsvist gives en række forslag til hvordan man kan anvende Star Wars og jediismen i folkeskolens religionsundervisning.......Artiklen redegør for George Lucas' religionspædagogiske projekt med Star Wars og jediismens brug af Star Wars som religiøs tekst i en fantasybaseret religion. Afslutningsvist gives en række forslag til hvordan man kan anvende Star Wars og jediismen i folkeskolens religionsundervisning....

Kollektivet som korrektiv

DEFF Research Database (Denmark)

Krøjer, Jo; Hutters, Camilla

2008-01-01

I Danmark har de unge individuel valgfrihed, når det kommer til valget af videregående uddannelse. Der er ikke nogen uddannelsespligt - valget er op til den unge. Den etablerede uddannelsesvejledning initierer et selvarbejde, hvorunder de unge anspores til at arbejde individuelt med at træffe det...... rette uddannelsesvalg. De unge oplever uddannelsesvalget som et ensomt og belastende arbejde. Så hvad sker der, når unge kollektivt reflekterer over at skulle vælge uddannelse? Artiklen præsenterer fortælleværksteder som metode til kollektive korrektioner af neoliberal regulering....

Fokusgruppeinterview som led i en evalueringsproces

DEFF Research Database (Denmark)

Andersen-Mølgaard, Hanna; Harrit, Ole

2006-01-01

Teoretiske begrundelser og perspektiver, responsiv-konstruktivistisk evaluering, fokusgruppeinterview som led i BIKVAmodellen, eksempler, vurdering og perspektivering......Teoretiske begrundelser og perspektiver, responsiv-konstruktivistisk evaluering, fokusgruppeinterview som led i BIKVAmodellen, eksempler, vurdering og perspektivering...

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

Science.gov (United States)

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

Directory of Open Access Journals (Sweden)

Cuicui Zhang

2014-12-01

Full Text Available Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1 how to define diverse base classifiers from the small data; (2 how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

Science.gov (United States)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

Kakkelovnen som arkiv

DEFF Research Database (Denmark)

Duedahl, Poul

2005-01-01

"Folk gjorde såmænd klogt i at gøre som jeg - jeg bruger kakkelovnen som arkiv" gentog tidligere konseilspræsident J.C. Christensen (1856-1930) ofte over for venner og bekendte. Selv destruerede han de fleste af sine breve, mens han var i live, og i mange år troede man, at hans dagbøger blev bræn...... anledning til nogle refleksioner over, hvad man skal stille op med dem; hvad det er, der får folk til at skrive dagbøger og hvad det er, der får folk til at skille sig af med dem igen....

An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

Science.gov (United States)

Yu, Hualong; Ni, Jun

2014-01-01

Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

New technologies for examining neuronal ensembles in drug addiction and fear

Science.gov (United States)

Cruz, Fabio C.; Koya, Eisuke; Guez-Barber, Danielle H.; Bossert, Jennifer M.; Lupica, Carl R.; Shaham, Yavin; Hope, Bruce T.

2015-01-01

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear. PMID:24088811

"Hvem er som jeg blandt Guder?"

DEFF Research Database (Denmark)

Holst, Søren

2004-01-01

En hymne fra Dødehavsrullerne beskriver en persons opstigning til himlen, hvor den pågældende får tildelt overmenneskelig indsigt, som giver ham autoritet til at belære sine tilhængere på jorden. Hymnen analyseres, og der foretages en kritisk gennemgang af Israel Knohls bog "The Messiah Before Je...... Jesus", som hævder at teksten skulle handle om et medlem af Qumran-menigheden, som blev dræbt i Jerusalem og senere hævdedes at være opstået fra de døde....

Byen som bevægelsesgrund?

DEFF Research Database (Denmark)

Højbjerre Larsen, Signe

2013-01-01

Parkour er en bevægelseskultur der ikke udfolder sig i afgrænsede rum eller på bestemte tidspunkter. Den er i stedet betinget af at byens rum anvendes som bevægelsesgrund. Parkourudøvernes særegne brug af byrummet kalder på opmærksomhed og analytisk undren: Hvordan kan vi forstå et fænomen som pa...

Gruppeeksamen som strategisk kommunikation

DEFF Research Database (Denmark)

Andersen, Tem Frank; Bang, Janne

2014-01-01

Artiklen har fokus på at skabe et grundlag for en teoretisk diskussion af gruppeeksamen som den bedste og mest egnede form for eksamen, når problembaseret læring er mål og middel i regi af Aalborg Universitet.......Artiklen har fokus på at skabe et grundlag for en teoretisk diskussion af gruppeeksamen som den bedste og mest egnede form for eksamen, når problembaseret læring er mål og middel i regi af Aalborg Universitet....

Artikelskrivning som eksamensform

Directory of Open Access Journals (Sweden)

Lars Domino Østergaard

2013-09-01

Full Text Available I denne artikel vil vi diskutere, hvordan studerendes arbejde med udformning af en videnskabelig artikel som afsæt for summativ evaluering af undervisningsmoduler, kan bidrage såvel til de studerendes oplevelse af motivation som til oplevelse af, at de gives muligheder for at udvikle konkrete aka­demiske kompetencer. Vi tager udgangspunkt i en række teoretiske over­vejelser, der knytter den summative evaluering til begreber som autentiske rammer, kompetenceudvikling og motivation. Disse overvejelser supplerer vi med resultaterne af en konkret undersøgelse af et pilotforsøg gen­nem­ført ved 2. semester på kandidatuddannelsen i Idræt på Aalborg Uni­versitet. I relation til to kursusmoduler har de studerende udarbejdet en videnskabelig artikel med afsæt i indsamlet empiri. Undersøgelsen viser, at de studerende i altovervejende grad er positive over for artikelskrivning som udgangspunkt for deres evaluering. Undersøgelsen viser videre, at de studerende oplever en følelse af motivation ved bl.a. at arbejde med selv­valgte problemstillinger. Hvorvidt de studerende faktisk udviklede kom­­petencer i forløbet, som de ikke kunne udvikle ved andre evalueringsformer, er fortsat ikke afdækket men vil være et naturligt næste genstandsfelt for undersøgelse.     In this paper we discuss how setting students a scientific paper to write as part of their course module summative assessment may help motivate students and contribute to their academic competencies. We depart from theoretical considerations that connect summative evaluation to concepts of authentic frameworks, competence development and motivation, and instead argue that writing a scientific paper as part of evaluation may be both motivational to students and provide a framework for developing competencies. We supplement these observations by presenting the results from a study conducted in a 2nd semester master programme in sport science at Aalborg University, Denmark

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

Science.gov (United States)

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT project.

Directory of Open Access Journals (Sweden)

Manal Alghamdi

Full Text Available Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE. The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree and achieved high accuracy of prediction (AUC = 0.92. The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

Directory of Open Access Journals (Sweden)

Gulshan Kumar

2012-01-01

Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

Exploring diversity in ensemble classification: Applications in large area land cover mapping

Science.gov (United States)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area

A hybrid ensemble learning approach to star-galaxy classification

Science.gov (United States)

Kim, Edward J.; Brunner, Robert J.; Carrasco Kind, Matias

2015-10-01

There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robust star-galaxy classification. To demonstrate this hybrid, ensemble approach, we combine a purely morphological classifier, a supervised machine learning method based on random forest, an unsupervised machine learning method based on self-organizing maps, and a hierarchical Bayesian template-fitting method. Using data from the CFHTLenS survey (Canada-France-Hawaii Telescope Lensing Survey), we consider different scenarios: when a high-quality training set is available with spectroscopic labels from DEEP2 (Deep Extragalactic Evolutionary Probe Phase 2 ), SDSS (Sloan Digital Sky Survey), VIPERS (VIMOS Public Extragalactic Redshift Survey), and VVDS (VIMOS VLT Deep Survey), and when the demographics of sources in a low-quality training set do not match the demographics of objects in the test data set. We demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method in these scenarios. Thus, strategies that combine the predictions of different classifiers may prove to be optimal in currently ongoing and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

Introduktionslæger som vejledere - hvorfor?

DEFF Research Database (Denmark)

Skaarup, Anne Marie; Ringsted, Charlotte Vibeke

2004-01-01

Statusartiklen har til hensigt at beskrive det pædagogiske rationale for at anvende introduktionslæger som vejledere, holdningen blandt introduktionslæger og speciallæger til at bruge introduktionslæger som vejledere, og hvilke ulemper der kan identificeres, og hvordan de eventuelt kan overvindes...

Tavs viden som led i vidensdiskursen

DEFF Research Database (Denmark)

Christensen, Jens

2004-01-01

Bidraget giver en  erkendelsesteoretisk tilgang til tavs viden som ressource i en teknologisk forandringsproces. Der tages udgangspunkt i, at tavs viden ikke kan betragtes uden at forholde sig til den mere generelle og historiske vidensdiskurs, som igen indgår i en kontekst af relationer mellem e...

Sociale medier som læringsredskaber

Directory of Open Access Journals (Sweden)

Lill Ingstad

2011-03-01

Full Text Available Kan sociale medier anvendes som læringsredskaber til støtte for sociale, kollaborative processer, der er udfordrende og motiverende, og som samtidig understøtter (fremmedsprogstilegnelsen?Artiklen diskuterer eksempler på fagdidaktiske udfordringer i sprogundervisningen på CBS med fokus på engelsk.

Tester og testtilbakemeldinger som direkte bidragsytere til dypere læring blant studenter

Directory of Open Access Journals (Sweden)

Henrik Herrebrøden

2014-11-01

Full Text Available Denne artikkelen diskuterer forskning som tilsier at tester og testtilbakemeldinger kan bidra til dypere læring blant studenter. Testing har vist seg å forbedre langtidshukommelsen av innlært kunnskap sammenlignet med repetert lesing, noe som kalles for testeffekten. For at testeffekten skal optimaliseres, bør tester utformes med mål om å bidra til dyp prosessering av innlært kunnskap. Testtilbakemeldingers rolle i utdanning kan være å bidra til økt metakognitiv selvregulering blant studenter. For at selvregulering skal promoteres best mulig, bør testtilbakemeldinger inneholde utdypende informasjon. Samlet ser tester og testtilbakemeldinger ut til å være nyttige læringsverktøy og potensielle bidragsytere til dypere læring blant studenter.AbstractThis article discusses research supporting that tests and test feedback are learning tools that may promote deeper learning among students. Testing improves long-term retention of knowledge compared to repeated study, a phenomenon known as the testing effect. In order to promote the testing effect, tests should facilitate deep processing of knowledge among students. Test feedback has the potential to increase students’ metacognitive self-regulation. For this type of metacognition to be enhanced, feedback should contain elaborate information. Conclusively, tests and test feedback appear to be valuable learning tools and potential promoters of deeper learning.

"Tryllefløjten" som musik-historisk allegori

DEFF Research Database (Denmark)

Jensen, Jørgen I.

1991-01-01

Musikhistorie, Mozarts "Tryllefløjten", Mozart som historisk fænomen, oplysningstid, frimureri, musik og religion......Musikhistorie, Mozarts "Tryllefløjten", Mozart som historisk fænomen, oplysningstid, frimureri, musik og religion...

Learning Objects Web

DEFF Research Database (Denmark)

Blåbjerg, Niels Jørgen

2005-01-01

Learning Objects Web er et DEFF projekt som Aalborg Universitetsbibliotek har initieret. Projektet tager afsæt i de resultater og erfaringer som er opnået med vores tidligere projekt Streaming Webbased Information Modules (SWIM). Vi har et internationalt netværk af interessenter som giver os...... sparring og feedback i forhold til udviklingskoncept både omkring de teoretiske rammer og i forhold til praktisk anvendelse af vores undervisningskoncept. Med disse rygstød og input har vi forfulgt ønsket om at videreudvikle SWIM i det nye projekt Learning Objects Web. Udgivelsesdato: juni...

Tema 1: Robotdidaktik. Læringsrobotter som indhold i undervisningen.

Directory of Open Access Journals (Sweden)

Jens Jørgen Hansen

2016-01-01

Full Text Available Robotter er en teknologi som i disse år bevæger sig ind i skolen. Spørgsmålet er med hvilken didaktisk begrundelse og dermed læringsmæssig relevans man kan arbejde med robotter i skolen? Hvorfor skal eleverne beskæftige sig med robotter? Dette spørgsmål er en opgave for didaktikken at svare på, hvilket sætter robotdidaktik på didaktikkens landkort. I artiklen undersøges en række didaktiske designs fra projektet Fremtek,hvor lærere bl.a. har eksperimenteret med at udvikle undervisningsforløb med humanidrobotter – en slags læringsrobotter -­‐ som omdrejningspunkt (Majgaard 2014. Robotters funktion som indhold i disse didaktiske design analyseres ud fra Frede V. Nielsens fire indholdskategorier: indhold som fænomen, som realia og kontekst, som aktivitet og metode og som personligog social erfaringsfelt. Målet er at udvikle en kritisk og kreativ refleksion over og diskussion af robotteknologi som mål og middel i skolen og andre uddannelsesinstitutioner og bidrage til udvikling af en robotdidaktik, der kan støtte og vejlede undervisere i at foretage didaktiske valg og kvalificere eksperimenter med robotter i undervisningen.

Tema 1: Robotdidaktik. Læringsrobotter som indhold i undervisningen.

Directory of Open Access Journals (Sweden)

Jens Jørgen Hansen

2015-12-01

Full Text Available Robotter er en teknologi som i disse år bevæger sig ind i skolen. Spørgsmålet er med hvilken didaktisk begrundelse og dermed læringsmæssig relevans man kan arbejde med robotter i skolen? Hvorfor skal eleverne beskæftige sig med robotter? Dette spørgsmål er en opgave for didaktikken at svare på, hvilket sætter robotdidaktik på didaktikkens landkort. I artiklen undersøges en række didaktiske designs fra projektet Fremtek,hvor lærere bl.a. har eksperimenteret med at udvikle undervisningsforløb med humanidrobotter – en slags læringsrobotter -­‐ som omdrejningspunkt (Majgaard 2014. Robotters funktion som indhold i disse didaktiske design analyseres ud fra Frede V. Nielsens fire indholdskategorier: indhold som fænomen, som realia og kontekst, som aktivitet og metode og som personligog social erfaringsfelt. Målet er at udvikle en kritisk og kreativ refleksion over og diskussion af robotteknologi som mål og middel i skolen og andre uddannelsesinstitutioner og bidrage til udvikling af en robotdidaktik, der kan støtte og vejlede undervisere i at foretage didaktiske valg og kvalificere eksperimenter med robotter i undervisningen.

Rwanda - eller hvor som helst

DEFF Research Database (Denmark)

Sonne, Charlotte Kærgaard; Weis, Rikke Lise

2010-01-01

Sundhedspersonalets rolle under konflikter er ofte et springende punkt, ikke mindst fordi intellektuelle og hermed også uddannet sundhedspersonale ikke sjældent er blandt ofrene i konflikten, som vi har set det med eksemplet i Rwanda.......Sundhedspersonalets rolle under konflikter er ofte et springende punkt, ikke mindst fordi intellektuelle og hermed også uddannet sundhedspersonale ikke sjældent er blandt ofrene i konflikten, som vi har set det med eksemplet i Rwanda....

Impact of ensemble learning in the assessment of skeletal maturity.

Science.gov (United States)

Cunha, Pedro; Moura, Daniel C; Guevara López, Miguel Angel; Guerra, Conceição; Pinto, Daniela; Ramos, Isabel

2014-09-01

The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

Informatikken som utopi

Directory of Open Access Journals (Sweden)

Jørgen Poulsen

1986-08-01

Full Text Available Medieforskerne mødte den nye informationsteknologi med be- tydelig skepsis i begyndelsen af 1980érne. Nu synes pendu- let at være i et andet yderpunkt og utopierne omkring in- formatikkens muligheder blomstrer. Denne artikel forsøger at se informationsteknologien i et socialt perspektiv, i forhold til brugen af andre medier, især TV og radio, og ikke som en kraft "i sig selv". Vores behov for at udføre nye kommunikative handlinger vil være afgørende for infor- matikkens succes. Og her véd vi faktisk moget: I analysen af behovene og deres sociale kontekst (som visse informa- tikutopister synes at glemme synes indsigterne fra de sid- ste 20 års positivestiske og kritiske medieforskning at være brugbare.

Kunst som metode i interaktiv forskning

DEFF Research Database (Denmark)

Jensen, Julie Borup

for at påvirke feltet og skabe ny viden i interaktionsprocesser, bygger dette på en antagelse om, at interaktion mellem mennesker betjener sig af såkaldte medier for kommunikation og læring (Säljö 2003:196, Bruner 1996:76). I denne forståelse kan et medium eksempelvis være sproget, skriften, praksisrutiner, men...... et medium kan også være kunst og kunstværker. Kunstværker kan ses som et udtryk for en given kulturs antagelser om virkeligheden (Bruner 1996:), men også som en provokation eller udfordring til denne virkelighedsopfattelse (Darsø 2004). I forlængelse heraf kan kunst og kunstværker ses som...

Poster som et pædagogisk redskab i sygeplejerskeuddannelsen

DEFF Research Database (Denmark)

Bagger, Bettan; Kelly, Hélène

2008-01-01

Som undervisere har vi gode erfaringer med, at de studerende anvender posters som redskab for formidling af viden. Posterne bruges som udgangspunkt for en mundtlig formidling af faglige perspektiver til de øvrige studerende. Formålet med at anvende formidling ved hjælp af en poster i...

Idrætten som social generator

DEFF Research Database (Denmark)

Kural, René

2002-01-01

Med henvisning til to svømmehaller i hhv. Manchester og Berlin illustrerers det, hvorledes idrætten, store sportslige begivenheder og sportsfaciliteterne kan fungere som social generator for hele byafsnit.......Med henvisning til to svømmehaller i hhv. Manchester og Berlin illustrerers det, hvorledes idrætten, store sportslige begivenheder og sportsfaciliteterne kan fungere som social generator for hele byafsnit....

Tv-fiktion som tværmedial oplevelsesdesign

DEFF Research Database (Denmark)

Sandvik, Kjetil

2010-01-01

konstrueres og et bestemt handlingsforløb simuleres, fra de mere kulørte og parapsykologisk funderede fremgangmetoder i serier som The Profiler og Medium, til mere realistiske praksiser som i Rejseholdet og CSI. Og jeg vil endvidere lave en sammenlignende analyse med virkelighedens gerningsstedsefterforskning...

Videokameraet som etnografisk redskab online og offline

DEFF Research Database (Denmark)

Rehder, Mads Middelboe

2013-01-01

“Med udgangspunkt i et visuelt feltarbejde såvel online som offline, på YouTube og i New York City, beskæftiger dette kapitel sig med kameraet som medieringsteknologi, med fokus på levende billeder. Det diskuteres, hvordan kameraets multiple anvendelsesmuligheder empirisk og metodisk kan skabe...... forskellige muligheder og begrænsninger i et feltarbejde, samt hvordan det visuelle materiale kan variere som resultat af disse forskellige anvendelser. Den indledende del af kapitlet består af en beskrivelse af det empiriske felt online på YouTube, og det diskuteres, hvordan et online felt relaterer sig til...

Queer eller manden som ideal

DEFF Research Database (Denmark)

Sjørup, Karen

2016-01-01

Forestillingen om barnet som den i kønslig forstand renvaskede tavle er fejlagtig og selvmodsigende. I stedet bør forældre og samfund give børn ballast til at begive sig ud i livet uden for mange normative direktiver.......Forestillingen om barnet som den i kønslig forstand renvaskede tavle er fejlagtig og selvmodsigende. I stedet bør forældre og samfund give børn ballast til at begive sig ud i livet uden for mange normative direktiver....

Fællesskab som fællesgørende bevægelse?

DEFF Research Database (Denmark)

Khawaja, Iram

2014-01-01

man kan tale om, og som noget, der konstant forhandles. 3. Fællesskab som situeret og forbundet med subjektivering 4. Fællesskab som hverken overindividuel eller subjektiv bevægelse og 5. Fællesskab som forbundet med belonging. Artiklen bevæger sig imellem teoretisk og empirisk analyse og inddrager et...... fokus på belonging som en central dimension i forståelsen af konstruktionen af fællesskaber....

Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

Science.gov (United States)

Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

2017-03-22

Cues that predict the availability of food rewards influence motivational states and elicit food-seeking behaviors. If a cue no longer predicts food availability, then animals may adapt accordingly by inhibiting food-seeking responses. Sparsely activated sets of neurons, coined "neuronal ensembles," have been shown to encode the strength of reward-cue associations. Although alterations in intrinsic excitability have been shown to underlie many learning and memory processes, little is known about these properties specifically on cue-activated neuronal ensembles. We examined the activation patterns of cue-activated orbitofrontal cortex (OFC) and nucleus accumbens (NAc) shell ensembles using wild-type and Fos-GFP mice, which express green fluorescent protein (GFP) in activated neurons, after appetitive conditioning with sucrose and extinction learning. We also investigated the neuronal excitability of recently activated, GFP+ neurons in these brain areas using whole-cell electrophysiology in brain slices. Exposure to a sucrose cue elicited activation of neurons in both the NAc shell and OFC. In the NAc shell, but not the OFC, these activated GFP+ neurons were more excitable than surrounding GFP- neurons. After extinction, the number of neurons activated in both areas was reduced and activated ensembles in neither area exhibited altered excitability. These data suggest that learning-induced alterations in the intrinsic excitability of neuronal ensembles is regulated dynamically across different brain areas. Furthermore, we show that changes in associative strength modulate the excitability profile of activated ensembles in the NAc shell. SIGNIFICANCE STATEMENT Sparsely distributed sets of neurons called "neuronal ensembles" encode learned associations about food and cues predictive of its availability. Widespread changes in neuronal excitability have been observed in limbic brain areas after associative learning, but little is known about the excitability changes that

Innovativ formidling af førsteårsstuderende som et design-based research-forløb

Directory of Open Access Journals (Sweden)

Ole Eggers Bjælde

2016-11-01

Full Text Available 14 videoproduktioner, 4 tegneserier, 3 filmfortællinger, 2 sange, 2 nyhedsindslag, 2 børnebøger, 2 facebook-sider, 1 novelle og 1 toiletrulle var blandt resultaterne, da 112 førsteårsstuderende i det obligatoriske kursus Astrofysik på fysikuddannelsen på Aarhus Universitet blev deltagere i et learning design-forløb, som en del af deres eksamen i kurset. Målet med forløbet var dels at sætte fokus på faglig formidling som en væsentlig kompetence blandt universitetsstuderende, men samtidig også at bringe værdier som kreativitet og innovation i spil. Forløbet var tilrettelagt efter principperne for design-based research, og publikationen her sætter fokus på intention, implementering, realisering og perspektivering af det underliggende design med henblik på forbedring af designet til fremtidig brug samt på en vurdering af forløbets samlede impact.

Musikterapi som samværsform

DEFF Research Database (Denmark)

Ridder, Hanne Mette Ochsner

2005-01-01

Det kan være svært for mennesker med omfattende demenssymptomer at deltage i aktiviteter eller samvær med andre – og uden interaktion med andre kan det være svært at få dækket psykosociale behov. Der er derfor brug for samvær og aktiviteter som er specielt tilrettelagt for at opfylde psykosociale...... erfaringer indenfor demensområdet. Der vil i foredraget blive taget udgangspunkt i praksiserfaring som musikterapeut på Plejehjemmet Caritas samt i et forskningsprojekt om individuel musikterapi, hvorfra der bliver vist videoeksempler....

Fænomenologisk dekonstruktion som udspørgning

DEFF Research Database (Denmark)

Keller, Kurt Dauer

Merleau-Ponty skelner udspørgning (interrogation) fra de dominerende opfattelser af refleksion, dialektik og intuition. Udspørgning må betragtes som hans bud på den begrebs- og traditionskritik, Heidegger kaldte ”destruktion”, og som Derrida førte videre under den mere adækvate betegnelse...

A stacking ensemble learning framework for annual river ice breakup dates

Science.gov (United States)

Sun, Wei; Trevor, Bernard

2018-06-01

River ice breakup dates (BDs) are not merely a proxy indicator of climate variability and change, but a direct concern in the management of local ice-caused flooding. A framework of stacking ensemble learning for annual river ice BDs was developed, which included two-level components: member and combining models. The member models described the relations between BD and their affecting indicators; the combining models linked the predicted BD by each member models with the observed BD. Especially, Bayesian regularization back-propagation artificial neural network (BRANN), and adaptive neuro fuzzy inference systems (ANFIS) were employed as both member and combining models. The candidate combining models also included the simple average methods (SAM). The input variables for member models were selected by a hybrid filter and wrapper method. The performances of these models were examined using the leave-one-out cross validation. As the largest unregulated river in Alberta, Canada with ice jams frequently occurring in the vicinity of Fort McMurray, the Athabasca River at Fort McMurray was selected as the study area. The breakup dates and candidate affecting indicators in 1980-2015 were collected. The results showed that, the BRANN member models generally outperformed the ANFIS member models in terms of better performances and simpler structures. The difference between the R and MI rankings of inputs in the optimal member models may imply that the linear correlation based filter method would be feasible to generate a range of candidate inputs for further screening through other wrapper or embedded IVS methods. The SAM and BRANN combining models generally outperformed all member models. The optimal SAM combining model combined two BRANN member models and improved upon them in terms of average squared errors by 14.6% and 18.1% respectively. In this study, for the first time, the stacking ensemble learning was applied to forecasting of river ice breakup dates, which appeared

Brands som ideologiske parasitter

DEFF Research Database (Denmark)

Hermansen, Judy

2009-01-01

De store, stærke brands, som rager op over alle andre som repræsentanter for nogle værdier, der bliver lagt vægt på i samfundet, kalder Douglas Holt for ikoniske. Vi snakker Coca Cola, Nike, Apple ligaen. For at opnå en sådan status er brandingen nødt til at holde et vågent øje med tendenser og...

New technologies for examining the role of neuronal ensembles in drug addiction and fear.

Science.gov (United States)

Cruz, Fabio C; Koya, Eisuke; Guez-Barber, Danielle H; Bossert, Jennifer M; Lupica, Carl R; Shaham, Yavin; Hope, Bruce T

2013-11-01

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. In addition, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches--Daun02 inactivation, FACS sorting of activated neurons and Fos-GFP transgenic rats--that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools--Fos-tTA transgenic mice and inactivation of CREB-overexpressing neurons--that have been used to study the role of neuronal ensembles in conditioned fear.

Selvskade som selvteknologi

DEFF Research Database (Denmark)

Rasmussen, Anna Lanken; Rasmussen, Svend Aage

2016-01-01

livshandlinger” – selvskade som social lidelse. Herefter argumenterer forfatterne med udgangspunkt i et feltstudie af Helen Gremillion for vigtigheden af at kaste et kritisk blik på behandlingskultur. Denne kritik videreføres i et afsnit om den postmoderne udfordring i forhold til selvskade. Afslutningsvis...

Online cross-validation-based ensemble learning.

Science.gov (United States)

Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark

2018-01-30

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Representing Color Ensembles.

Science.gov (United States)

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-10-01

Colors are rarely uniform, yet little is known about how people represent color distributions. We introduce a new method for studying color ensembles based on intertrial learning in visual search. Participants looked for an oddly colored diamond among diamonds with colors taken from either uniform or Gaussian color distributions. On test trials, the targets had various distances in feature space from the mean of the preceding distractor color distribution. Targets on test trials therefore served as probes into probabilistic representations of distractor colors. Test-trial response times revealed a striking similarity between the physical distribution of colors and their internal representations. The results demonstrate that the visual system represents color ensembles in a more detailed way than previously thought, coding not only mean and variance but, most surprisingly, the actual shape (uniform or Gaussian) of the distribution of colors in the environment.

Varehusbesøget som fornøjeligt hverdagsdrama

DEFF Research Database (Denmark)

Bouchet, Julie Kirstine Linderoth; Jantzen, Christian

2014-01-01

Artiklen analyserer de koreografiske, scenografiske og dramaturgiske virkemidler, som IKEA har anvendt til at skabe et succesfuldt oplevelsesdesign.......Artiklen analyserer de koreografiske, scenografiske og dramaturgiske virkemidler, som IKEA har anvendt til at skabe et succesfuldt oplevelsesdesign....

An Ensemble Approach in Converging Contents of LMS and KMS

Science.gov (United States)

Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay

2017-01-01

Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the…

Digitalisering som kulturpolitik

DEFF Research Database (Denmark)

Lund, Niels Dichov

2009-01-01

Begreberne kulturarv, digitalisering og formidling drøftes generelt som væsentlige nutidsagendaer i lyset af en treeninghed af teknologi, demokrati og kultur. Digital formidling af kulturarv belyses i forhold til en længere dobbbeltsporet forskningskontekst mellem IKT og humaniora og ses primært...

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

Science.gov (United States)

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

IKT som empowerment af lærer og elever!

DEFF Research Database (Denmark)

Christiansen, René B.; Binggeli, Andreas

2011-01-01

I denne korte artikel ønsker vi at pege på at ikt kan fungere som empowerment for både lærere og elever i skolen. It optræder i forvejen i alt for mange menneskers bevidsthed som en barriere eller som en sten i den pædagogiske sundhedssandal. Vi har begge på egen krop oplevet at arbejdet med ny t...

Kan Batman dateres? Om massekommunikasjonen som kulturhistorie

Directory of Open Access Journals (Sweden)

Hans Frederik Dahl

1990-08-01

Full Text Available Hans Frederik Dahls artikel er et bud på om massekommunikationen kan tænkes ind i kulturhistorien.Første halvdel af artiklen fremhæver de prob- lemer, det rejser, når massekommunikation med dens karakteristika i form af serialitet, triviatitet og internationalitet skal reflekteres i forhold til en kulturhistorisk tradition, som normalt tænker i begreber som tidsånd, kunstnerisk originalitet og nationalt særpræg. Sidste halvdel focuserer på tre centrale aspekter ved massekommunikationen, som også er af- gørende for dens kulturhistorie: dens nære forbindelse til musikken, dens internationale karakter og dens konstante tilbagevenden til tidligere pro- dukter.

Kan Batman dateres? Om massekommunikasjonen som kulturhistorie

Directory of Open Access Journals (Sweden)

Hans Frederik Dahl

1990-06-01

Full Text Available Hans Frederik Dahls artikel er et bud på om massekommunikationen kan tænkes ind i kulturhistorien.Første halvdel af artiklen fremhæver de prob- lemer, det rejser, når massekommunikation med dens karakteristika i form af serialitet, triviatitet og internationalitet skal reflekteres i forhold til en kulturhistorisk tradition, som normalt tænker i begreber som tidsånd, kunstnerisk originalitet og nationalt særpræg. Sidste halvdel focuserer på tre centrale aspekter ved massekommunikationen, som også er af- gørende for dens kulturhistorie: dens nære forbindelse til musikken, dens internationale karakter og dens konstante tilbagevenden til tidligere pro- dukter.

Om filmteori og filmhistorie: Stumfilmen som eksempel

Directory of Open Access Journals (Sweden)

Casper Tybjerg

1995-09-01

Full Text Available Tilbage til filmene og filmmagerne siger Casper Tybjerg i denne artikel. Han plæderer dog ikke nødvendigvis for en tilbagevending til "gammel- dags filmhistorieskrivning" og til en fokusering kun på film som æstetik og kunst. Med udgangspunkt i konkrete overvejelser over den danske stumfilm og gennemgang af væsentlige nyere filmhistoriske værker og teorier polemiserer han mod det han opfatter som upræcise og meget abstrakte kulturalistiske positioner, som enten glemmer de konkrete film eller overbetoner det blot formelle i filmhistorien. Samtidig tegner han også et levende billede af den kultur og de betingelser den tidlige film blev til i.

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

Science.gov (United States)

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as

Kierkegaard som eksistentiel fænomenolog

DEFF Research Database (Denmark)

Kierkegaard som eksistentiel fænomenolog er en antologi, som fremhæver de fænomenologiske aspekter i Kierkegaards beskrivelser af de grundlæggende fænomener i den menneskelige tilværelse. Den fokuserer på Kierkegaards anskuelse af mennesket som en konkret eksisterende person, et tænkende, følende...... og handlende subjekt, der altid har til opgave at forholde sig til sig selv, sin omverden og de andre. Bogen behandler for det første Kierkegaards eget fænomenologiske projekt og hans analyser af angst, fortvivlelse, blufærdighed, samvittighed, selvbedrag og autenticitet. For det andet hans forhold...... til samtidens eksistentielle tænkning (bl.a. Schelling og Poul Martin Møller) og ikke mindst hans forhold til eftertidens eksistentielle tænkning (bl.a. Jaspers, Wittgenstein, Agampen og Zisek). Og for det tredje Kierkegaards betydning for den senere eksistentielle psykologi og psykiatri....

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

Directory of Open Access Journals (Sweden)

Xin Wang

2018-02-01

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

Evaluation of Strontium Selectivity by Sandia Octahedral Molecular Sieves (SOMS).

Energy Technology Data Exchange (ETDEWEB)

Rigali, Mark J.; Stewart, Thomas Austin

2016-01-01

Sandia National Laboratories has collaborated with Pleasanton Ridge Research Company (PRRC) to determine whether Sandia Octahedral Molecular Sieves (SOMS) and modified SOMs materials can be synthesized in large batches and produced in granular form. Sandia National Laboratories tested these SOMS and its variants based in aqueous chemical environments for an application-based evaluation of material performance as a sorbent. Testing focused primarily on determining the distribution coefficients (K _d ) and chemical selectivity SOMs for alkali earth (Sr) ions in aqueous and dilute seawater solutions. In general the well-crystallized SOMS materials tested exhibited very high K d values (>10 ⁶ ) in distilled water but K _d values dropped substantially (%7E10 ² -10 ³ ) in the dilute seawater (3%). However, one set of SOMS samples (1.4.2 and 1.4.6) provided by PRRC yielded relatively high K d (approaching 10 ⁴ ) in dilute seawater. Further examination of these samples by scanning electron microscopy (SEM) revealed the presence of at least two phases at least one of which may be accounting for the improved K d values in dilute seawater. Evaluation of Strontium Selectivity by Sandia Octahedral Molecular Sieves (SOMS) January 20, 2016

Multi-SOM: an Algorithm for High-Dimensional, Small Size Datasets

Directory of Open Access Journals (Sweden)

Shen Lu

2013-04-01

Full Text Available Since it takes time to do experiments in bioinformatics, biological datasets are sometimes small but with high dimensionality. From probability theory, in order to discover knowledge from a set of data, we have to have a sufficient number of samples. Otherwise, the error bounds can become too large to be useful. For the SOM (Self- Organizing Map algorithm, the initial map is based on the training data. In order to avoid the bias caused by the insufficient training data, in this paper we present an algorithm, called Multi-SOM. Multi-SOM builds a number of small self-organizing maps, instead of just one big map. Bayesian decision theory is used to make the final decision among similar neurons on different maps. In this way, we can better ensure that we can get a real random initial weight vector set, the map size is less of consideration and errors tend to average out. In our experiments as applied to microarray datasets which are highly intense data composed of genetic related information, the precision of Multi-SOMs is 10.58% greater than SOMs, and its recall is 11.07% greater than SOMs. Thus, the Multi-SOMs algorithm is practical.

Motivation og refleksion i e-learning

DEFF Research Database (Denmark)

Majgaard, Gunver; Thisted, Anni

2009-01-01

, spilelementer (serious gaming) og opslugthed (flow) og dermed et grundlag for at stimulere refleksion og motivation. Målet med artiklen er at give inspiration til praktikere, som udvikler e-learning til individuelt brug, og som ønsker at gøre e-learning engagerende og motiverende og samtidig opnå væsentlig...... læringsdybde.   Artiklen giver en introduktion til udvalgte teoretikere, som arbejder med motivation og opslugthed og er krydret med eksempler fra en undersøgelse af en individuel e-learning-applikation fra den finansielle sektor, som understøtter social stimulation. Applikationen er udviklet med henblik på......Hvad sker der med motivation og refleksion, når individuel læring understøttes af digitale medier? Individuel læring giver en række begrænsninger på grund af manglende samspil med andre kursister, men samtidig giver digitale læringsmedier muligheder for bevidst at arbejde med læringsstile...

The Ensembl REST API: Ensembl Data for Any Language.

Science.gov (United States)

Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

2015-01-01

We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.

Industrirobotten som servomekanisme

DEFF Research Database (Denmark)

Sørensen, Torben

2001-01-01

). Bevægeleddene er typisk enten roterende drejeled, eller lineære forskydningsled. Rotationen eller forskydningen mellem to bevægeled varetages af en servostyret motor. Selve robotarmen er forbundet til robottens base gennem et bevægeled (servoakse) som bærer hele armen - det vil sige alle armens elementer og...

Krisen som udfordring

DEFF Research Database (Denmark)

Højrup, Thomas

2009-01-01

Om Thorupstrand Kystfiskerlaug, Han Herred Puljefiskeri og Han Herred Havbådes innovative bidrag til en samlet indsats for at sikre kystfiskeriet i Skagerrak fra åben strand som en levende og håndværksbåret kystkultur i Danmark. Den venturekapitalistiske højkonjunktur, regeringens privatiseringsp...

Det nordiske som brandværdi

DEFF Research Database (Denmark)

Hermansen, Judy

2009-01-01

Kommunikation spiller en vigtig rolle for, hvilke associationer forbrugeren har til brandet. Og da brand image består af summen af alle corporate aktiviteter, er der ifølge Roncha især én fællesnævner, som styrker strategierne bag nordisk brand management: Opbygningen af et globalt og konsistent...... brand under en nordisk designparaply, fordi design i mange år reelt har været en integreret del af det kulturelle og samfundsmæssige liv i Skandinavien. Men ikke alle nordiske designere, er tilfredse med de stilistiske associationer og begrænsninger, som er vokset op omkring det nordiske brand. Derfor...... er det også vigtigt at arbejde aktivt på at udvikle dets symbolske værdi. Der er ifølge Roncha en unik chance for at skabe et bæredygtigt brand, som ikke kun handler om bestemte stilarter eller kunstneriske værdier, men også om kvalitet og innovation. Udgivelsesdato: April...

Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.

Science.gov (United States)

Awad, Aya; Bader-El-Den, Mohamed; McNicholas, James; Briggs, Jim

2017-12-01

Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission. This study highlights the main data challenges in early mortality prediction in ICU patients and introduces a new machine learning based framework for Early Mortality Prediction for Intensive Care Unit patients (EMPICU). The proposed method is evaluated on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. Mortality prediction models are developed for patients at the age of 16 or above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU). We employ the ensemble learning Random Forest (RF), the predictive Decision Trees (DT), the probabilistic Naive Bayes (NB) and the rule-based Projective Adaptive Resonance Theory (PART) models. The primary outcome was hospital mortality. The explanatory variables included demographic, physiological, vital signs and laboratory test variables. Performance measures were calculated using cross-validated area under the receiver operating characteristic curve (AUROC) to minimize bias. 11,722 patients with single ICU stays are considered. Only patients at the age of 16 years old and above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU) are considered in this study. The proposed EMPICU framework outperformed standard scoring systems (SOFA, SAPS-I, APACHE-II, NEWS and qSOFA) in terms of AUROC and time (i.e. at 6h compared to 48h or more after admission). The results show that although there are many values missing in the first few hour of ICU admission

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Corporate film som værktøj til employer branding

DEFF Research Database (Denmark)

Hansen, Heidi

2016-01-01

En corporate film kan ses som organisationens autoritative tekst kommunikeret i en enkelt digital fortælling. I denne artikel belyses corporate film som genre fra henholdsvis kommunikationsdirektørens, forskerens, det kreative bureau og filmskaberens side.......En corporate film kan ses som organisationens autoritative tekst kommunikeret i en enkelt digital fortælling. I denne artikel belyses corporate film som genre fra henholdsvis kommunikationsdirektørens, forskerens, det kreative bureau og filmskaberens side....

Insulin som trickster

DEFF Research Database (Denmark)

Lassen, Aske Juul

2011-01-01

grænser nedbrydes i en konstant penetrering af huden, når blodsukkeret måles eller insulinen indsprøjtes. Insulin analyseres som en tricksterfigur, der udøver et grænsearbejde på kroppen, leger med dens kategorier og vender forholdet mellem gift og medicin, frihed og ufrihed, kunstighed og naturlighed...

Kroppen som monitor

DEFF Research Database (Denmark)

Filges, Tine

2005-01-01

Tine Filges tager fat i de unges brug og forhold til porno, og har som en central pointe, at unge langt fra er en homogen gruppe. Der er væsentlige forskelle i unges forhold til deres krop. Dette lyder måske banalt og indlysende, men det er en pointe, der ofte glemmes i den offentlige debat om unge...... og deres kroppe. Abstract: Unges kroppe forbindes ofte med risici og farer. Vi lurer farer alle steder, og medierne er fulde af overskrifter: Fedmeepidemier. Unge med åreforkalkning. Druk. Porno. Spiseforstyrrelser. Selvskadende adfærd. I takt med at kroppen monitoreres, er den i stigende grad blevet...... koloniseret af en bekymret hær af forskere, forældre, pædagoger og terapeuter. Vi spørger ængsteligt: Vil de unge lave deres krop om livet igennem? Vil de virkelig have plastik i brysterne? Vil den næste generation tage Viagra som 18-årige? Kan unge indgå i intime relationer, når de er vokset op med porno på...

Tweet-based Target Market Classification Using Ensemble Method

Directory of Open Access Journals (Sweden)

Muhammad Adi Khairul Anshary

2016-09-01

Full Text Available Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting. To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras and three categories of sentiments (positive, negative and neutral were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%. On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.

Visualization and classification of physiological failure modes in ensemble hemorrhage simulation

Science.gov (United States)

Zhang, Song; Pruett, William Andrew; Hester, Robert

2015-01-01

In an emergency situation such as hemorrhage, doctors need to predict which patients need immediate treatment and care. This task is difficult because of the diverse response to hemorrhage in human population. Ensemble physiological simulations provide a means to sample a diverse range of subjects and may have a better chance of containing the correct solution. However, to reveal the patterns and trends from the ensemble simulation is a challenging task. We have developed a visualization framework for ensemble physiological simulations. The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data to existing ensembles. We demonstrated the effectiveness of the visualization on simulated physiological data. The lessons learned here can be applied to clinically-collected physiological data in the future.

Music playlist generation by assimilating GMMs into SOMs

NARCIS (Netherlands)

Balkema, Wietse; van der Heijden, Ferdinand

A method for music playlist generation, using assimilated Gaussian mixture models (GMMs) in self organizing maps (SOMs) is presented. Traditionally, the neurons in a SOM are represented by vectors, but in this paper we propose to use GMMs instead. To this end, we introduce a method to adapt a GMM

Om dass-sætninger som afficeret eller efficeret objekt

DEFF Research Database (Denmark)

Holsting, Alexandra

2013-01-01

Temaet for nærværende artikel er en diskussion af muligheden for at fastlægge en tysk dass-sætnings semantiske funktion som afficeret eller efficeret objekt , når den optræder i rollen som komplement for verber, der har såvel en afficerende som en efficerende betydningsvariant, hvorved dass......-sætningen i det første tilfælde betegner et forhold, matrixsætningens subjektsreferent reagerer på, og i det andet indholdet af en ytring, der fremsættes af matrixsætningens subjektsreferent. Jeg går i artiklen særligt ind i diskussionen om, hvorvidt dass-sætningens modus kan tjene som formel indikation på...... den semantiske funktion, dvs. om en dass-sætning i konjunktiv utvetydigt kan tilskrives en funktion som efficeret objekt for matrixsætningsverbet....

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

Science.gov (United States)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

Ole Rømer som enevældens administrator

DEFF Research Database (Denmark)

Olden-Jørgensen, Sebastian

2002-01-01

En gennemgang af Ole Rømers liv med vægt på hans rolle som embedsmand og hans deltagelse eller mangel på samme i det politiske spil.......En gennemgang af Ole Rømers liv med vægt på hans rolle som embedsmand og hans deltagelse eller mangel på samme i det politiske spil....

Sprog som musikalsk notation

DEFF Research Database (Denmark)

Bergstrøm-Nielsen, Carl

Artiklen behandler sprogbrug som supplement til noderne fra barokken og frem; Fluxus og Scratch Orchestra; den engelske Verbal Anthology; Stockhausens brug af verbal notation før tekst-samlingerne; detaljerede analyser af de to samlinger. Der konkluderes bla. om forskellige grader af fastlæggelse...

Distribution of peptidergic populations in the human dentate gyrus (somatostatin [SOM-28, SOM-12] and neuropeptide Y [NPY]) during postnatal development.

Science.gov (United States)

Cebada-Sánchez, S; Insausti, R; González-Fuentes, J; Arroyo-Jiménez, M M; Rivas-Infante, E; Lagartos, M J; Martínez-Ruiz, J; Lozano, G; Marcos, P

2014-10-01

The postnatal development of the human hippocampal formation establishes the time and place at which we start autobiographical memories. However, data concerning the maturation of the neurochemical phenotypes characteristic of interneurons in the human hippocampus are scarce. We have studied the perinatal and postnatal changes of the dentate gyrus (DG) interneuron populations at three rostrocaudal levels. Immunohistochemically identified neurons and fibers for somatostatin (SOM-12 and SOM-28) and neuropeptide Y (NPY) and the co-localization of SOM-28 and NPY were analyzed. In total, 13 cases were investigated from late pregnancy (1 case), perinatal period (6 cases), first year (1 case), early infancy (3 cases), and late infancy (2 cases). Overall, the pattern of distribution of these peptides in the DG was similar to that of the adult. The distribution of cells was charted, and the cell density (number of positive cells/mm(2)) was calculated. The highest density corresponded to the polymorphic cell layer and was higher at pre- and perinatal periods. At increasing ages, neuron density modifications revealed a decrease from 5 postnatal months onward. In contrast, by late infancy, two immunoreactive bands for SOM-28 and NPY in the molecular layer were much better defined. Double-immunohistochemistry showed that NPY-positive neurons co-localized with SOM-28, whereas some fibers contained only one or other of the neuropeptides. Thus, this peptidergic population, presumably inhibitory, probably has a role in DG maturation and its subsequent functional activity in memory processing.

E-Læring som pedagogisk virkemiddel for innlæring av anatomi, fysiologi og biokjemi i sykepleierutdanningen

Directory of Open Access Journals (Sweden)

Mona Elisabeth Meyer

2014-02-01

Full Text Available Utgangspunktet for studien er at naturvitenskaplige fag, særlig anatomi, fysiologi og biokjemi (AFB, oppleves som vanskelig for sykepleierstudentene. Hensikten var å undersøke om læringsverktøyet e-læring, i form av nettester, kan være et effektivt virkemiddel til innlæring av og påvirke læringsutbyttet i AFB.Førsteårsstudentene i bachelorutdanningen i sykepleie ved Høgskolen i Akershus fikk et spørreskjema med 23 spørsmål som ble besvart anonymt.Resultatet av studentundersøkelsen viste at nettoppgavene ble mye brukt, og at studentene mente de ga et godt læringsutbytte. Nettoppgaver med umiddelbar feedback gir stor studentaktivitet og «time-on-task». Testene kan tette hullet mellom det studentene forventes å kunne og det de faktisk kan, og egner seg til faktafag som m å pugges og forstås. E-læring kan gi god hjelp til studenters strukturering av tid, læringsstrategier og selvregulering.Læringsutbyttet av nettester diskuteres i lys av nytten av umiddelbar feedback i et pedagogisk perspektiv.AbstractThe starting point of the study is that the natural sciences, particularly anatomy, physiology and biochemistry (collectively known as APB, are often difficult subjects for nursing students. The aim was to investigate whether e-learning tools, in terms of web services, can be an effective educational tool and affect learning outcomes in APB.First year students in the Bachelor's degree program in nursing at Akershus University College of Applied Sciences received a questionnaire with 23 questions, which were answered anonymously.The results of the student survey showed that online tasks were widely used, and that students felt they resulted in good learning outcomes. Online training with immediate feedback shows great student activity and “time-on-task.” These tests can close the gap between what the students are expected to know and what they actually do, and is suitable for factual subjects requiring both memorization and

Schelling og Nietzsche som kritikere af Descartes

DEFF Research Database (Denmark)

Liisberg, Sune

2008-01-01

: 'Hvad er det første for mig' - og svaret var: 'Det er jeg selv'. Cogito-argumentet, der implicerer en forudgående beslutning om at drage alt i tvivl, manifesterer således en historisk enestående 'frigørelse fra al autoritet' (Schelling). Ifølge Schelling - der ligesom Schopenhauer og Nietzsche havde...... blik for, at cogito-argumentet og dets problemer har med dets sprogbundethed at gøre - er ytringsdimensionen nu helt afgørende for cogito-argumentets meningsfuldhed, da allerede 'den blotte tanke 'Jeg er' kan forstås som en indre ytring og forudsætter et talende jeg'. Nietzsche henleder opmærksomheden...... på, at 'eftersom Descartes formulerer cogito-argumentet sprogligt, kan det ikke være nogen umiddelbar, første erkendelse'. Nietzsche antager nemlig, at Descartes selv fejlagtigt betragtede cogito-argumentet som en umiddelbar vished - altså strengt taget ikke som et argument. Men Descartes blev...

Portræt af litteraturkritikeren som ungt menneske

DEFF Research Database (Denmark)

Schatz-Jakobsen, Claus

2004-01-01

Artiklen præsenterer den amerikanske litteraturkritiker Geoffrey Hartmans tidlige forfatterskab og fremdrager dets selvbiografiske undertekst som nødvendig effekt af den unge Hartmans fascination af den engelske romantiker William Wordsworths forfatterskab.......Artiklen præsenterer den amerikanske litteraturkritiker Geoffrey Hartmans tidlige forfatterskab og fremdrager dets selvbiografiske undertekst som nødvendig effekt af den unge Hartmans fascination af den engelske romantiker William Wordsworths forfatterskab....

Knowledge-based approach for functional MRI analysis by SOM neural network using prior labels from Talairach stereotaxic space

Science.gov (United States)

Erberich, Stephan G.; Willmes, Klaus; Thron, Armin; Oberschelp, Walter; Huang, H. K.

2002-04-01

Among the methods proposed for the analysis of functional MR we have previously introduced a model-independent analysis based on the self-organizing map (SOM) neural network technique. The SOM neural network can be trained to identify the temporal patterns in voxel time-series of individual functional MRI (fMRI) experiments. The separated classes consist of activation, deactivation and baseline patterns corresponding to the task-paradigm. While the classification capability of the SOM is not only based on the distinctness of the patterns themselves but also on their frequency of occurrence in the training set, a weighting or selection of voxels of interest should be considered prior to the training of the neural network to improve pattern learning. Weighting of interesting voxels by means of autocorrelation or F-test significance levels has been used successfully, but still a large number of baseline voxels is included in the training. The purpose of this approach is to avoid the inclusion of these voxels by using three different levels of segmentation and mapping from Talairach space: (1) voxel partitions at the lobe level, (2) voxel partitions at the gyrus level and (3) voxel partitions at the cell level (Brodmann areas). The results of the SOM classification based on these mapping levels in comparison to training with all brain voxels are presented in this paper.

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

Science.gov (United States)

Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

2017-03-01

Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Security Enrichment in Intrusion Detection System Using Classifier Ensemble

Directory of Open Access Journals (Sweden)

Uma R. Salunkhe

2017-01-01

Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

Børn med diagnoser som pædagogisk-psykologisk udfordring

DEFF Research Database (Denmark)

Bøttcher, Louise

2013-01-01

hovedsageligt vanskelighederne ud fra en social vinkel. Den artikels dialektiske balancegang mellem de to positioner vil udfolde en forståelse af diagnoser som ADHD eller autismespektrumforstyrrelser, som tager udgangspunkt i at en eller flere biologiske afvigelser meget vel kan være den oprindelige årsag til...... hvordan uoverensstemmelsen mellem barnet og omgivelserne skaber problemer og udfordringer, kan diagnosen blive nyttig. Nemlig som dialektisk redskab til at analysere de psykologiske og pædagogiske udviklingsmæssige konsekvenser af at have en psykisk udviklingsforstyrrelse af typen ASD eller ADHD – frem...

Netbaserede uddannelser og blended learning

DEFF Research Database (Denmark)

Bertelsen, Jesper Vedel; Vognsgaard Hjernø, Henriette; Jensen, Michael Peter

2016-01-01

Denne håndbog er tænkt som inspiration til uddannelsesfaglige medarbejdere, som er eller skal i gang med at undervise på en netbaseret uddannelse i UCL. Håndbogen giver et teoretisk overblik i forhold til netbaserede uddannelser, online- og blended learning samt en indførsel i hvilke didaktiske...

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

Energy Technology Data Exchange (ETDEWEB)

Potter, Kristin C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brunhart-Lupo, Nicholas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bush, Brian W [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Gruchalla, Kenny M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bugbee, Bruce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-10-09

We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

Turistkort som geografisk kommunikation

DEFF Research Database (Denmark)

Nielsen, Niels Christian

2009-01-01

  Kort har længe spillet en central rolle i turisters ferieplanlægning og aktiviteter på feriestedet, og forventes at gøre det i fremtiden, suppleret med andre kilder til geografisk information. Men først i forbindelse med introduktionen af GIS og mobile applikationer som telefon og GPS er den...

Som at noget lettede

DEFF Research Database (Denmark)

Berliner, Peter; Glendøs, Mia

2010-01-01

Den høje voldsrate i Kalaallit Nunaat (Grønland) forklares ofte ud fra alkoholmisbrug og affektive handlinger. I denne artikel vises det gennem en kvantitativ og kvalitativ undersøgelse, at den episodiske vold må forstås som symptomer på strukturel vold. Den strukturelle vold er en kontekst, der ...

Interaktiv forskning som samarbejdsdrevet innovation

DEFF Research Database (Denmark)

Martinez, Laia

2012-01-01

Hovedformålet med denne artikel er at formidle nogle af de metodiske overvejelser, som jeg har gjort mig om samspillet mellem forskere og praktikere i forbindelse med gennemførelsen af mit Ph.d.-projekt om innovationsledelse i danske kommuner....

Gruppen som container og potentielt læringsrum gennem refleksion

DEFF Research Database (Denmark)

Søbirk, Helle Henni

2012-01-01

Al psykomotorisk intervention foregår i en relation mellem den psykomotoriske terapeut og individ eller gruppe. Derfor anses relations- og refleksionskompetencer som fagpersonlige kernekompetencer i den psykomotoriske praksis og er et centralt formål med "anvendte grupper" som hhv. udviklings-, t...

A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines

Energy Technology Data Exchange (ETDEWEB)

Meza, Juan C.; Woods, Mark

2009-12-18

Machine or statistical learning is a growing field that encompasses many scientific problems including estimating parameters from data, identifying risk factors in health studies, image recognition, and finding clusters within datasets, to name just a few examples. Statistical learning can be described as 'learning from data' , with the goal of making a prediction of some outcome of interest. This prediction is usually made on the basis of a computer model that is built using data where the outcomes and a set of features have been previously matched. The computer model is called a learner, hence the name machine learning. In this paper, we present two such algorithms, a support vector machine method and a rule ensemble method. We compared their predictive power on three supernova type 1a data sets provided by the Nearby Supernova Factory and found that while both methods give accuracies of approximately 95%, the rule ensemble method gives much lower false negative rates.

CEMSKUM - Anvendelighed som isoleringsmateriale i bygninger

DEFF Research Database (Denmark)

Hansen, Ernst Jan de Place

2002-01-01

Resume af rapport om mulighederne for at anvende Cemskum som isoleringsmateriale i bygningskonstruktioner, udarbejdet af Cemsystems I/S under Energistyrelsens udviklingsprogram "Miljø- og arbejdsmiljøvenlig isolering"...

SVM and SVM Ensembles in Breast Cancer Prediction.

Science.gov (United States)

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

SVM and SVM Ensembles in Breast Cancer Prediction.

Directory of Open Access Journals (Sweden)

Min-Wei Huang

Full Text Available Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

«Halvdød av frost, men levende og beredt». Speiderbevegelsen som disiplineringsprosjekt

OpenAIRE

Schaanning, Espen

2013-01-01

I det som gjerne betegnes som speidernes Bibel, Scouting for Boys (1908), løftet speiderbevegelsens grunnlegger, Robert Baden-Powell (1857–1941), fram personer som alle speidergutter burde ha som forbilder. Et slikt forbilde beskrives slik: «Den romerske soldaten fra gamle dager, som ble stående på sin post da Pompeii ble begravd av lava og aske fra vulkanen Vesuv, viste lojalitet mot sin plikt. De har funnet liket hans og kan se hvordan han holdt hånden foran munn og nese for ikke å bli kval...

Cooperative Learning i voksenundervisningen – planlægning og gennemførelse af dynamiske og samarbejdende læreprocesser

DEFF Research Database (Denmark)

Nielsen, Lone; Christensen, Lars

2018-01-01

Om cooperative learning som læringsteknologi i voksenundervisningen, blikke fra underviser- og deltagerpespektiv......Om cooperative learning som læringsteknologi i voksenundervisningen, blikke fra underviser- og deltagerpespektiv...

Ensemble manifold regularization.

Science.gov (United States)

Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng

2012-06-01

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

Lekfull kreativitet. Fysiska användargränssnitt som erbjuder social och fysisk interaktion

DEFF Research Database (Denmark)

Brooks, Eva Irene

2017-01-01

I den här artikeln presenteras en studie som ägde rum under två år i fem olika förskolor i Danmark och som inkluderade 55 barn. Specifikt undersökte vi hur fysiska användargränssnitt kan stötta social och fysisk interaktion. Studien applicerade en design-baserad metodologi som följde en iterativ,......, cyklisk process. Analysen visade att fysiska gränssnitt som främjar utforskande aktiviteter, verbala interaktioner och samarbete har en potential att erbjuda lekfulla lärande situationer som understödjer fria men också guidade aktiviteter....

Peer-Teaching in the Secondary Music Ensemble

Science.gov (United States)

Johnson, Erik

2015-01-01

Peer-teaching is an instructional technique that has been used by teachers world-wide to successfully engage, exercise and deepen student learning. Yet, in some instances, teachers find the application of peer-teaching in large music ensembles at the secondary level to be daunting. This article is meant to be a practical resource for secondary…

Kongehuset som varemærke og nationalt symbol

DEFF Research Database (Denmark)

Ravn Sørensen, Anders

2016-01-01

Kongehuset er ikke bare et nationalt symbol. Det er også et varemærke, som der er al mulig grund til at beskytte og vogte nidkært over. Kronen har nemlig god brandmæssig værdi.......Kongehuset er ikke bare et nationalt symbol. Det er også et varemærke, som der er al mulig grund til at beskytte og vogte nidkært over. Kronen har nemlig god brandmæssig værdi....

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

Directory of Open Access Journals (Sweden)

Stella Fabio

2011-05-01

Full Text Available Abstract Background Molecular dynamics (MD simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering. Results The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions. Conclusions The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

Science.gov (United States)

2011-01-01

Background Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering. Results The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions. Conclusions The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from

Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

Directory of Open Access Journals (Sweden)

Xiaowei Zhao

2013-01-01

Full Text Available Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed.

On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

KAUST Repository

Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.

2010-01-01

However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

Validering som kommunikationsfärdighet till par där kvinnan har vaginism

OpenAIRE

Bernling, Marit; Munck af Rosenschöld, Lisa

2009-01-01

Vaginism är ett sexuellt problem som förekommer hos kvinnor och som i tidigare utvärderade behandlingar betraktats som kvinnans problem. Forskning inom området vaginism är begränsad och kunskap om parrelationens roll för vidmakthållandet av problematiken och upplevd relationstillfredsställelse saknas. Studiens syfte var att utforma en parbehandling där kommunikationsinterventionen validering lärs ut som färdighet, utforska om det fanns ett intresse för deltagande, samt utvärdera om behandling...

Safety evaluation and bacterial community of kung-som using PCR-DGGE technique

Directory of Open Access Journals (Sweden)

Sutanate Saelao

2016-08-01

Full Text Available This study evaluates the safety of kung-som which was distributed in local markets and using PCR-DGGE technique to identify microflora in kung-som. Lactic acid bacteria (LAB were found at counts of more than 7 log CFU g-1 in all samples and the total viable counts were about 5-8 log CFU g-1 . Bacillus cereus and yeasts were detected at around 2 log CFU g-1 and 5-6log CFU g-1, respectively. For DGGE analysis, LAB and coagulase negative staphylococci (CNS bacteria dominated over other microorganisms. The sequencing of the DNA bands from DGGE gels corresponding to kung-som samples showed the presence of LAB as the major microflora in the products, namely: Lactobacillus farciminis, Lactobacillus plantarum, Lactococcus garvieae, Tetragenococcus halophilus and Weissella thailandensis. In addition, Staphylococcus carnosus was detected in kung-som as minor microflora. These dominant strains would allow the development of defined starter cultures for improving the quality of kung-som.

DroidEnsemble: Detecting Android Malicious Applications with Ensemble of String and Structural Static Features

KAUST Repository

Wang, Wei

2018-05-11

Android platform has dominated the Operating System of mobile devices. However, the dramatic increase of Android malicious applications (malapps) has caused serious software failures to Android system and posed a great threat to users. The effective detection of Android malapps has thus become an emerging yet crucial issue. Characterizing the behaviors of Android applications (apps) is essential to detecting malapps. Most existing work on detecting Android malapps was mainly based on string static features such as permissions and API usage extracted from apps. There also exists work on the detection of Android malapps with structural features, such as Control Flow Graph (CFG) and Data Flow Graph (DFG). As Android malapps have become increasingly polymorphic and sophisticated, using only one type of static features may result in false negatives. In this work, we propose DroidEnsemble that takes advantages of both string features and structural features to systematically and comprehensively characterize the static behaviors of Android apps and thus build a more accurate detection model for the detection of Android malapps. We extract each app’s string features, including permissions, hardware features, filter intents, restricted API calls, used permissions, code patterns, as well as structural features like function call graph. We then use three machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Random Forest (RF), to evaluate the performance of these two types of features and of their ensemble. In the experiments, We evaluate our methods and models with 1386 benign apps and 1296 malapps. Extensive experimental results demonstrate the effectiveness of DroidEnsemble. It achieves the detection accuracy as 95.8% with only string features and as 90.68% with only structural features. DroidEnsemble reaches the detection accuracy as 98.4% with the ensemble of both types of features, reducing 9 false positives and 12 false

Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

Directory of Open Access Journals (Sweden)

Suyu Mei

Full Text Available Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM, where support vector machine (SVM is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

Medborgerskab som skolefag i Malaysia

DEFF Research Database (Denmark)

Tiemensma, Britt Due; Hald, Anne Mette

2009-01-01

Globalisering og internationalisering indgår med selvfølgelighed i uddannelsessektorens begrebsverden. Men hvad mener vi egentlig, når vi bruger begreberne? Benyttes de som abstrakte talemåder, for at vi skal kunne forholde os til uoverskueligheden i den aktuelle udvikling, eller kan begreberne k...

A Design-Based introduction to learning centres

Directory of Open Access Journals (Sweden)

Anne Kristine Petersen

2016-05-01

Full Text Available In the last decades, outskirt areas in Denmark have suffered from depopulation and economic decline, a development that has led to a centralised education system where higher education institutions are vested in a central body in urban areas rather than in rural communities. University College Zealand has initiated a research project in collaboration with three municipalities in the region of Zealand and partners from the Nordic countries, which investigates the potential of municipal learning centres as a means to solve educational challenges in outskirt areas. A municipal learning centre is a physical location owned by a municipality, which offers (asynchronous courses through digital couplings to higher education institutions. The paper presents research findings showing that the development of an ecosystem based on collaboration between municipalities, higher education institutions and private and public businesses is pivotal for achieving a sustainable model for online education in rural areas. Furthermore, the paper presents a series of thinking technologies in the form of models and categories, which can be used as tools for establishing learning centres and designing learning activities for learning centres. --- Kommuner placeret i yderområder i Danmark har i de seneste årtier oplevet affolkning og økonomisk nedgang, og denne udvikling har medvirket til et centraliseret uddannelsessystem, hvor videregående uddannelsesinstitutioner flyttes fra yderområder til større byer. University College Sjælland har igangsat et forskningsprojekt i samarbejde med tre kommuner i Region Sjælland og partnere fra de nordiske lande, som har til formål at undersøge hvorvidt uddannelseskonceptet kommunale læringscentre kan medvirke til at løse uddannelsesudfordringer i landets yderområder. Et kommunalt læringscenter er en fysisk lokation som ejes af en kommune, som gennem læringscenteret kan give borgere mulighed for at tage et kursus eller en

A multi-model ensemble approach to seabed mapping

Science.gov (United States)

Diesing, Markus; Stephens, David

2015-06-01

Seabed habitat mapping based on swath acoustic data and ground-truth samples is an emergent and active marine science discipline. Significant progress could be achieved by transferring techniques and approaches that have been successfully developed and employed in such fields as terrestrial land cover mapping. One such promising approach is the multiple classifier system, which aims at improving classification performance by combining the outputs of several classifiers. Here we present results of a multi-model ensemble applied to multibeam acoustic data covering more than 5000 km2 of seabed in the North Sea with the aim to derive accurate spatial predictions of seabed substrate. A suite of six machine learning classifiers (k-Nearest Neighbour, Support Vector Machine, Classification Tree, Random Forest, Neural Network and Naïve Bayes) was trained with ground-truth sample data classified into seabed substrate classes and their prediction accuracy was assessed with an independent set of samples. The three and five best performing models were combined to classifier ensembles. Both ensembles led to increased prediction accuracy as compared to the best performing single classifier. The improvements were however not statistically significant at the 5% level. Although the three-model ensemble did not perform significantly better than its individual component models, we noticed that the five-model ensemble did perform significantly better than three of the five component models. A classifier ensemble might therefore be an effective strategy to improve classification performance. Another advantage is the fact that the agreement in predicted substrate class between the individual models of the ensemble could be used as a measure of confidence. We propose a simple and spatially explicit measure of confidence that is based on model agreement and prediction accuracy.

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Science.gov (United States)

Lu, Wei; Li, Zhe; Chu, Jinghui

2017-04-01

Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improving the performance of base classifiers. We extracted morphological, various texture, and Gabor features. To clarify the feature subsets' physical meaning, subspaces are built by combining morphological features with each kind of texture or Gabor feature. We tested our proposal using a manually segmented Region of Interest (ROI) data set, which contains 438 images of malignant tumors and 1898 images of normal tissues or benign tumors. Our proposal achieves an area under the ROC curve (AUC) value of 0.9617, which outperforms most other state-of-the-art breast MRI CADx systems. Compared with other methods, our proposal significantly reduces the false-positive classification rate. Copyright © 2017 Elsevier Ltd. All rights reserved.

EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

KAUST Repository

Rapakoulia, Trisevgeni

2014-04-26

Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem ofmissing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. © The Author 2014.

Ole Rømer som enevældens administrator

DEFF Research Database (Denmark)

Olden-Jørgensen, Sebastian

2004-01-01

En biografisk skitse af Ole Rømers virke med vægt på hans rolle som embedsmand og rådgiver for Christian V. Hans virke analyseres på baggrund af en ny forståelse for den tidlige enevældes politiske kultur.......En biografisk skitse af Ole Rømers virke med vægt på hans rolle som embedsmand og rådgiver for Christian V. Hans virke analyseres på baggrund af en ny forståelse for den tidlige enevældes politiske kultur....

Krisekommunikation som model for virksomhedsretorik?

DEFF Research Database (Denmark)

Rasmussen, Rasmus Kjærgaard

2017-01-01

kommunikativt konstitueret fænomener. Her argumenterer jeg for en analytisk syntese mellem framingteorier og det såkaldte CCO-paradigme, til sammen kan hjælpe krisekommunikationsteorierne med at slippe den aprioriske tilgang til kriser ved at forstå såvel kriser og organisationer som resultatet af kommunikative...

Learning Museum genbesøgt

DEFF Research Database (Denmark)

Zipsane, Henrik; Fristrup, Tine; Lundborg, Maria Domeij

Hvordan kan LM-modellens fokus på det tværinstitutionelle samarbejde mellem skole, læreruddannelse og museum anskues som en kapacitetsopbyggende strategi i forbindelse med implementeringen af folkeskolereformen? Dette undersøgelsesspørgsmål udgør det centrale omdrejningspunkt i nærværende...... forskere tilknyttet NCK: Henrik Zipsane (projektleder), Tine Fristrup (gæsteforsker fra DPU, Aarhus Universitet), Maria Domeij Lundborg og Sara Grut. Undersøgelsens baggrund skal først og fremmest findes i projektet ’Learning Museum’ som i perioden 2011-2013 etablerede et samarbejde mellem læreruddannelser...... og museer i hele Danmark. Det var resultaterne fra dette projekt, som igangsatte nærværende undersøgelse eftersom ændringer af læreruddannelsen (fra LU07 til LU13) i forbindelse med folkeskolereformen i 2013 faldt sammen med afslutningen på Learning Museum projektet. Folkeskolereformen og dermed...

Identification of illicit drugs by using SOM neural networks

Energy Technology Data Exchange (ETDEWEB)

Liang Meiyan; Shen Jingling; Wang Guangqin [Beijing Key Lab for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100037 (China)], E-mail: liangyan661982@163.com, E-mail: jinglingshen@gmail.com, E-mail: pywgq2004@163.com

2008-07-07

Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with.

Identification of illicit drugs by using SOM neural networks

International Nuclear Information System (INIS)

Liang Meiyan; Shen Jingling; Wang Guangqin

2008-01-01

Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with

On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

KAUST Repository

Luo, Xiaodong

2010-09-19

The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

Skønlitteraturen som opposition

DEFF Research Database (Denmark)

Larsen, Pelle Oliver

2011-01-01

Troels Troels-Lund er kendt som foregangsmanden i den første kulturhistoriske bølge i Danmark fra omkring 1880. Hans form for kulturhistorie vandt imidlertid aldrig for alvor faghistorikernes anerkendelse, og Troels-Lund blev udgrænset. Denne artikel går tilbage til 1870’erne og indeholder en ana...

Med det sociale som designmateriale

DEFF Research Database (Denmark)

Brandt, Eva; Binder, Thomas

2016-01-01

Samarbejde, samskabelse og social nysgerrighed er i dag en præmis for den nyskabende designer. Det sociale er det nye designmateriale som unge designere giver sig i kast med når f.eks. Emmy Linde samarbejder med plejehjemsbeboere, deres pårørende, plejepersonale og fysioterapeuter for at designe ...

Subsurface oxidation for micropatterning silicon (SOMS).

Science.gov (United States)

Zhang, Feng; Sautter, Ken; Davis, Robert C; Linford, Matthew R

2009-02-03

Here we present a straightforward patterning technique for silicon: subsurface oxidation for micropatterning silicon (SOMS). In this method, a stencil mask is placed above a silicon surface. Radio-frequency plasma oxidation of the substrate creates a pattern of thicker oxide in the exposed regions. Etching with HF or KOH produces very shallow or much higher aspect ratio features on silicon, respectively, where patterning is confirmed by atomic force microscopy, scanning electron microscopy, and optical microscopy. The oxidation process itself is studied under a variety of reaction conditions, including higher and lower oxygen pressures (2 and 0.5 Torr), a variety of powers (50-400 W), different times and as a function of reagent purity (99.5 or 99.994% oxygen). SOMS can be easily executed in any normal chemistry laboratory with a plasma generator. Because of its simplicity, it may have industrial viability.

M-Learning & e-inclusion. Il progetto ENSEMBLE

Directory of Open Access Journals (Sweden)

Maria Ranieri

2013-03-01

Full Text Available Nel corso degli ultimi dieci anni, l’Unione europea ha più volte sottolineato, nelle sue raccomandazioni e documenti, il ruolo che le ICT possono svolgere per favorire le opportunità di partecipazione ed integrazione dei cittadini più svantaggiati. In questo contesto, il progetto ENSEMBLE, qui presentato, si è proposto di mettere a punto una strategia d’impiego delle tecnologie della comunicazione per promuovere l’integrazione socio-culturale dei cittadini immigrati, facendo leva sull’uso di tecnologie come il lettore MP3 e il telefono cellulare, e sperimentando metodologie didattiche e formati comunicativi adatti agli strumenti impiegati.

Utholdenhetstrening i fotball: En analyse av smålagsspill brukt som utholdenhetstrening

OpenAIRE

Lorvik, Knut

2013-01-01

Utholdenhet er en fysisk egenskap som har betydning for fotballprestasjonen. Det viser flere studier. Helgerud (et al. 2001) fant signifikante forbedringer i forhold til flere fysiske mål som ble testet. Impellizzeri (et al. 2008) fant i sin studie at en forbedring i VO2max påvirket kortpasningsferdighetene positivt. I denne oppgaven har jeg fokusert på utholdenhetstrening i fotball og har to problemstillinger. Den første problemstillingen handler om hvilke studier som er gjort av utholde...

Kina som stormagt i Arktis

DEFF Research Database (Denmark)

Nørup Sørensen, Camilla Tenna

I slutningen af januar 2018 offentliggjorde Kina sin længe ventede Arktisstrategi. Den står som en foreløbig kulmination på de senere års udvikling af et mere selvsikkert, proaktivt og sofistikeret kinesisk diplomati i Arktis. Beijing har intensiveret bestræbelserne på at etablere stærke og...

Flipped Learning

DEFF Research Database (Denmark)

Holmboe, Peter; Hachmann, Roland

I FLIPPED LEARNING – FLIP MED VIDEO kan du læse om, hvordan du som underviser kommer godt i gang med at implementere video i undervisning, der har afsæt i tankerne omkring flipped learning. Bogen indeholder fire dele: I Del 1 fokuserer vi på det metarefleksive i at tænke video ind i undervisningen...

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

Science.gov (United States)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Learning design som systematisk alternativ til one-hit wonders

DEFF Research Database (Denmark)

Godsk, Mikkel; Hansen, Janne Saltoft

2016-01-01

In 2011 an ambitious policy for educational IT was issued at Aarhus University (AU). The policy includes a number of focus areas of which particularly the acquisition and implementation of a common e‐learning platform, training of educators, and the development of teaching are of the highest...... priority (Aarhus Universitet, 2011). Due to limited funds there has been a need for an extraordinary systematic and effective way to manage the work of implementation and development. This meant that Faculty of Science and Technology (ST) in 2013 developed a learning design framework, STREAM (Godsk, 2013...... of coherent teaching (Conole & Fill, 2005; Fink, 2013). This operationalisation meant that the learning design approach could also be used to pedagogically qualify the implementation and deployment of the university's new e­‐learning platform, Blackboard Learn. In the article we describe our learning design...

A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

Directory of Open Access Journals (Sweden)

Bin Jia

2017-01-01

Full Text Available The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR, accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN, and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

Fermi field and Dirac oscillator in a Som-Raychaudhuri space-time

Science.gov (United States)

de Montigny, Marc; Zare, Soroush; Hassanabadi, Hassan

2018-05-01

We investigate the relativistic dynamics of a Dirac field in the Som-Raychaudhuri space-time, which is described by a Gödel-type metric and a stationary cylindrical symmetric solution of Einstein field equations for a charged dust distribution in rigid rotation. In order to analyze the effect of various physical parameters of this space-time, we solve the Dirac equation in the Som-Raychaudhuri space-time and obtain the energy levels and eigenfunctions of the Dirac operator by using the Nikiforov-Uvarov method. We also examine the behaviour of the Dirac oscillator in the Som-Raychaudhuri space-time, in particular, the effect of its frequency and the vorticity parameter.

Værdibasering som kommunal praksis

DEFF Research Database (Denmark)

Ostrowski, Kasper

2009-01-01

og tværkommunale forskelle foranlediget af kommunalreformen i Danmark. Begrebet ’værdibaseret ledelse’ gestaltes ofte med en både frustrerende lethed og på fascinerende vis som fænomen. Dette har ansporet min interesse for i denne afhandling at undersøge, hvorledes værdibaseret ledelse kan forstås...

Sæbeopera som Forestillingsgenererende Teknologi

DEFF Research Database (Denmark)

Waltorp, Karen

2013-01-01

Gennem en række empiriske eksempler fra Mauretanien, Danmark og Sydafrika analyserer artiklen dramaserier på tv som en art social teknologi, der tillader infor- manterne at generere forestillinger om andre liv og andre verdener, der på én gang er langt væk og tæt på. 'Soap operas' produceret i USA...

Feltarbejde som læringsrum

DEFF Research Database (Denmark)

Lund, Lisbeth Revsbæk

2010-01-01

artiklen beskrives, hvordan refleksionsrum og metakommunikative processer opleves af studerende, og hvilken betydning disse rum får for deres forståelse af såvel egne som uddannelsens læreprocesser. Artiklen er en del af en antologi, der tager sit udgangspunkt i de mange rum mellem uddannelse og profession......, databearbejdning, feltarbejde og teaching lab....

Løgnene er ikke så nemme at huske som sandheden

DEFF Research Database (Denmark)

Bertelsen, Bettina

2005-01-01

Formålet med denne artikel er at belyse og diskutere, hvordan og hvorvidt prostituion kan forstås som et socialt problem. Dette vil jeg gøre gennem en teoretisk diskussion og analyse af prostitution som et socialt problem belyst ud fra konkrete projekterfaringer. Erfaringerne stammer dels fra...

Ensembl 2004.

Science.gov (United States)

Birney, E; Andrews, D; Bevan, P; Caccamo, M; Cameron, G; Chen, Y; Clarke, L; Coates, G; Cox, T; Cuff, J; Curwen, V; Cutts, T; Down, T; Durbin, R; Eyras, E; Fernandez-Suarez, X M; Gane, P; Gibbins, B; Gilbert, J; Hammond, M; Hotz, H; Iyer, V; Kahari, A; Jekosch, K; Kasprzyk, A; Keefe, D; Keenan, S; Lehvaslaiho, H; McVicker, G; Melsopp, C; Meidl, P; Mongin, E; Pettett, R; Potter, S; Proctor, G; Rae, M; Searle, S; Slater, G; Smedley, D; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Storey, R; Ureta-Vidal, A; Woodwark, C; Clamp, M; Hubbard, T

2004-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organize biology around the sequences of large genomes. It is a comprehensive and integrated source of annotation of large genome sequences, available via interactive website, web services or flat files. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. The facilities of the system range from sequence analysis to data storage and visualization and installations exist around the world both in companies and at academic sites. With a total of nine genome sequences available from Ensembl and more genomes to follow, recent developments have focused mainly on closer integration between genomes and external data.

E-learning Nordic 2006

DEFF Research Database (Denmark)

Pedersen, Sanya Gertsen

2006-01-01

E-learning Nordic 2006 er den første fællesnordiske undersøgelse, som specifikt fokuserer på effekten af it i uddannelsessektoren. Studiet er gennemført i Finland, Sverige, Norge og Danmark og mere end 8000 personer (elever, lærere, forældre og skoleledere i grundskoler og på de gymnasiale...... ungdomsuddannelser) har deltaget. Dette studie giver en række svar på centrale spørgsmål såsom: Hvad har vi fået ud af satsningen på it i uddannelsessektoren indtil nu? Og hvilke udfordringer inden for uddannelsessektoren står de nordiske lande foran i en globaliseret verden? E-learning Nordic 2006 er designet og...

Automated generation and ensemble-learned matching of X-ray absorption spectra

Science.gov (United States)

Zheng, Chen; Mathew, Kiran; Chen, Chi; Chen, Yiming; Tang, Hanmei; Dozier, Alan; Kas, Joshua J.; Vila, Fernando D.; Rehr, John J.; Piper, Louis F. J.; Persson, Kristin A.; Ong, Shyue Ping

2018-03-01

X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green's function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak "learners" comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.

Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

Directory of Open Access Journals (Sweden)

Jan D. Keller

2008-12-01

Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

CLUSTER ANALYSIS UNTUK MEMPREDIKSI TALENTA PEMAIN BASKET MENGGUNAKAN JARINGAN SARAF TIRUAN SELF ORGANIZING MAPS (SOM

Directory of Open Access Journals (Sweden)

Gregorius Satia Budhi

2008-01-01

Full Text Available Basketball World has grown rapidly as the time goes on. This is signed by many competition and game all over the world. With the result there are many basketball players with their different playing characteristics. Demand for a coach or scout to look for or search great players to make a solid team as a coach requirement. With this application, a coach or scout will be helped in analyzing in decision making. This application uses Self Organizing Maps algorithm (SOM for Cluster Analysis. The real NBA player data is used for competitive learning or training process and real player data from Indonesian or Petra Christian University Basketball Players is used for testing process. The NBA Player data is prepared through cleaning process and then is transformed into a form that can be processed by SOM Algorithm. After that, the data is clustered with the SOM algorithm. The result of that clusters is displayed into a form that is easy to view and analyze. This result can be saved into a text file. By using the output / result of this application, that are the clusters of NBA player, the user can see the statistics of each cluster. With these cluster statistics coach or scout can predict the statistic and the position of a testing player who is in the same cluster. This information can give a support for the coach or scout to make a decision. Abstract in Bahasa Indonesia : Dunia bola basket telah berkembang dengan pesat seiring dengan berjalannya waktu. Hal ini ditandai dengan munculnya berbagai macam dan jenis kompetisi dan pertandingan baik dunia maupun dalam negeri. Sehingga makin banyak dilahirkannya pemain berbakat dengan berbagai karakteristik permainan yang berbeda. Tuntutan bagi seorang pelatih/pemandu bakat, untuk dapat melihat secara jeli dalam memenuhi kebutuhan tim untuk membentuk tim yang solid. Dengan dibuatnya aplikasi ini, maka akan membantu proses analisis dan pengambilan keputusan bagi pelatih maupun pemandu bakat Aplikasi ini

Ensemble Clustering using Semidefinite Programming with Applications.

Science.gov (United States)

Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui

2010-05-01

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Machinima som kreativ praksis

DEFF Research Database (Denmark)

Frølunde, Lisbeth

2014-01-01

Video: Machinima som Kreativ Praksis. Videoen fortæller om de overordnede temaer i et forskningsprojekt der undersøgte nye former for online multimedie- og filmproduktion og fællesskaber med fokus på 'machinima' animation. Studiet af machinima var en del af et større dansk projekt og et nordisk f...... forskningprojekt om virtuelle verdener og teknologiudvikling, 2010-13. Videoen er præsenteret på Audiovisual Thinking nr. 7 under temaet: Den kreative økonomi....

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation.

Directory of Open Access Journals (Sweden)

Dhakshin S Ramanathan

Full Text Available Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

Science.gov (United States)

Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

[Study on mechanism of SOM stabilization of paddy soils under long-term fertilizations].

Science.gov (United States)

Luo, Lu; Zhou, Ping; Tong, Cheng-Li; Shi, Hui; Wu, Jin-Shui; Huang, Tie-Ping

2013-02-01

Fourier transform infrared spectroscopy (FTIR) was applied to study the structure of soil organic matter (SOM) of paddy soils under long-term different fertilization treatments. The aim was to clarify the different distribution of SOM between different fertilization methods and between topsoil and subsoil, and to explore the stability mechanism of SOM under different fertilization treatments. The results showed that the content of topsoil organic carbon (SOC) was the highest under organic-inorganic fertilizations, with the increment of SOC by 18.5%, 12.9% and 18.4% under high organic manure (HOM), low organic manure (LOM) and straw returning (STW) respectively compared with no fertilization treatment (CK). The long-term fertilizations also changed the chemical structure of SOM. As compared with CK, different fertilization treatments increased the functional group absorbing intensity of chemical resistance compounds (aliphatic, aromaticity), carbohydrate and organo-silicon compounds, which was the most distinctive under treatments of HOM, LOM and STW. For example, the absorbing intensity of alkyl was 0.30, 0.25 and 0.29 under HOM, LOM and STW, respectively. These values were increased by 87% , 56% and 81% as compared with that under CK treatment. The functional group absorbing intensity of SOM in the topsoil was stronger than that in the subsoil, with the most distinctive difference under HOM, LOM and STW treatments. The present research indicated that the enhanced chemical resistance of functional group of SOM may contribute to the high contents of SOC in the paddy soils under long-term organic-inorganic fertilizations, which also suggested a chemical stabilization mechanism of SOM in the paddy soils.

Supply Chain Management som interaktionel praksis

DEFF Research Database (Denmark)

Smith, Leo Feddersen; Lund, Anders

2016-01-01

I dette bidrag undersøger vi Supply Chain Management som en interaktionel praksis forankret i kommunikation. Med udgangspunkt i e-mail korrespondancer undersøger vi, hvordan køber-leverandør forholdet kommunikativt konstitueres mellem to danske SMVer og deres kinesiske leverandører. Det...

Får - ett miljövänligt alternativ som ogräsbekämpning

DEFF Research Database (Denmark)

Jensen, Anne Mette Dahl; Sintorn, Kim

2010-01-01

Erfarenhetsutbyte omkring fårbete som ogräsbekämpning var bland annat på dagordningen då svenska och danska greenkeepers som helt eller delvis sköter sina banor pesticidfritt möttes på Furesö Golfklubb den 4 maj i år.......Erfarenhetsutbyte omkring fårbete som ogräsbekämpning var bland annat på dagordningen då svenska och danska greenkeepers som helt eller delvis sköter sina banor pesticidfritt möttes på Furesö Golfklubb den 4 maj i år....

Prediction of Coal Face Gas Concentration by Multi-Scale Selective Ensemble Hybrid Modeling

Directory of Open Access Journals (Sweden)

WU Xiang

2014-06-01

Full Text Available A selective ensemble hybrid modeling prediction method based on wavelet transformation is proposed to improve the fitting and generalization capability of the existing prediction models of the coal face gas concentration, which has a strong stochastic volatility. Mallat algorithm was employed for the multi-scale decomposition and single-scale reconstruction of the gas concentration time series. Then, it predicted every subsequence by sparsely weighted multi unstable ELM(extreme learning machine predictor within method SERELM(sparse ensemble regressors of ELM. At last, it superimposed the predicted values of these models to obtain the predicted values of the original sequence. The proposed method takes advantage of characteristics of multi scale analysis of wavelet transformation, accuracy and fast characteristics of ELM prediction and the generalization ability of L1 regularized selective ensemble learning method. The results show that the forecast accuracy has large increase by using the proposed method. The average relative error is 0.65%, the maximum relative error is 4.16% and the probability of relative error less than 1% reaches 0.785.

Learning design – praktisk planlægning af et blended undervisningsforløb

DEFF Research Database (Denmark)

Hansen, Janne Saltoft

undervisningsplanlægning. Model og toolkit introduceres desuden generelt som planlægningsværktøj på Health til planlægning af blendede undervisningsforløb. På workshoppen præsenteres learning design-model og toolkit, samt de teoretiske rammer, som de valg, der er truffet i forbindelse med udviklingen, er baseret på......I 2011 vedtog Aarhus Universitet en politik for educational it, der indeholdt visioner for anskaffelse af et nyt learning management system (LMS), undervisningsudvikling og uddannelse af undervisere. Blackboard blev anskaffet, og implementering samt uddannelse af undervisere startede i slutningen...... udvidet kursus. For at eksplicitere og kvalitetssikre undervisningsplanlægningen med educational it blev det besluttet at binde undervisningen i Blackboard op på et learning design med en central learning design-model, som skulle lægge op til at skabe sammenhængende undervisning, understøtte aktiverende...

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Science.gov (United States)

Wallach, Daniel; Mearns, Linda O.; Ruane, Alexander C.; Roetter, Reimund P.; Asseng, Senthold

2016-01-01

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

An ensemble deep learning based approach for red lesion detection in fundus images.

Science.gov (United States)

Orlando, José Ignacio; Prokofyeva, Elena; Del Fresno, Mariana; Blaschko, Matthew B

2018-01-01

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our

Perception som forståelsens grundlag

DEFF Research Database (Denmark)

Nielsen, Charlotte Marie Bisgaard

2005-01-01

sprogpsykologisk som sense-making procedurer. Perception har ifølge William James at gøre med personers forståelser. Monets katedralmalerier fra Rouen og Cézannes billeder fra Aix-en Provence samt hans Selvportræt med Bowlerhat (Ny Carlsberg Glyptotek) sammenholdes med Merleau-Pontys Perceptionens Primat og...

Massemedier som forum for politisk debat

DEFF Research Database (Denmark)

Laursen, Bo; Trapp, Leila

2015-01-01

Ifølge normative teorier om politisk offentlighed og demokrati bør der i deliberative demokratier som det danske foregå en bred offentlig debat, inden politikere træffer beslutninger. Debatten, der i dagens Danmark hovedsagelig foregår i massemedierne, skal blandt andet sikre, at alle synspunkter...

Modelling bare fallow SOM dynamics on a Chernozem soil in Central Germany

Science.gov (United States)

Franko, Uwe; Merbach, Ines

2017-04-01

The level of our process understanding about carbon and nitrogen fluxes in soils becomes visible at extreme situations like bare fallow soils. The observed dynamics of soil organic carbon (SOC) and total nitrogen (TN) in the top soil on a 28 years old fallow experiment on Haplic Chernozem in Bad Lauchstädt (Germany) was modelled using the Candy Carbon Balance (CCB) model that in its standard version was previously validated with LTFE data from Central Europe and a tillage experiment in Austria. For this study we selected two treatments of the fallow experiment in Bad Lauchstädt where the soil was kept bare with mechanical or chemical treatments. For this extreme land use (no input of fresh organic matter) the CCB model was improved to include the SOC related change of soil physical parameters and a dynamic handling of the physically stabilized soil organic matter (SOM) pool. The results from observation and modelling reflected the increased SOM turnover due to soil tillage for carbon as well as nitrogen and thus confirmed the modelling approach for non-tillage in CCB. The added sub model for the dynamics of physically stabilized SOM was also verified. The long term stabilized SOM is very important on this site. The modelled size of the physically stabilized SOC pool was about 55% of total SOC and reduced only slowly during the nearly three decades but the implementation of this effect resulted in improved simulation results. Thus we conclude that scenarios that lead to bigger changes of SOM stocks require a modelling approach that acknowledges the interaction between SOM and soil physical properties.

An ensemble model of QSAR tools for regulatory risk assessment.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

Entropy of network ensembles

Science.gov (United States)

Bianconi, Ginestra

2009-03-01

In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

Science.gov (United States)

Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

2018-05-04

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ensembl variation resources

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Marin-Garcia Pablo

2010-05-01

Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org.

An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

DEFF Research Database (Denmark)

Lyksborg, Mark; Puonti, Oula; Agn, Mikael

2015-01-01

Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images....... The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally......, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation...

Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

Science.gov (United States)

Pistoia, Jenny; Pinardi, Nadia; Oddo, Paolo; Collins, Matthew; Korres, Gerasimos; Drillet, Yann

2017-04-01

Short-term ocean analyses for Sea Surface Temperature SST in the Mediterranean Sea can be improved by a statistical post-processing technique, called super-ensemble. This technique consists in a multi-linear regression algorithm applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset, a collection of different operational forecasting analyses together with ad-hoc simulations produced by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is capable to prevent overfitting problems, even if best performances are achieved when we add correlation to the super-ensemble structure using a simple spatial filter applied after the linear regression. Our outcomes show that super-ensemble performances depend on the selection of an unbiased operator and the length of the learning period, but the quality of the generating MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. Lower RMSE analysis estimates result from the following choices: 15 days training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

NYYD Ensemble

Index Scriptorium Estoniae

2002-01-01

NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

Mindfulness som smertehåndteringsredskab for kvinder med endometriose

DEFF Research Database (Denmark)

Jensen, Mette Kold; Vedsted-Hansen, Hanne; Hansen, Tia G. B.

2011-01-01

Endometriose er en kronisk underlivssygdom med smerter og en række afledte problemer, som ikke nødvendigvis kan behandles med lægelige tiltag. Artiklen argumenterer for, at en mindfulness-baseret tilgang med fokus på smertehåndtering kan anvendes til denne klientgruppe. I vestlig terapeutisk...... sammenhæng kan mindfulness karakteriseres som nærvær, observation og beskrivelse af sansninger uden vurdering eller reaktion. Mindfulness-træning har effekt mod andre typer stress og kroniske smerter, og artiklen opridser et koncept til anvendelse ved endometriose. Konceptet belyses med en case, hvor...

Brands som brikker i konstruktionen af identitet

DEFF Research Database (Denmark)

Hermansen, Judy

2008-01-01

Forbrug spiller en vigtig rolle i produktionen af mening og værdier, som kan skabe og vedligeholde både forbrugerens personlige og sociale verden - og derfor er reklame også en vigtig kilde til disse symbolske betydninger. Udgivelsesdato: juni...

Djævelen som Guds advokat - Nietzsche, filosofien og Paulus

DEFF Research Database (Denmark)

Pallesen, Carsten

2012-01-01

Artiklen tegner et dobbeltportræt af Paulus og Nietzsche ud fra en række nyere diskussioner om Nietzsches syn på Paulus hos aktuelle Nietzscheforskere, men også med udblik til den Paulus-renæssance, som har fundet sted i filosofien. Med det sidste tænkes navnlig på den franske filosof Alain Badious...... tese om Paulus som universalismens tænker. Artiklen fremhæver, hvordan hermeneutiske, kommunikationsteoretiske og semiotiske tilgange i den nyere tyske forskning navnlig hos Werner Stegmaier har ændret synet på forholdet mellem Nietzsches ’tegn’ (Dionysos, Zarathustra, ’anti-krist’) over for Nietzsches...... retfærdiggørelse ved troen. Nietzsches forhold til kristendommen er ikke kun bestemt af negative vurderinger, men tillige af en ambivalent taknemmelighed, som når han i den sene fase af forfatterskabet på en gang identificerer sig med Dionysos og ’den korsfæstede’....

Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

Directory of Open Access Journals (Sweden)

Jianzhong Zhou

2018-05-01

Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Directory of Open Access Journals (Sweden)

Łukasz Augustyniak

2015-12-01

Full Text Available We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature.

'Lazy' quantum ensembles

International Nuclear Information System (INIS)

Parfionov, George; Zapatrin, Roman

2006-01-01

We compare different strategies aimed to prepare an ensemble with a given density matrix ρ. Preparing the ensemble of eigenstates of ρ with appropriate probabilities can be treated as 'generous' strategy: it provides maximal accessible information about the state. Another extremity is the so-called 'Scrooge' ensemble, which is mostly stingy in sharing the information. We introduce 'lazy' ensembles which require minimal effort to prepare the density matrix by selecting pure states with respect to completely random choice. We consider two parties, Alice and Bob, playing a kind of game. Bob wishes to guess which pure state is prepared by Alice. His null hypothesis, based on the lack of any information about Alice's intention, is that Alice prepares any pure state with equal probability. Then, the average quantum state measured by Bob turns out to be ρ, and he has to make a new hypothesis about Alice's intention solely based on the information that the observed density matrix is ρ. The arising 'lazy' ensemble is shown to be the alternative hypothesis which minimizes type I error

"Vi har ikke noget at sige, men vi gør det så koncentreret som muligt"

DEFF Research Database (Denmark)

Hansen, Kamma Overgaard

mellemposition mellem på den ene side Arthur C. Dantos udråbelse af postmodernismen som The End of Art og Fredric Jamesons kritiske udlægning af den postmoderne kunst som simulerende og hans begrædelse af en tabt autenticitet - og på den anden side Linda Hutcheons langt mere positive definering af det...... postmoderne som ironisk og parodisk og dermed nytænkende og metareflekterende. Herudfra karakteriserer jeg De Unge Vilde som ironikere ud fra Søren Kierkegaards karakteristisk af ironikeren som en mellemfase mellem det æstetiske og det etiske livsstadie. Videre argumenterer jeg for, at Dantos ’end of art...

Stress affects the neural ensemble for integrating new information and prior knowledge.

Science.gov (United States)

Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

2018-06-01

Prior knowledge, represented as a schema, facilitates memory encoding. This schema-related learning is assumed to rely on the medial prefrontal cortex (mPFC) that rapidly integrates new information into the schema, whereas schema-incongruent or novel information is encoded by the hippocampus. Stress is a powerful modulator of prefrontal and hippocampal functioning and first studies suggest a stress-induced deficit of schema-related learning. However, the underlying neural mechanism is currently unknown. To investigate the neural basis of a stress-induced schema-related learning impairment, participants first acquired a schema. One day later, they underwent a stress induction or a control procedure before learning schema-related and novel information in the MRI scanner. In line with previous studies, learning schema-related compared to novel information activated the mPFC, angular gyrus, and precuneus. Stress, however, affected the neural ensemble activated during learning. Whereas the control group distinguished between sets of brain regions for related and novel information, stressed individuals engaged the hippocampus even when a relevant schema was present. Additionally, stressed participants displayed aberrant functional connectivity between brain regions involved in schema processing when encoding novel information. The failure to segregate functional connectivity patterns depending on the presence of prior knowledge was linked to impaired performance after stress. Our results show that stress affects the neural ensemble underlying the efficient use of schemas during learning. These findings may have relevant implications for clinical and educational settings. Copyright © 2018 Elsevier Inc. All rights reserved.

Global Optimization Ensemble Model for Classification Methods

Science.gov (United States)

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

Global Optimization Ensemble Model for Classification Methods

Directory of Open Access Journals (Sweden)

Hina Anwar

2014-01-01

Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

The online performance estimation framework: heterogeneous ensemble learning for data streams

NARCIS (Netherlands)

van Rijn, J.N.; Holmes, G.; Pfahringer, B.; Vanschoren, J.

2018-01-01

Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

Science.gov (United States)

Azami, Hamed; Escudero, Javier

2015-08-01

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

A target recognition method for maritime surveillance radars based on hybrid ensemble selection

Science.gov (United States)

Fan, Xueman; Hu, Shengliang; He, Jingbo

2017-11-01

In order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm II for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.

From poverty to power? Brasiliansk sosialpolitikk som soft power-ressurs

OpenAIRE

Flatjord, Inger-Marie

2015-01-01

Grunnet markant fattigdomsreduksjon det siste tiåret, har Brasil gått fra å være det klassiske eksemplet på et land med stor fattigdom og enorme sosiale forskjeller, til å innta en posisjon som foregangsland i den globale utviklingsdiskursen. Landets sosialpolitikk har høstet annerkjennelse fra både FN og Verdensbanken, og Brasil opplever en stor etterspørsel fra utviklingsland som vil lære av landets sosialpolitiske erfaringer. I lys av dette har jeg i denne oppgaven analysert hvordan landet...

Djur som en del i omvårdnad : En litteraturstudie

OpenAIRE

Jirback, Fia; Olsson, Malin

2011-01-01

Bakgrund: Äldre på boende idag kan känna att de saknar sociala interaktioner med vårdpersonal, familj eller vänner. För att uppleva god livskvalitet krävs bland annat trygghet, sociala interaktioner och aktiviteter. Djur kan användas som behandlingsmetod i vården i form av Animal-Assisted Activity (AAA) och Animal-Assisted Therapy (AAT). Syfte: Syftet med litteraturstudien var att belysa sällskapsdjurs betydelse för äldres livskvalitet på äldreboende. Metod: Studien har genomförts som en allm...

Imprinting and recalling cortical ensembles.

Science.gov (United States)

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

2016-08-12

Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

Science.gov (United States)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

Using synchronization in multi-model ensembles to improve prediction

Science.gov (United States)

Hiemstra, P.; Selten, F.

2012-04-01

In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of

Ensemble based system for whole-slide prostate cancer probability mapping using color texture features.

LENUS (Irish Health Repository)

DiFranco, Matthew D

2011-01-01

We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.

World Music Ensemble: Kulintang

Science.gov (United States)

Beegle, Amy C.

2012-01-01

As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

Time travel in the homogeneous Som-Raychaudhuri Universe

International Nuclear Information System (INIS)

Paiva, F.M.; Reboucas, M.J.; Teixeira, A.F.F.

1987-01-01

Properties of the rotating Som-Raychaudhuri homogeneous space-time are investigated: time-like and null geodesics, causality features, horizons and invariant characterization. An integral representation of its five isometries is also discussed. (author) [pt

Historiens Mening som et Kreativt Produkt - En Analyse av Camille Paglias "The Birth of the Western Eye"

OpenAIRE

Kyllingstad, Elisabeth

2016-01-01

Dette prosjektet er en metodisk tilnærming til Camille Paglias filosofiske historietekst ”The Birth of the Western Eye”. Sexual Personae – Art and Decadence from Nefertiti to Emily Dickinson (1991) er en revidert versjon av doktorgradsavhendlingen hennes, som hun forsvarte i 1974. Den forteller om det Paglia identifiserer som grunnleggende drivkrefter i det vestlige mennesket, og dets kunstneriske produksjon. På denne måten kan verket leses som en guide gjennom vestlig tenkning så vel som kun...

Snehvides Billede som virkelighedsteater

DEFF Research Database (Denmark)

Krøgholt, Ida

2015-01-01

nyere affektforskning, der er rettet mod kunstens henvendelsesformer og virkninger (bl.a. Massumi og Deleuze), markeres der en interesseforskydning fra følelse til affekt. Det får den konsekvens, at forklaringer på hvordan kunst fungerer og virker ikke søges i tilskuerens følelsesregistreringer men...... affektive henvendelsesform, henter jeg inspiration i aktør-netværks teorien (Latour) og denne teoris materialitets- og tingsbegreb. Den kølige form for virkelighedsteater, som Snehvides Billede repræsenterer, vil ikke vække tilskuerens trang til at gribe ind i virkeligheden. Den vil snarere, via affekt...

Hvordan ser den ost ud, som De ikke ved, De vil elske om to år?

DEFF Research Database (Denmark)

Grunert, Klaus G.

2006-01-01

Kender de Deres "latente behov"? Det gør De selvfølgelig ikke. Det er jo definitionen af begrebet: Et latent behov er et behov, som man ikke ved, at man har, men som kommer til overfladen, når man møder det rigtige produkt, som opfylder netop dette behov. Latente behov vedrører derfor den ost, vi...

Predicting gene function using hierarchical multi-label decision tree ensembles

Directory of Open Access Journals (Sweden)

Kocev Dragi

2010-01-01

Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

Forskningsoversigt - Effekterne af Cooperative Learning

DEFF Research Database (Denmark)

Larsen, Lea Lund

Kan Cooperative Learning - en undervisningsform hvor lærerens tid ved tavlen mindskes og hvor de lærende samarbejder om stoffet - maksimere de lærendes indlæring og medvirke til en forbedring af deres interpersonelle og kommunikative kompetencer, samt øge deres motivation for læring? Den megen...... forskning fra USA viser, at Cooperative Learning øger lærerens bevidsthed om, hvilken adfærd, han er medvirkende til at skabe blandt de lærende. Og den øger lærerens bevidsthed omkring interaktioner i klasserummet, og giver god plads og taletid til hver enkelt lærende. Set i lyset heraf kan Cooperative......, at Cooperative Learning har lige så høj grad af positiv effekt, som den viser sig at have på grundskoleområdet. Det er sigtet med denne oversigt over den empiriske forskning. Til start præsenteres Cooperative Learning som metode, dens rødder og udvikling, dernæst skitseres den omfattende forskning omkring...

Med sjefen på Facebook: En studie av ledere som er "venner" med sine ansatte

OpenAIRE

Jensen, Anita

2014-01-01

MR690 Masteroppgave i organisasjon og ledelse - utdanningsledelse Formålet med studien er å belyse hvordan aktiv bruk av sosiale medier, i dette tilfellet Facebook, påvirker relasjoner mellom mennesker. Hva skjer når en tar i bruk en websjanger som i utgangspunktet er umiddelbar og uformell, til jobbrelatert og mer formell kommunikasjon? Er det sjangeren eller relasjonen som endres? Søkelyset rettes mot ledere som er “venner” med sine medarbeidere, og problemstillingen er...

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

KAUST Repository

Toye, Habib

2017-05-26

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions

CERN Document Server

Seni, Giovanni

2010-01-01

This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although e

Filosofisk terapi som træning til døden

DEFF Research Database (Denmark)

Dræby, Anders

Den eksistentielfænomenologiske terapi deler den opfattelse med stoicismen, at filosofiens hovedopgave er at hjælpe mennesket med at finde vej i en problematisk verden. Oplægget udforsker desuden et overlap, som består i den indsigt, at terapeutisk udfrielse fra menneskelig lidelse til livsduelig......Den eksistentielfænomenologiske terapi deler den opfattelse med stoicismen, at filosofiens hovedopgave er at hjælpe mennesket med at finde vej i en problematisk verden. Oplægget udforsker desuden et overlap, som består i den indsigt, at terapeutisk udfrielse fra menneskelig lidelse til...

Ensemble modeling for aromatic production in Escherichia coli.

Directory of Open Access Journals (Sweden)

Matthew L Rizk

2009-09-01

Full Text Available Ensemble Modeling (EM is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt, transaldolase (Tal, and phosphoenolpyruvate synthase (Pps to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.

Forholdet mellem public relations og marketing som ledelsesfunktioner

Directory of Open Access Journals (Sweden)

James E. Grunig

1993-09-01

Full Text Available I forsommeren 1993 tildelte Dansk Public Relations Forening årets PR- pris til mesterkommunikatøren Lars Larsen, Jysk Sengetøjslager/Larsen Rejser. I motiveringen hedder det, at Lars Larsen hædres for "en prisvær- dig Public Relations indsats, der kan motivere til en øget anvendelse af Public Relations i den danske forretningsverden". Blandt PR-foreningens medlemmer vakte pristildelingen en vis furore; et indlæg i foreningens blad PRspektiv invendte, at Lars Larsen driver "publicity" (dvs. et reklame- værktøj - "Præcis den slags PR, som vi i mange år har arbejdet på at distancere Public Relation-begrebet fra". Lars Larsen selv sagde, at han slet ikke skelner mellem Public Relations, PR og markedsføring. Et andet indlæg i bladet argumenterede for, at det ikke længere giver mening at adskille begreberne Public Relations og markedsføring, fordi grænsen mellem det offentlige og det private ude i virkeligheden er mere og mere flydende. James Grunigs artikel, der søger at afklare forholdet mellem PR og marketing teoretisk og praktisk, er derfor brændende aktuelt, ikke mindst for PR-fagets bestræbelser på at blive anerkendt som en respek- tabel profession. Den blev præsenteret som foredrag under Grunigs Dan- marksbesøg i marts 1993, er redigeret af Kim Schrøder og oversat fra engelsk af Kenja Henriksen.

Rousseau som modsvar til forceret talentudvikling

DEFF Research Database (Denmark)

Holm, Claus; Nielsen, Jens Christian

2015-01-01

pædagogisk syn på børn og unges udvikling. Men dette syn er nu under pres. Det kommer for eksempel til udtryk, når en international idrætsaktør som FC Barcelona vil oprette elitært orienterede fodboldakademier for børn ned til 6-årsalderen i Danmark. Men inspireret af filosoffen Jean-Jacques Rousseau og...

A multi-stage intelligent approach based on an ensemble of two-way interaction model for forecasting the global horizontal radiation of India

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao; Xiao, Ling

2017-01-01

Highlights: • Ensemble learning system is proposed to forecast the global solar radiation. • LASSO is utilized as feature selection method for subset model. • GSO is used to select the weight vector aggregating the response of subset model. • A simple and efficient algorithm is designed based on thresholding function. • Theoretical analysis focusing on error rate is provided. - Abstract: Forecasting of effective solar irradiation has developed a huge interest in recent decades, mainly due to its various applications in grid connect photovoltaic installations. This paper develops and investigates an ensemble learning based multistage intelligent approach to forecast 5 days global horizontal radiation at four given locations of India. The two-way interaction model is considered with purpose of detecting the associated correlation between the features. The main structure of the novel method is the ensemble learning, which is based on Divide and Conquer principle, is applied to enhance the forecasting accuracy and model stability. An efficient feature selection method LASSO is performed in the input space with the regularization parameter selected by Cross-Validation. A weight vector which best represents the importance of each individual model in ensemble system is provided by glowworm swarm optimization. The combination of feature selection and parameter selection are helpful in creating the diversity of the ensemble learning. In order to illustrate the validity of the proposed method, the datasets at four different locations of the India are split into training and test datasets. The results of the real data experiments demonstrate the efficiency and efficacy of the proposed method comparing with other competitors.

Village Building Identification Based on Ensemble Convolutional Neural Networks

Science.gov (United States)

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

Science.gov (United States)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

Localization of atomic ensembles via superfluorescence

International Nuclear Information System (INIS)

Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail

2007-01-01

The subwavelength localization of an ensemble of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the ensemble with a standing wave laser field. The light scattered in the interaction of the standing wave field and the atom ensemble depends on the position of the ensemble relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters and the ensemble properties and which is modified due to collective effects in the ensemble of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an ensemble fixed at a certain position in the standing wave field. Second, we discuss localization of an ensemble passing through the standing wave field

Ensembl 2017

OpenAIRE

Aken, Bronwen L.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Gir?n, Carlos Garc?a; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.

2016-01-01

Ensembl (www.ensembl.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access ...

Ensemble Sampling

OpenAIRE

Lu, Xiuyuan; Van Roy, Benjamin

2017-01-01

Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of complex models such as neural networks. Ensemble sampling dramatically expands on the range of applica...

Livsvalg konceptualiseret som dynamisk realisering af livsbaner

DEFF Research Database (Denmark)

Schnieber, Anette

2009-01-01

De valgsituationer, man i daglig tale kender som ”tilværelsens store valg”, mangler definition i den psykologiske litteratur, og det er således ikke klart, hvorvidt og evt. hvorledes disse valg adskiller sig fra andre typer. I artiklen gives en definition af ”de store valg”, og de adskilles fra...

Læring som improviseret udvikling af praksis

DEFF Research Database (Denmark)

Brinck, Lars

2010-01-01

Med afsæt i forfatterens fænomenografiske studie af amerikanske musikeres oplevelse af det at jamme diskuterer artiklen, hvorvidt jam-bandets spontant improviserende praksis kan give ny forståelse af rytmiske musikeres læring som et overordnet socialt fænomen. Ved at betragte jam-bandets praksis i...

Feedback i professionsuddannelsen som pædagog

DEFF Research Database (Denmark)

Rold, Mette; Bjørnshave, Inge Andrea

2015-01-01

Feedback som begreb er her og nu kommet i søgelyset på de videregående uddannelser, fordi mange studerende, blandt andet i medierne, tilkendegiver at de ikke modtager nok feedback i forhold til den undervisning, de modtager. Dette paper er udgangspunkt for en fælles diskussion om netop dette....

A Contribution to the Study of Ensemble of Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Leandro Antonio Pasa

2015-01-01

Full Text Available This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

KAUST Repository

Rapakoulia, Trisevgeni; Theofilatos, Konstantinos A.; Kleftogiannis, Dimitrios A.; Likothanasis, Spiridon D.; Tsakalidis, Athanasios K.; Mavroudi, Seferina P.

2014-01-01

do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel

Proposed hybrid-classifier ensemble algorithm to map snow cover area

Science.gov (United States)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

Energy Technology Data Exchange (ETDEWEB)

Shu, Qingya; Guo, Hanqi; Che, Limei; Yuan, Xiaoru; Liu, Junfeng; Liang, Jie

2016-04-19

We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

Reacting to different types of concept drift: the Accuracy Updated Ensemble algorithm.

Science.gov (United States)

Brzezinski, Dariusz; Stefanowski, Jerzy

2014-01-01

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.

Contributions to Ensemble Classifiers with Image Analysis Applications

OpenAIRE

Ayerdi Vilches, Borja

2015-01-01

134 p. Ésta tesis tiene dos aspectos fundamentales, por un lado, la propuesta denuevas arquitecturas de clasificadores y, por otro, su aplicación a el análisis deimagen.Desde el punto de vista de proponer nuevas arquitecturas de clasificaciónla tesis tiene dos contribucciones principales. En primer lugar la propuestade un innovador ensemble de clasificadores basado en arquitecturas aleatorias,como pueden ser las Extreme Learning Machines (ELM), Random Forest (RF) yRotation Forest, llamado ...

Selvrefleksion som uddannelsesgreb – en kritisk diskussion

DEFF Research Database (Denmark)

Feilberg, Casper

2015-01-01

Hvordan kan ureflekteret motivation gribe forstyrrende ind i de studerendes videnskabelige arbejde, og hvordan kan man som vejleder forholde sig til disse private forhold på en både faglig og etisk forsvarlig måde? I artiklen vises eksempler på, at studerende nemt kan komme til at agere ud fra ur...

A new design for SLAM front-end based on recursive SOM

Science.gov (United States)

Yang, Xuesi; Xia, Shengping

2015-12-01

Aiming at the graph optimization-based monocular SLAM, a novel design for the front-end in single camera SLAM is proposed, based on the recursive SOM. Pixel intensities are directly used to achieve image registration and motion estimation, which can save time compared with the current appearance-based frameworks, usually including feature extraction and matching. Once a key-frame is identified, a recursive SOM is used to actualize loop-closure detecting, resulting a more precise location. The experiment on a public dataset validates our method on a computer with a quicker and effective result.

Dorsal-CA1 Hippocampal Neuronal Ensembles Encode Nicotine-Reward Contextual Associations.

Science.gov (United States)

Xia, Li; Nygard, Stephanie K; Sobczak, Gabe G; Hourguettes, Nicholas J; Bruchas, Michael R

2017-06-06

Natural and drug rewards increase the motivational valence of stimuli in the environment that, through Pavlovian learning mechanisms, become conditioned stimuli that directly motivate behavior in the absence of the original unconditioned stimulus. While the hippocampus has received extensive attention for its role in learning and memory processes, less is known regarding its role in drug-reward associations. We used in vivo Ca 2+ imaging in freely moving mice during the formation of nicotine preference behavior to examine the role of the dorsal-CA1 region of the hippocampus in encoding contextual reward-seeking behavior. We show the development of specific neuronal ensembles whose activity encodes nicotine-reward contextual memories and that are necessary for the expression of place preference. Our findings increase our understanding of CA1 hippocampal function in general and as it relates to reward processing by identifying a critical role for CA1 neuronal ensembles in nicotine place preference. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Learning Outlier Ensembles

DEFF Research Database (Denmark)

Micenková, Barbora; McWilliams, Brian; Assent, Ira

into the existing unsupervised algorithms. In this paper, we show how to use powerful machine learning approaches to combine labeled examples together with arbitrary unsupervised outlier scoring algorithms. We aim to get the best out of the two worlds—supervised and unsupervised. Our approach is also a viable......Years of research in unsupervised outlier detection have produced numerous algorithms to score data according to their exceptionality. wever, the nature of outliers heavily depends on the application context and different algorithms are sensitive to outliers of different nature. This makes it very...... difficult to assess suitability of a particular algorithm without a priori knowledge. On the other hand, in many applications, some examples of outliers exist or can be obtain edin addition to the vast amount of unlabeled data. Unfortunately, this extra knowledge cannot be simply incorporated...

Fiskeren som forsvant? En studie av avfolking, overbefolking og endringsprosesser i norsk fiskerinæring

OpenAIRE

Johnsen, Jahn Petter

2003-01-01

Hva er en fisker? Dette er det sentrale spørsmålet i denne avhandlinga. Spørsmålet er utgangspunkt for å utforske endringsprosesser i fiskerinæringa, både i fortida og i samtida. På bakgrunn av dette drøfter avhandlinga hvilke oppfatninger, forståelser og beskrivelser som til enhver tid benyttes til å definere hva fiskere er i ulike sammenhenger. Undersøkelsen starter i det som kan kalles rekrutteringsdiskursen i fiske, som nettopp handler om hva fiskere er og hvordan de skapes...

Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

Science.gov (United States)

Liu, Li; Xu, Yue-Ping

2017-04-01

Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

Didaktisk design af et pervasive learning game

DEFF Research Database (Denmark)

Oldenskov, Joakim; Westergren, Jens; Frank, Anders Bredahl

2010-01-01

Projektet er et studie i det teoretiske grundlag for, og en empirisk undersøgelse, af en prototype for et didaktisk design af et pervasive learning game. Der arbejdes overordnet med udviklingen af et didaktisk design af et læringsspil som kombinerer mobile teknologier, det fysiske rum og eleverne...... game med en analyse af legen og motivationen iboende spillet. I det læringsteoretiske felt inddrages og diskuteres forskellige tilgange til læring og pervasive games, som alle repræsenterer et socialkonstruktivistisk syn på læring. Det didaktiske design danner grundlag for et prototypedesign af et...... funktionalitet som en del af undervisningen. Det er afgørende at teknologien er pålidelig og er let at gå til, da det er kernefunktionen i denne type af spil. Legens rolle er vigtig for motivationen, men må ikke overtage de formelle krav til den undervisning, som spillet er del af. Det var entydigt omkring...

The canonical ensemble redefined - 1: Formalism

International Nuclear Information System (INIS)

Venkataraman, R.

1984-12-01

For studying the thermodynamic properties of systems we propose an ensemble that lies in between the familiar canonical and microcanonical ensembles. We point out the transition from the canonical to microcanonical ensemble and prove from a comparative study that all these ensembles do not yield the same results even in the thermodynamic limit. An investigation of the coupling between two or more systems with these ensembles suggests that the state of thermodynamical equilibrium is a special case of statistical equilibrium. (author)

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

Science.gov (United States)

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Machine-Learning Research

OpenAIRE

Dietterich, Thomas G.

1997-01-01

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

Feedback som indgang til læringsprocessen

DEFF Research Database (Denmark)

Kirkegaard, Preben Olund

2017-01-01

Feedback har til formål at forbedre dine studerendes færdigheder i et fag. Feedback skal tage udgangspunkt i den studerendes nuværende færdigheder og inkludere anvisninger på, hvordan den studerende fremadrettet kan arbejde for at opfylde et ønsket læringsmål. Måden hvorpå du som underviser...

Applications of Self-Organising Map (SOM) for prioritisation of endemic zones of filariasis in Andhra Pradesh, India.

Science.gov (United States)

Murty, Upadhayula Suryanaryana; Rao, Mutheneni Srinivasa; Sriram, K; Rao, K Madhusudhan

2011-01-01

Entomological and epidemiological data of Lymphatic Filariasis (LF) was collected from 120 villages of four districts of Andhra Pradesh, India. Self-Organising Maps (SOMs), data-mining techniques, was used to classify and prioritise the endemic zones of filariasis. The results show that, SOMs classified all the villages into three major clusters by considering the data of Microfilaria (MF) rate, infection, infectivity rate and Per Man Hour (PMH). By considering the patterns of cluster, appropriate decision can be drawn for each parameter that is responsible for disease transmission of filariasis. Hence, SOM will certainly be a suitable tool for management of filariasis. The detailed application of SOM is discussed in this paper.

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

Science.gov (United States)

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

enDNA-Prot: Identification of DNA-Binding Proteins by Applying Ensemble Learning

Directory of Open Access Journals (Sweden)

Ruifeng Xu

2014-01-01

Full Text Available DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97–9.52% in ACC and 0.08–0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83–16.63% in terms of ACC and 0.02–0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.

Pre-larp workshops as learning situations

DEFF Research Database (Denmark)

Bruun, Jesper

2011-01-01

I dette kapitel udforskes ligheder mellem prærollespilsworkshops og undervisningssituationer ved at bruge constructive alignment som en designramme. Analogien mellem workshops og undervisning udvikles ved at analysere, hvordan deltagere og arrangører arbejder med rollespilsværktøjet ars armandi i...... de workshops som blev afholdt før rollespillet Delirium (2010). Nogle af aktiviteterne i en prærollespilsworkshop minder meget om undervisnings- og læringsaktiviteter i universitetskurser der søger at anvende konstruktivistisk tankegang. Begrebet tilsigtede læringsresultater (intended learning...

Molecular characterization of charcoal to identify adsorbed SOM and assess the effectiveness of common SOM-removing pretreatments prior to radiocarbon dating

NARCIS (Netherlands)

Wagner, T.V.; Mouter, A.K.; Parsons, J.R.; Sevink, J.; van der Plicht, J.; Jansen, B.

A significant part of our knowledge on Holocene landscape development and associated human history in northwestern Europe is based on radiocarbon dating of charcoal originating from podzols. However, these soils are characterized by leaching of soil organic matter (SOM) that may adsorb to older

Eigenfunction statistics of Wishart Brownian ensembles

International Nuclear Information System (INIS)

Shukla, Pragya

2017-01-01

We theoretically analyze the eigenfunction fluctuation measures for a Hermitian ensemble which appears as an intermediate state of the perturbation of a stationary ensemble by another stationary ensemble of Wishart (Laguerre) type. Similar to the perturbation by a Gaussian stationary ensemble, the measures undergo a diffusive dynamics in terms of the perturbation parameter but the energy-dependence of the fluctuations is different in the two cases. This may have important consequences for the eigenfunction dynamics as well as phase transition studies in many areas of complexity where Brownian ensembles appear. (paper)

Measuring social interaction in music ensembles.

Science.gov (United States)

Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

2016-05-05

Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances. © 2016 The Author(s).

Soil organic matter (SOM) dynamics determined by stable isotope techniques

International Nuclear Information System (INIS)

Gerzabek, M. H.

1998-09-01

Being aware of limitations and possible bias the 13 C natural abundance technique using the different 13 C enrichments in plants with differing photosynthetic pathways in a powerful tool to quantify turnover processes, both in long-term field studies and short-term laboratory experiments. Special care is needed in choosing reference plots and the proper number of replicate samples. The combination of 13 C and 14 C measurements has a high potential for a further improvement of isotope techniques in SOM studies. Natural abundance of 15 N is less powerful with respect to quantification of SOM processes than the isotope dilution technique. However its usefulness could be distinctly improved by introducing other stable isotopes into the studies.(author)

Surhetsvariasjoner som følge av nedtapping av et regulert vann.

OpenAIRE

Selmer-Olsen, A. R.

1981-01-01

Det har vært utført analyser og lagringsforsøk med prøver av tørrlagt bunnmateriale fra Trevatn tatt våren 1976. Prøver tatt ute i terrenget om høsten etter en lang tørr sommer viste stort sett samme bilde som prøvene fra våren etter lagring i laboratoriet under aerobe betingelser. Tabell 2 viser hvordan pH og SO4-S varierer med Iagringsbetingelsene. Oksydasjonsprosessene som slikt materiale blir utsatt for ved lufttilgang over et lengre tidsrom kan resultere i utvasking av meget sure forbind...

Changes in SOM composition and stability to microbial degradation over time in response to wood chip ash fertilisation

DEFF Research Database (Denmark)

Hansen, Mette; Saarsalmi, Anna; Peltre, Clement

2016-01-01

spectroscopy (FTIR-PAS) analysis of bulk soil samples. Ash fertilisation of forest soils affected SOM composition in the O-horizon, but not in the top 5 cm of the mineral soil. The pH and biodegradability of SOM were increased in the O-horizon. The changes in SOM composition consisted of enrichment of Fe...... in Denmark, where ash had been spread at different times. Changes in SOM biodegradability were estimated based on an incubation experiment, expressed as percentage of initial carbon. Changes in SOM composition were characterised using thermal analysis and Fourier transform mid-infrared photoacoustic......- and Al-oxides/hydroxides, depletion of carboxylic and aromatic groups and lower thermal stability in soils with older and greater ash application. Ash fertilisation increased soil pH, either right after ash application or through a buffering effect of the ash on acidification caused by decomposing...

Joys of Community Ensemble Playing: The Case of the Happy Roll Elastic Ensemble in Taiwan

Science.gov (United States)

Hsieh, Yuan-Mei; Kao, Kai-Chi

2012-01-01

The Happy Roll Elastic Ensemble (HREE) is a community music ensemble supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of ensemble playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…

[SOMS-2: translation into portuguese of the screening for Somatoform Disorders].

Science.gov (United States)

Fabião, Cristina; Costa E Silva, Carolina; Fleming, Manuela; Barbosa, António

2008-01-01

The diagnosis of Somatization Disorder (SD) requires the presence of somatic medically unexplained symptoms (MUS) which must be assessed so that organic diseases may be excluded. SOMS-2 is a self-report measure for SD that assesses medically unexplained symptoms by requiring participants to answer affirmatively and qualify any of the complaints as MUS, only if they have obtained from his doctor the opinion that the said complaint is not due to an organic disease. According to the authors, original SOMS-2 has a good internal consistency with Cronbach's a = .87 and a good correlation between selfratings and interview (r = .75). After obtaining the author's permission, translation from and into English has been made by experienced translators. The resulting questionnaire has been used on a small group of patients. Afterwards the items in which there were difficulties in understanding during the pretest were identified and experienced practitioners were asked for suggestions. The resulting version was answered by 123 primary health care patients (sample I). After some modifications of the SOMS-2, another group of 190 primary health care patients answered the questionnaire (sample II). Most patients, in the first sample, found it difficult to understand that, in order to answer affirmatively it was necessary to answer three questions: 1) is the symptom present? 2) has your doctor found no clear causes for the symptom? 3) does the symptom affect your well-being? The difficulties in understanding items 21 and 45 (pre-test) were confirmed. Items 11, 28 and 38 were more easily understood when worded differently. In sample I, less than 5% of positive answers were given to items 20, 21, 23, 40, 43, 45, and 51. Probably because of the low education level of the Portuguese population which this sample reflects, difficulties in carrying out the instructions given at the beginning made it advisable to modify the SOMS-2, so that the three implicit questions in each question of the

E-learning og lærerkompetencer

Directory of Open Access Journals (Sweden)

Birgitte Heiberg

2004-06-01

Full Text Available Første gang publiceret i UNEV nr. 4: Undervisere og e-læring - problemer og perspektiver, september - december 2004, red. Poul Gøtke og Annette Lorentsen. ISSN 1603-5518. På flere uddannelsesinstitutioner udformes der i disse år strategier og politikker for e-learning og underviserne må således forholde sig til de muligheder som informations- og kommunikationsteknologien stiller til rådighed for deres undervisning. E-learning indebærer både teknologisk og pædagogisk omstilling: Underviserne må overveje og tilegne sig nye undervisningsformer, der måske baserer sig på nye læringsparadigmer end de kendte. De skal også tilegne sig nye teknologiske værktøjer og metoder. Ikke alle undervisere oplever det som en spændende udfordring. Der er derfor behov for at genoverveje hvordan kompetenceudviklingen for universitetsunderviserne skal organiseres i forhold til e-learning og læring generelt. I denne artikel vil jeg forsøge at belyse de barrierer kompetenceudvikling må overkomme på baggrund af de erfaringer vi har gjort os på CBS Learning Lab med kompetenceudviklingsinitiativer rettet mod e-learning, samt en nylig undersøgelse af CBS studerendes, underviseres og de studieadministrativt ansattes forhold til e-learning.

Ulikhet som impuls for nye oppdagelser i dans: Å tøye både muskler og meninger gjennom mangfold i Danselaboratoriet

Directory of Open Access Journals (Sweden)

Tone Pernille Østern

2014-12-01

Full Text Available I denne artikkelen har forfatterne fokus på hvordan ulikhet mellom mennesker kan fungere som en impuls som bidrar til meningsskaping, nye oppdagelser og transformasjon i improvisasjon i dans. Artikkelen tar utgangspunkt i Østerns avhandling ”Meaning-making in the Dance Laboratory. Exploring dance improvisation with differently bodied dancers” (2009. I artikkelen vever forfatterne sammen sine ulike stemmer. Tone Pernille Østerns perspektiv er som initiativtaker og tidligere koreograf-dansepedagog i Danselaboratoriet, mens Elen Øyens perspektiv er som danser i den samme gruppen. Øyen er danser og rullestolbruker. Hovedkonklusjonen i artikkelen er at gjennom å forstå ulikhet som en verdi og impuls til å møtes og skape i improvisasjon i dans, så kan deltakerne i gruppen gjennomgå kraftfulle transformative endringsprosesser når det gjelder synet på ”den andre”. Dette har en betydning som går langt ut over det konkrete, kunstneriske undervisningsrommet. ”Den andre” forstås i artikkelen som en konstruksjon som både muliggjør adskillelse og utestenging fra et fellesskap, men også som en mulighet til å forstå at ”den andre” ikke nødvendigvis er, eller skal være, som meg.

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

Science.gov (United States)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

SOM storage and pool distribution in forest soils along climatic and altitudinal gradients across Switzerland

Science.gov (United States)

Gosheva, Sia; Müller, Mirjam; Walthert, Lorenz; Zimmermann, Stephan; Niklaus, Pascal A.; González Domínguez, Beatriz R.; Abiven, Samuel; Hagedorn, Frank

2016-04-01

Soil organic matter (SOM) plays a key role for a number of soil and ecosystem functions. Yet our quantitative understanding of the main driving factors is uncertain. SOM consists of a continuum of compounds ranging from slightly altered plant residues, known as particulate OM (POM) to mineral-associated OM (mOM). POM is the most rapidly cycling and hence vulnerable fraction of SOM. Therefore, it might respond particularly sensitive to climate change. In grassland soils, SOM content as well as the contribution of POM was found to increase with increasing elevation, which suggests that climate exerts a major control on SOM stability and storage. Little is known, however, for forest soils where a substantial fraction of POM is stored in the organic layer. In our study based on 1000 soil profiles, we explore the controlling factors of SOM stocks and the distribution of POM in the organic layer as well as within mineral soils in forests across Switzerland. We hypothesize that (i) elevation and hence climate have rather negligible effects on carbon (C) stocks, but exert large effects on SOM quality (contribution of POM, SOM depth distribution, and C/N ratio); (ii) furthermore, we postulate to find an elevational effect on C stocks in the organic layer but not in the mineral soil. We examined SOM stocks in the organic layer and the mineral soil of 1000 soil profiles. Mineral soils (0-20cm) from a subset of 54 sites were separated into free light fraction and occluded light fraction, representing POM, while fine heavy fraction and coarse heavy fraction represented the mineral-associated OM. The sites, all located in Swiss forests, were distributed along a great elevational gradient ranging between 277 and 2207 m a.s.l., and spanning a gradient in mean annual temperatures (MAT) from 0.6 to 11.9 °C, and mean annual precipitation (MAP) from 704 to 2340 mm. Our results indicate that POM and C/N ratio are more closely related to elevation and climate compared to mOM. For C

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

Science.gov (United States)

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

Selected Influences on Solo and Small-Ensemble Festival Ratings: Replication and Extension

Science.gov (United States)

Bergee, Martin J.; McWhirter, Jamila L.

2005-01-01

Festival performance is no trivial endeavor. At one midwestern state festival alone, 10,938 events received a rating over a 3-year period (2001-2003). Such an extensive level of participation justifies sustained study. To learn more about variables that may underlie success at solo and small ensemble evaluative festivals, Bergee and Platt (2003)…

Ensembl 2002: accommodating comparative genomics.

Science.gov (United States)

Clamp, M; Andrews, D; Barker, D; Bevan, P; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Hubbard, T; Kasprzyk, A; Keefe, D; Lehvaslaiho, H; Iyer, V; Melsopp, C; Mongin, E; Pettett, R; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Birney, E

2003-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web site or as flat files. Ensembl also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in Ensembl have focusing on developing automatic comparative genome analysis and visualisation.

Rushdie som symbol på blasfemi mod islam

DEFF Research Database (Denmark)

Jacobsen, Brian Arly

2012-01-01

Vil man forstå dødsdommen over Rushdie, skal man forstå billederne om Iran, der er konstrueret af Rushdie i De Sataniske Vers. Det handler om Rushdies brug af forskellige narrativer, eksempelvis brugen af begrebet 'Jahilia', som er velkendt af alle muslimer, der anvendes til at karakterisere Mekk...

Sansning som redskab og metode for faget Modedesign

DEFF Research Database (Denmark)

Ræbild, Ulla

2009-01-01

  Abstract / dansk Intentionen i denne afhandling er at undersøge, hvornår og hvordan designere anvender sansemæssig viden og kompetence i deres arbejdsproces, for igennem denne afdækning at kunne foretage en eksplicitering og artikulation af sansning som redskab og metode i designprocessen. Afha...

Socialpædagogik iagttaget som et socialt praksisfelt

DEFF Research Database (Denmark)

Hansen, Janne Hedegaard

2013-01-01

Ved at iagttage socialpædagogers situerede handlinger i praksis er det muligt at opnå viden om de forståelser, socialpædagoger handler på grundlag af, og som sætter betingelser for brugernes handle- og livsmuligheder. Men brugerne er ikke blot underlagt socialpædagogers betydningskonstruktioner...

Pave Benedikt XVI som social kritiker og moderniseringsteoretiker

DEFF Research Database (Denmark)

Thomassen, Bjørn

2013-01-01

former for kritik af det moderne projekt, en kritik som deler mange lighedspunkter med Frankfurterskolen. Disse ligheder kom blandt andet til syne i Kardinal Ratzingers berømte debat med Habermas, men ligeledes i nogle af hans seneste skrifter, især hans meget omtalte tredje encyklik, Caritas in Veritate...

The classicality and quantumness of a quantum ensemble

International Nuclear Information System (INIS)

Zhu Xuanmin; Pang Shengshi; Wu Shengjun; Liu Quanhui

2011-01-01

In this Letter, we investigate the classicality and quantumness of a quantum ensemble. We define a quantity called ensemble classicality based on classical cloning strategy (ECCC) to characterize how classical a quantum ensemble is. An ensemble of commuting states has a unit ECCC, while a general ensemble can have a ECCC less than 1. We also study how quantum an ensemble is by defining a related quantity called quantumness. We find that the classicality of an ensemble is closely related to how perfectly the ensemble can be cloned, and that the quantumness of the ensemble used in a quantum key distribution (QKD) protocol is exactly the attainable lower bound of the error rate in the sifted key. - Highlights: → A quantity is defined to characterize how classical a quantum ensemble is. → The classicality of an ensemble is closely related to the cloning performance. → Another quantity is also defined to investigate how quantum an ensemble is. → This quantity gives the lower bound of the error rate in a QKD protocol.

40 år gamle kvinner som bruker hormonregimer – sunne utvalg eller risikogrupper?

Directory of Open Access Journals (Sweden)

Sidsel Graff-Iversen

2009-11-01

Full Text Available  SAMMENDRAGFormålet med denne artikkelen var å studere brukerne av kombinerte antikonsepsjonsmidler, regimer med progesteronalene og østrogensubstitusjon blant norske 40-åringer. Var det først og fremst lavrisikogrupper – friske ogrøykfrie kvinner – som brukte p-piller av kombinasjonstype, slik gjeldende anbefalinger råder til? Var progesteronbrukerneet utvalg av særlig helsebevisste kvinner? Hvor utbredt var bruken av østrogen i denne aldersgruppen?Var det tegn til at østrogenbrukerne er en “healthy selection”, slik utenlandske studier har vist?Materialet er fra 40-åringsundersøkelser i 11 norske fylker i tiden 1997-99. Resultatene viser at 3,5% avkvinnene brukte kombinerte antikonseptiva og at disse utgjorde et “sunt utvalg” med lave andeler røykere og godhelse. De 9,4% av kvinnene som brukte progesteron alene, skilte seg derimot lite fra premenopausale kvinner utenhormonbruk. De 2,4% av kvinnene som brukte østrogen, hadde mindre god helse, høyere andel røykere og lavereutdanningsnivå, sammenlignet med premenopausale kvinner. Men sammenligning innen gruppen av post- og perimenopausalekvinner viste ingen vesentlig forskjell mellom østrogenbrukerne og andre. Resultatet fra den førstekontrollerte studien av østrogen og hjertesykdom kom i 1998, men førte ikke til noen påviselig forskjell i seleksjonentil østrogenbruk fra 1997 til 1999.Konklusjonen er at kvinnene som brukte kombinerte antikonseptiva i 40-årsalderen var en “sunn seleksjon” itråd med det som blir anbefalt, mens de som brukte progesteron alene skilte seg lite ut fra kvinner som ikke bruktehormoner. Kvinner som brukte østrogen i denne unge alderen, sto fram som en helsemessig risikogruppe imaterialet som helhet, men skilte seg lite ut fra andre antatt post- eller perimenopausale kvinner i 40-årsalderen.Graff-Iversen, S. 40-year old female sex hormone users: healthy selections or risk groups? Results fromhealth surveys in 11 Norwegian

Machine learning in the string landscape

Science.gov (United States)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Energy Technology Data Exchange (ETDEWEB)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

2014-03-15

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

International Nuclear Information System (INIS)

Singh, Kunwar P.; Gupta, Shikha

2014-01-01

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R 2 ) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R 2 and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

Musik som værk og handling

DEFF Research Database (Denmark)

Vandsø, Anette

Denne bog handler om den nye musik som både har værkkarakter men også en udpræget handlingskarakter. Her sys på symaskiner og smadres violiner, men hvad gør det ved værkkarakteren? ved værkanalysen ? og ved 'musikken'? På baggrund af disse spørgsmål tilbyder bogen en grundlagsteoretisk gentænkning...

Studiemiljøundersøgelse som empowerment

DEFF Research Database (Denmark)

Keller, Hanne Dauer; Jensen, Annie Aarup

2012-01-01

Artiklen fokuserer på det metodemæssige i at arbejde med studiemiljø og studiemiljøundersøgelser i en kvalitativ, procesorienteret form, der står i kontrast til de traditionelle kvantitative metoder. En alternativ organisering med de studerende som ansvarlige aktører gav andre, mere nuancerede da...... datatyper og aktiverede de studerende i forhold til at påvirke eget studiemiljø....

Data splitting for artificial neural networks using SOM-based stratified sampling.

Science.gov (United States)

May, R J; Maier, H R; Dandy, G C

2010-03-01

Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified sampling, based on Neyman sampling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and sample allocation in order to minimize the bias and variance in the datasets. Using an example ANN function approximation task, the SOM-based approach is evaluated in comparison to random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality samples and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets. Copyright 2009 Elsevier Ltd. All rights reserved.

Faktorer som påverkar barndödligheten i Etiopien

OpenAIRE

Östman, Hanna

2014-01-01

Temat för detta examensarbete är barndödlighet i Etiopien. Syftet är att undersöka den höga småbarnsdödligheten, orsakad av diarré och malaria. Detta är ett beställningsarbete för organisationen PADet, som har ett samarbete med yrkeshögskolan Arcada. Med småbarn menas i denna studie, barn under fem år. Frågeställningarna för arbetet är, Vilka är de främsta orsakerna som påverkar småbarnsdödligheten vid diarré och malaria? Och Vad görs idag för att minska på småbarnsdödligheten vid diarré och ...

Online lektiehjælp – Udvikling af en vejledningsdidaktik med pædagogisk designforskning som metode

Directory of Open Access Journals (Sweden)

Jens Jørgen Hansen

2016-05-01

af koncepter og modeller som: Lektie-vejlederens kompetenceprofil, Vejledningskompasset, Vejledningens scener og Vejledningsstrategimodellen – og evalueringen af disse modellers pædagogiske værdi. For det andet demonstrerer artiklen, hvordan forskerteamet metodisk har fortolket og udmøntet den pædagogiske designforsknings metode til ud-vikling af en vejledningsdidaktik. -- Homework counselling is a fast growing pedagogical practice where focus is on supporting students in understanding and handling challenges in their homework. Homework counselling is on the political agenda in schools and secondary schools and is offered both internally by the educational institutions as well as private and public operators. Homework counselling however is not established as a theoretical field or as a specific counselling competence. Hence there is a need to conceptualise and devel-op theories about what homework counselling is and how the form of counselling can be handled didactical. In concrete terms the challenge is to develop a nuanced terminology and a modelling of the field to explain the complex practices of homework counselling with the aim to develop and qualify homework counselling as a pedagogical theory and pedagogical practice. This article presents the result of a research project, which aim is to devel-op a counselling didactic, including methods, models and materials for homework counselling within the framework of Homework Online. Home-work Online is an organisation at the State Library in Aarhus, Denmark, which offers a place for online homework guidance where i.a. secondary school students can get help from volunteer university students. This didactical design is an example on how digitisation and new technology acts as a catalyst for developing new innovative learning environments and possibilities for learning, which opens flexible and spaces for learning irrespective of place, allowing interactions with external operators outside the formal school

Specialet som glasloft – hvorfor får mandlige studerende bedre specialekarakterer?

Directory of Open Access Journals (Sweden)

Vibeke Lehmann Nielsen

2012-03-01

Full Text Available Selvom de mandlige universitetsstuderende ikke klarer sig bedre end kvinderne frem til specialet, får de gennemsnitligt bedre specialekarakterer. Artiklens analyser viser, at det hverken handler om specialevejlederens køn eller om typen af universitetsstudium. Mulige forklaringer, som det er værd for kommende forskning at forfølge, er dels generel diskrimination af kvindelige specialestuderende og dels de studerendes møde med specialet, hvor kønnenes livssituation og/eller deres reaktioner på rollen som specialestuderende kan være forskellige.

Conducting Expressively: Navigating Seven Misconceptions That Inhibit Meaningful Connection to Ensemble and Sound

Science.gov (United States)

Snyder, Courtney

2016-01-01

When expressivity (ignited by imagination) is incorporated into the learning process for both the conductor (teacher) and player (student), the qualities of movement, communication, instruction, and ensemble sound all change for the better, often with less work. Expressive conducting allows the conductor to feel more connected to the music and the…

The semantic similarity ensemble

Directory of Open Access Journals (Sweden)

Andrea Ballatore

2013-12-01

Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.

Digital portfolio som metode og pædagogik

DEFF Research Database (Denmark)

Toft, Hanne; Luplau Schnefeld, Mette

2004-01-01

Artiklen præsenterer erfaringer med digital portfolio som didaktik og metode. Fokus er på kobling mellem didaktiske læreprocesser på en grunduddannelse og så understøttelse af disse via brug af en digital portfolio. Portfolioen adskiller en række forskellige dokumentsamlinger i privat og offentlig...

Social outcomes of learning - Response to paper by David Campwell

DEFF Research Database (Denmark)

Andersen, John

Expert kommentar til rapportudkast fra David Cambell (tidligere forskningsassistent for Robert Putman) i OECD projektet SOL (Social Outcomes of Learning). Publiceres senere som Discussionpaper af OECD...

Musical ensembles in Ancient Mesapotamia

NARCIS (Netherlands)

Krispijn, T.J.H.; Dumbrill, R.; Finkel, I.

2010-01-01

Identification of musical instruments from ancient Mesopotamia by comparing musical ensembles attested in Sumerian and Akkadian texts with depicted ensembles. Lexicographical contributions to the Sumerian and Akkadian lexicon.

PSO-Ensemble Demo Application

DEFF Research Database (Denmark)

2004-01-01

Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output...

Multilevel ensemble Kalman filter

KAUST Repository

Chernov, Alexey; Hoel, Haakon; Law, Kody; Nobile, Fabio; Tempone, Raul

2016-01-01

This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

Multilevel ensemble Kalman filter

KAUST Repository

Chernov, Alexey

2016-01-06

This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

An ensemble self-training protein interaction article classifier.

Science.gov (United States)

Chen, Yifei; Hou, Ping; Manderick, Bernard

2014-01-01

Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

Luhmanns masmedieteori och Internet som ett artificiellt intelligent semiotiskt system Luhmanns massmedieteori och Internet som ett artificiellt intelligent semiotiskt system [Luhmann’s mass-media theory and Internet as an artificial intelligent semiotic system

Directory of Open Access Journals (Sweden)

Peter Kåhre

2010-11-01

Full Text Available Artikeln diskuterar hur en modern form av AI-programmering, som kallas Konnektionism i en design som kallas Distribuerad AI (DAI, påverkar den uppfattning Luhmann har om massmediernas roll för den andra ordningens observationer. DAI använder noder för att skapa aktivitet i systemen och inte de koder som styr processerna i den klassiska eller symboliska formen av AI. Luhmanns teori kan utvecklas genom att ersätta systemens koder med noder som förändras beroende på i vilken relation de står till andra noder. På så sätt kan kommunikationsbegreppet utvecklas så att det också omfattar systemens interaktioner med omvärlden. Det skapar en bättre förutsättning för att observationsmöjligheter direkt uppstår genom systemens relationer till omvärlden. Internet och AI-programmerade söksystem och robotar kan då fungera som ett artificiellt semiotiskt system som skapar möjligheter att göra observationer.The article discusses how a modern form of AI programming, known as Connectionism in a design known as Distributed Artificial Intelligence (DAI, affects the perception Luhmann has on mass media's role concerning second-order observations. DAI uses nodes to create activity in the systems and not the codes used by the Classic or Symbolic form of AI. Luhmann’s theory can be developed by replacing the systems codes with nodes that change depending on their relations to other nodes. In this way, we can reformulate the concept of communication, so that it includes the systems interactions with the environment. It creates better conditions so that observing opportunities may arise directly from these interactions. Internet and AI-programmed search systems and robots can then act as an artificial semiotic system that creates opportunities for making observations.

Som i et spejl - Hans Egede-receptionen gennem tre hundrede år

DEFF Research Database (Denmark)

Kjærgaard, Kathrine

2008-01-01

stor, og længe ville man ikke vide af Hans Egede, der - skønt født i Norge - opfattedes som dansk. Først efter 1945, hvor Norge i kølvandet på Danskehjælpen under Anden Verdenskrig omsider forsonede sig med Danmark, tog man atter Hans Egede til sig. Blandt større mindesmærker, som er blevet til siden...... 1945, kan nævnes Nicolai Schiølls ungdommelige statue af grønlandsmissionæren i det centrale Oslo (1965) og Axel Revolds monumentale udskmykning af kirken i Hans Egedes nordnorske fødeby Harstad (1958). I Danmark, hvor sorgen over tabet af Norge var stor, var man i årene efter 1814 meget...... tilbageholdende med at omklamre tidligere norske landsmænd som Hans Egede. Først hen mod slutningen af 1800-tallet begyndte forbeholdene at smelte et efter et. Den definitive omdefinering af den dansk-norske 1700-tals patriot til national dansk kirkefyrste kom med opstillingen 1913 af hans statue ved Marmorkirken...

Multi-Model Ensemble Wake Vortex Prediction

Science.gov (United States)

Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

2015-01-01

Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

Using support vector machine ensembles for target audience classification on Twitter.

Science.gov (United States)

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Using support vector machine ensembles for target audience classification on Twitter.

Directory of Open Access Journals (Sweden)

Siaw Ling Lo

Full Text Available The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA. A Support Vector Machine (SVM ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Energy Technology Data Exchange (ETDEWEB)

Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics

Institute of Scientific and Technical Information of China (English)

Zhaoxia PU; Joshua HACKER

2009-01-01

This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.

Den Pædagogiske Refleksionsmodel som sammenhængskraft

DEFF Research Database (Denmark)

Voergaard Poulsen, Bettina; Lucht, Camill; Vibholm Persson, Stine

Den Pædagogiske Refleksions Model (PRM) illustrerer de vidensdimensioner, der indgår i en kvalificeret klinisk beslutning. I projektet 'Sammenhæng på TVÆRS i fremtidens sygeplejeuddannelse (PÅ TVÆRS)" indgår PRM som pædagogisk greb. Der undersøges om PRM skaber sammenhæng for studerendes læring i...

Sociale teknologier og web 2.0 som vidensmedier

DEFF Research Database (Denmark)

Johannsen, Carl Gustav

2011-01-01

Artiklen fokuserer på effekten af brugen af sociale teknologier i folkebibliotekerne med fokus på i hvilken udstrækning de sociale teknologier lader sig forstå og analysere som vidensmedier, kommunikationsmønstre ved brug af sociale medier, de sociale mediers virkninger i undervisningssammenhænge...... og endelig de kulturpolitiske konsekvenser af bibliotekernes inddragelse af sociale teknologier....

Monthly ENSO Forecast Skill and Lagged Ensemble Size

Science.gov (United States)

Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

2018-04-01

The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

Lyd som kommunikation. En tværfaglig forskningsoversigt og en dagsorden for medieforskningen

Directory of Open Access Journals (Sweden)

Klaus Bruhn Jensen

2008-06-01

Full Text Available Lyden har ikke noget videnskabeligt hjem – i modsætning til f.eks. faste og levende billeder, der traditionelt er blevet studeret af henholdsvis kunsthistorien og filmvidenskaben. Denne artikel giver en oversigt over de mange, meget forskellige slags forskning om lyd. Formålet er at stimulere og kvalificere mere medieforskning om lyd. Hovedvægten ligger på lyd som kommunikation i og omkring medierne – som en kilde til mening og en ressource i social og kulturel handling, snarere end på lyd i et teknisk eller akustisk perspektiv (se f.eks. Plomp, 2002. Første del af artiklen gennemgår forskellige humanistiske og samfundsvidenskabelige tilgange til lydens tre grundformer – tale, musik og soundscapes eller lydmiljøer. Det midterste afsnit diskuterer den tidligere forsknings relevans for forskellige slags medier; artiklen skelner her mellem tre grader af medier. Og artiklens sidste del samler de tværfaglige indsigter i en dagsorden for (meget mere medieforskning om lyd som kommunikation.

Lyd som kommunikation. En tværfaglig forskningsoversigt og en dagsorden for medieforskningen

Directory of Open Access Journals (Sweden)

Klaus Bruhn Jensen

2006-06-01

Full Text Available Lyden har ikke noget videnskabeligt hjem – i modsætning til f.eks. faste og levende billeder, der traditionelt er blevet studeret af henholdsvis kunsthistorien og filmvidenskaben. Denne artikel giver en oversigt over de mange, meget forskellige slags forskning om lyd. Formålet er at stimulere og kvalificere mere medieforskning om lyd. Hovedvægten ligger på lyd som kommunikation i og omkring medierne – som en kilde til mening og en ressource i social og kulturel handling, snarere end på lyd i et teknisk eller akustisk perspektiv (se f.eks. Plomp, 2002. Første del af artiklen gennemgår forskellige humanistiske og samfundsvidenskabelige tilgange til lydens tre grundformer – tale, musik og soundscapes eller lydmiljøer. Det midterste afsnit diskuterer den tidligere forsknings relevans for forskellige slags medier; artiklen skelner her mellem tre grader af medier. Og artiklens sidste del samler de tværfaglige indsigter i en dagsorden for (meget mere medieforskning om lyd som kommunikation.

Infiltrationsanæstesi som postoperativ smertebehandling ved hoftenær femurfraktur - et igangværende studie

DEFF Research Database (Denmark)

Bech, Rune Dueholm

2008-01-01

pertrochantær femurfraktur og collum femoris fraktur inkluderes. 50 patienter indgår i hver af de to diagnose-grupper (to delstudier). Ved randomisering tildeles patienterne hhv. Ropivacain eller placebo (NaCl), der gives som 1 peroperativ og 6 postoperative bolusinstillationer via et kateter, som fjernes efter...

Adversary phase change detection using S.O.M. and text data

International Nuclear Information System (INIS)

Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

2010-01-01

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Credit scoring using ensemble of various classifiers on reduced feature set

Directory of Open Access Journals (Sweden)

Dahiya Shashi

2015-01-01

Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

New technique for ensemble dressing combining Multimodel SuperEnsemble and precipitation PDF

Science.gov (United States)

Cane, D.; Milelli, M.

2009-09-01

The Multimodel SuperEnsemble technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the ensemble dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.

Rommet som den tredje pedagog - En studie av pedagogers forståelse av rommets betydning - med fokus på estetisk virksomhet

OpenAIRE

Krokstad, Inger Elisabeth

2014-01-01

Masteroppgaven «Rommet som den tredje pedagog i tre Reggio Emilia-inspirerte barnehager. En studie av pedagogers forståelse av rommets betydning –med fokus på estetisk virksomhet» har til hensikt å bidra til økt kunnskap om estetisk virksomhet som er forbundet med Reggio Emilias tanke om rommet som den tredje pedagog. Avhandlingens problemstilling er: Hvilke mønster kan identifiseres i barnehagepedagogens forståelse av rommet som den tredje pedagog-med fokus på estetisk virksomhet? ...

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

Science.gov (United States)

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

SINs and SOMs: Neural microcircuits for size tuning in the zebrafish and mouse visual pathway.

Directory of Open Access Journals (Sweden)

Alison J. Barker

2013-05-01

Full Text Available In many animals, a fast and reliable circuit for discriminating between predator-sized objects and edible (prey-sized objects is necessary for survival. How are receptive fields in visual brain areas organized to extract information about size? Recent studies from the zebrafish optic tectum and the mouse visual cortex suggest de novo shaping of receptive fields by subtypes of inhibitory neurons. Del Bene et al. (2010 describe a population of GABAergic neurons in the zebrafish optic tectum (Superficial Interneurons, SINs that are necessary for size filtering during prey capture. Adesnik et al. (2012 describe a somatostatin-expressing interneuron population (SOMs that confers surround suppression on layer II/III pyramidal cells in mouse V1. Strikingly both the SINs and the SOMs, display size-dependent response properties. Increasing visual stimulus size increases excitatory input to these neurons. Dampening SIN or SOM activity alters tuning of neighboring circuits such that they lose preference for small objects. Both results provide exciting evidence for mechanisms of size filtering in visual circuits. Here we review the roles of the SINs and the SOMs and speculate on the similarity of such spatial filters across species.

Som Energia: sostenibilitat energètica i cooperativisme

OpenAIRE

Duran i Grant, Alexandre; Huijink, Gijsbert; Roselló, Marc

2013-01-01

La cooperativa Som Energia és una organització innovadora en el sector energètic a casa nostra. Proposa una forma de fer diferent de la de les empreses tradicionals del sector: ven energia renovable certificada, no té ànim de lucre i busca el bé comú. La proposta treballa per la sostenibilitat energètica.

An Organic Computing Approach to Self-organising Robot Ensembles

Directory of Open Access Journals (Sweden)

Sebastian Albrecht von Mammen

2016-11-01

Full Text Available Similar to the Autonomous Computing initiative, that has mainly been advancing techniques for self-optimisation focussing on computing systems and infrastructures, Organic Computing (OC has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behaviour of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g. by means of adapting to changing goals and conditions.Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organise themselves to fulfil spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

A Theoretical Analysis of Why Hybrid Ensembles Work

Directory of Open Access Journals (Sweden)

Kuo-Wei Hsu

2017-01-01

Full Text Available Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

Bysykler : perspektiver på bysykkelordninger som en del av en bærekraftig bytransport og urban identitet

OpenAIRE

Langfeldt, Tuva

2011-01-01

SAMMENDRAG - Byutviklingen er de senere årene preget av grønne visjoner og planlegging for levende og bærekraftige byer. Et viktig ledd i denne utviklingen er menneskelig bevegelse og mobilitet, og det ses flere og flere steder med nye øyne på hvordan mobiliteten i byen kan og bør være. Fokus på sykkel som framkomstmiddel har blitt stadig mer populært, i Europa som USA, og som et resultat av behov for en mer syklende og mindre bilkjørende befolkning er det dukket opp bysykkelordninger i bå...

Bearing Condition Recognition and Degradation Assessment under Varying Running Conditions Using NPE and SOM

Directory of Open Access Journals (Sweden)

Shaohui Zhang

2014-01-01

Full Text Available Manifold learning methods have been widely used in machine condition monitoring and fault diagnosis. However, the results reported in these studies focus on the machine faults under stable loading and rotational speeds, which cannot interpret the practical machine running. Rotating machine is always running under variable speeds and loading, which makes the vibration signal more complicated. To address such concern, the NPE (neighborhood preserving embedding is applied for bearing fault classification. Compared with other algorithms (PCA, LPP, LDA, and ISOP, the NPE performs well in feature extraction. Since the traditional time domain signal denoising is time consuming and memory consuming, we denoise the signal features directly in feature space. Furthermore, NPE and SOM (self-organizing map are combined to assess the bearing degradation performance. Simulation and experiment results validate the effectiveness of the proposed method.

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Science.gov (United States)

Wei, Jiangfeng; Dirmeyer, Paul A.; Yang, Zong-Liang; Chen, Haishan

2017-10-01

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Content Management som platform og pædagogisk strategi

DEFF Research Database (Denmark)

Petersen, Kim Bach; Rasmussen, Steen Cnops

2005-01-01

Med afsæt i konkrete erfaringer på pædagoguddannelsen argumenteres der for, at implementeringen af it-systemer og udviklingen af de ansattes it-kompetencer bør tage afsæt i en pædagogisk tænkning, som prioriterer enkelthed og brugervenlighed højt....

Importancia del conteo de células somáticas en la calidad de la leche

OpenAIRE

Hernández Reyes, Juan Manuel; Bedolla Cedeño, José Luís Carlos

2008-01-01

Este trabajo es una revisión sobre la importancia de las células somáticas en la calidad de la leche. Las células somáticas son, entre otras, células blancas propias del organismo que le sirven como defensa a la glándula mamaria de la vaca contra organismos patógenos. La determinación del contenido de células somáticas de la leche del tanque, o de los cuartos de la glándula mamaria de las vacas, es el medio auxiliar de diagnóstico más importante para juzgar el estado de salud de la ubre de un...

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Directory of Open Access Journals (Sweden)

Drzewiecki Wojciech

2017-12-01

Full Text Available We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Science.gov (United States)

Drzewiecki, Wojciech

2017-12-01

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.

Soil Response to Natural Vegetation Dynamics During the Late Holocene in Minnesota, USA, and Implications for SOM Accumulation and Loss

Science.gov (United States)

Mason, J. A.; Kasmerchak, C. S.; Keita, H.; Gruley, K. E.

2016-12-01

We studied soil response to late Holocene shifts in the dynamic boundary between forest and grassland, in two contrasting landscapes of Minnesota, USA. On both the glaciated landscape of northwestern Minnesota and steep bedrock slopes of southeastern Minnesota, forest has replaced grassland in the late Holocene (after 4 ka in the NW, during at least the last few 100 yr in the SE). Two distinct soil morphologies coexist in essentially the same climate and parent materials, Mollisols with deep SOM accumulation under grassland and Alfisols with most SOM in thin A horizons under forest. Organic carbon stocks of the Mollisols we sampled (to 1 m depth) are at least 50% greater than those of the Alfisols; thus, replacement of grassland by forest involves substantial SOM loss. Ultimately, the transition from Alfisols to Mollisols can probably be explained by much lower proportions of belowground SOM addition, and possibly less bioturbation, under forest; however, the timescale of this change is of great interest. Mollisols and transitional soils occur under forest today near the 19th century location of the vegetation boundary in NW Minnesota, and in certain slope positions in SE Minnesota. Stable C isotope profiles within those soils record the transition from C4 or mixed C3/C4 vegetation (tallgrass prairie or savanna) to C3 forest vegetation. Combined with 14C dating these data demonstrate a substantial lag in loss of the Mollisol morphology—thick SOM-rich A horizons with highly stable aggregates—after forest occupation. In fact, these thick A horizons may persist even when C4 grass-derived SOM has largely been replaced by SOM added after forest occupation. We are exploring possible explanations for this persistence in NW Minnesota. In SE Minnesota, it is likely related to parent material rich in dolomite fragments, with stable aggregation and SOM accumulation favored by abundant Ca2+and Mg2+. This parent material effect results in localization of high SOM

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

Science.gov (United States)

Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

Learning About Climate and Atmospheric Models Through Machine Learning

Science.gov (United States)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

The hippocampal CA2 ensemble is sensitive to contextual change.

Science.gov (United States)

Wintzer, Marie E; Boehringer, Roman; Polygalov, Denis; McHugh, Thomas J

2014-02-19

Contextual learning involves associating cues with an environment and relating them to past experience. Previous data indicate functional specialization within the hippocampal circuit: the dentate gyrus (DG) is crucial for discriminating similar contexts, whereas CA3 is required for associative encoding and recall. Here, we used Arc/H1a catFISH imaging to address the contribution of the largely overlooked CA2 region to contextual learning by comparing ensemble codes across CA3, CA2, and CA1 in mice exposed to familiar, altered, and novel contexts. Further, to manipulate the quality of information arriving in CA2 we used two hippocampal mutant mouse lines, CA3-NR1 KOs and DG-NR1 KOs, that result in hippocampal CA3 neuronal activity that is uncoupled from the animal's sensory environment. Our data reveal largely coherent responses across the CA axis in control mice in purely novel or familiar contexts; however, in the mutant mice subject to these protocols the CA2 response becomes uncoupled from CA1 and CA3. Moreover, we show in wild-type mice that the CA2 ensemble is more sensitive than CA1 and CA3 to small changes in overall context. Our data suggest that CA2 may be tuned to remap in response to any conflict between stored and current experience.

Random ensemble learning for EEG classification.

Science.gov (United States)

Hosseini, Mohammad-Parsa; Pompili, Dario; Elisevich, Kost; Soltanian-Zadeh, Hamid

2018-01-01

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility. Copyright © 2017 Elsevier B.V. All rights reserved.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

Science.gov (United States)

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

Reklamen som pastiche. Om Dansk Sygeplejeråds annoncekampagne efteråret 1986

Directory of Open Access Journals (Sweden)

Gunhild Agger

1989-08-01

Full Text Available Der er flere og flere medieproducenter, der anser modtagerne for at besidde en vis tekstuel intelligens. Fra detektivpasticher som Dick Spanner og Batman over den konstant genreparodierende "Moon- lighting" (De heldige helte til Poul og Nulles respektløse faktion "I sandhedens Tjeneste" leges der med betydningsdannelsen. Seerne/læserne indbydes til selv at spille ping-pong med billeder og tekst. Gunhild Agger analyserer her denne tendens, sådan som den kom- mer til udtryk inden for reklamen: Anker Jørgensen som James Bond, og kondomer til Prinsessen på Ærten! Mest indgående beskæftiger hun sig med Dansk Sygeplejeråds brug af myten om sygeplejersken i en annoncekampagne op mod overenskomstforhandlingerne i 1987. Gunhild Agger nøjes ikke med en indholdsanalyse af annoncerne, men beskriver hele kampagnen: Dansk Sygeplejeråds hensigter med frem- stødet, kampagnens lancering over for den tre-dobbelte målgruppe, læsernes formodede reception, og kampagnens politiske og holdnings- mæssige effekter.

New concept of statistical ensembles

International Nuclear Information System (INIS)

Gorenstein, M.I.

2009-01-01

An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution is introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

A class of energy-based ensembles in Tsallis statistics

International Nuclear Information System (INIS)

Chandrashekar, R; Naina Mohammed, S S

2011-01-01

A comprehensive investigation is carried out on the class of energy-based ensembles. The eight ensembles are divided into two main classes. In the isothermal class of ensembles the individual members are at the same temperature. A unified framework is evolved to describe the four isothermal ensembles using the currently accepted third constraint formalism. The isothermal–isobaric, grand canonical and generalized ensembles are illustrated through a study of the classical nonrelativistic and extreme relativistic ideal gas models. An exact calculation is possible only in the case of the isothermal–isobaric ensemble. The study of the ideal gas models in the grand canonical and the generalized ensembles has been carried out using a perturbative procedure with the nonextensivity parameter (1 − q) as the expansion parameter. Though all the thermodynamic quantities have been computed up to a particular order in (1 − q) the procedure can be extended up to any arbitrary order in the expansion parameter. In the adiabatic class of ensembles the individual members of the ensemble have the same value of the heat function and a unified formulation to described all four ensembles is given. The nonrelativistic and the extreme relativistic ideal gases are studied in the isoenthalpic–isobaric ensemble, the adiabatic ensemble with number fluctuations and the adiabatic ensemble with number and particle fluctuations

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

Science.gov (United States)

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

The Ensembl genome database project.

Science.gov (United States)

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Programproduktion som Kritisk Teori – eller tv-teksten som industriel iscenesættelse

Directory of Open Access Journals (Sweden)

John Caldwell

2003-09-01

Full Text Available Denne artikel peger på nødvendigheden af en revurdering af den tre-delte model, som Fiske og Gripsrud har fremført, ved at vise, hvorledes ‘sekundære’ og ‘tertiære’ tv-tekster uafladeligt bevæger sig eller rejser hen imod en ‘primær’ tekstuel status i det amerikan- ske multikanals-»flow«. En detaljeret gennemgang af industriens tekstuelle praksis – programbegivenheder, network-branding, ka- nallogoer, filmene bag filmen, pressemateriale på video, salgsma- teriale og tilgrænsende digitale medier – viser hvorledes industrien teoretiserer sine egne vilkår direkte på skærmen, og dermed også hvordan publikum vejledes igennem en sådan offentlig cirkulation af »inside«-viden om tv-systemet. Artiklen er oversat af Henrik Bødker.

Fjernsynet som shaman: Om store mediebegivenheders transformative virkninger

Directory of Open Access Journals (Sweden)

Daniel Dayan

1988-08-01

Full Text Available Den direkte transmission af levende begivenheder har altid været TV- mediets særlige styrke, hvad enten det drejer sig om fodboldkampe, Lørdagskanalen eller kongebrylluper. Medieforskerne har da også interesseret sig for fjernsynets formidling af sportsbegivenheder og der er blevet lavet analyser af Kanaludsendelserne. Men hverken i Danmark eller andre steder har forskerne fundet det interessant at undersøge de store, ceremonielle mediebegivenheder. Det er Dayan & Katz´ projekt at udfylde dette tomrum i medie- og kul- turforskningen: Hvad er det, der fascinerer millioner ved disse TV- ceremonier, der ofte ikke blot henvender sig til et enkelt lands seere, men transmitteres til og ses af store dele af verden? Hvordan fungere disse begivenheder - hvad enten det drejer sig om Præsident Kennedys begravelse, Sadats besøg i Jerusalem, Månelandingen eller Watergate- høringerne - ideologisk, og hvilke interesser tjener de dermed i den politiske offentlighed? I denne artikel, der er et bearbejdet uddrag af den bog, projektet skal munde ud i, beskæftiger Dayan & Katz sig især med det de kalder "transformative" mediebegivenheder, dvs. TV-ceremonier der markerer, at et samfund står ved en tærskel mellem den herskende orden og en ny mulig orden, og som selv er et aktivt og aktiverende bidrag til forvand- lingen af kulturelle opfattelser og værdier. Artiklens perpektiv er udpræget tværvidenskabeligt, idet der trækkes på såvel kulturantropologiske (Levi-Strauss, Geertz, Turner som filosofiske (Bakhtin, Baudrillard, sprogteoretiske (Austin og traditionelt samfunds- videnskabelige/mediesociologiske (Boorstin arbejder. I artiklens første halvdel foretages der en afgrænsning af transformative over for andre ceremonielle begivenheder. I anden halvdel gennemgås de fem faser, som udgør transformative begivenheders typiske struktur. Artiklen er bearbejdet af Jørgen Bang og Kim Schrøder. Den er oversat af Kim Schrøder.

Distinct roles of SOM and VIP interneurons during cortical Up states

Directory of Open Access Journals (Sweden)

Garrett T. Neske

2016-07-01

Full Text Available During cortical network activity, recurrent synaptic excitation among pyramidal neurons is approximately balanced by synaptic inhibition, which is provided by a vast diversity of inhibitory interneurons. The relative contributions of different interneuron subtypes to inhibitory tone during cortical network activity is not well understood. We previously showed that many of the major interneuron subtypes in mouse barrel cortex are highly active during Up states (Neske et al., 2015; while fast-spiking (FS, parvalbumin (PV-positive cells were the most active interneuron subtype, many non-fast-spiking (NFS, PV-negative interneurons were as active or more active than neighboring pyramidal cells. This suggests that the NFS cells could play a role in maintaining or modulating Up states. Here, using optogenetic techniques, we further dissected the functional roles during Up states of two major NFS, PV-negative interneuron subtypes: somatostatin (SOM-positive cells and vasoactive intestinal peptide (VIP-positive cells. We found that while pyramidal cell excitability during Up states significantly increased when SOM cells were optogenetically silenced, VIP cells did not influence pyramidal cell excitability either upon optogenetic silencing or activation. VIP cells failed to contribute to Up states despite their ability to inhibit SOM cells strongly. We suggest that the contribution of VIP cells to the excitability of pyramidal cells may vary with cortical state.

Advanced Atmospheric Ensemble Modeling Techniques

Energy Technology Data Exchange (ETDEWEB)

Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2017-09-29

Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.

Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Rotationally invariant family of Levy-like random matrix ensembles

International Nuclear Information System (INIS)

Choi, Jinmyung; Muttalib, K A

2009-01-01

We introduce a family of rotationally invariant random matrix ensembles characterized by a parameter λ. While λ = 1 corresponds to well-known critical ensembles, we show that λ ≠ 1 describes 'Levy-like' ensembles, characterized by power-law eigenvalue densities. For λ > 1 the density is bounded, as in Gaussian ensembles, but λ < 1 describes ensembles characterized by densities with long tails. In particular, the model allows us to evaluate, in terms of a novel family of orthogonal polynomials, the eigenvalue correlations for Levy-like ensembles. These correlations differ qualitatively from those in either the Gaussian or the critical ensembles. (fast track communication)

Sporløs – Om biologi, identitet og slægten som fjernsyn [Find My Family - On biology, identity and kinship on television] Sporløs – Om biologi, identitet og slægten som fjernsyn

Directory of Open Access Journals (Sweden)

Birgitta Frello

2011-12-01

Full Text Available During recent years, the Danish public service television station (DR has launched several documentary serials that focus on kinship and genealogy. Genealogy makes ‘good TV’ because it enables an immediate identification with the protagonist of the programme. Furthermore, the protagonist’s personal story can function as a vehicle for telling other stories. However, the way kinship is depicted in the serials presupposes that a person’s knowledge about her biological kin equals knowledge about her personal identity. The article analyses the serial Find my Family (Danish: Sporløs, discussing the naturalisation of biological kin and the possible consequences of the conceptualisation of kinship that is taken for granted in the serial.DR har i de senere år lanceret flere programserier, som har slægt og slægtsforskning som fokus. Slægtsprogrammer er ’godt fjernsyn’ i den forstand, at de giver mulighed for en umiddelbar identifikation med hovedpersonen, samtidig med at dennes historie kan bruges som løftestang for andre historier. Imidlertid anlægger programmerne en vinkel på slægten, som forudsætter, at et øget kendskab til den biologiske slægt automatisk medfører et øget kendskab til den personlige identitet. Denne selvfølgeliggørelse af den biologiske slægts betydning problematiseres i artiklen, og med udgangspunkt i Sporløs diskuteres mulige implikationer og konsekvenser af den forståelse af slægten, som programmerne tager udgangspunkt i og tager for givet.

Livsstil som tv-underholdning

Directory of Open Access Journals (Sweden)

Christa Lykke Christensen

2008-09-01

Full Text Available Livsstilsprogrammer har siden slutningen af 1990’erne domineret programfladen i den tidlige prime time på de danske public service-kanaler DR og TV 2. Dermed er emner, som figurerer i bl.a. magasiner og ugeblade, massivt rykket ind på tv’s sendeflade. Bolig, have, mad, ferie, krop og sundhed er indholdet i programmer, der både vil informere, give gode råd og underholde seerne. Artiklen fokuserer på underholdningsdimensionen i tre kategorier af livsstilsprogrammer og undersøger elementer, der kan tænkes at fremme motivationen for at se dem. Her lægges vægten på hhv. vidensperspektivets kompetenceaspekt og livsstilsbegrebets imaginative muligheder i relation til drømmen om ’det gode liv’ og ønsket om den forjættende forandring. Artiklen diskuterer endvidere, hvorledes public service-fjernsynet med disse livsstilsprogrammer har suppleret dets oplysende funktion med en opdaterende funktion, og hvordan dets tidligere kulturelt dannende funktion i kraft af disse programmers karakter af manual kan opfattes som en slags livsstilsguide i forhold til markedets forbrugsmuligheder. Lifestyle as TV entertainment Since the late 1990s lifestyle programmes have dominated early prime time on the Danish public service channels, DR and TV 2. For this reason topics that have traditionally figured in magazines extensively have entered television programming. House, garden, vacation, body, and health form the content of programmes which aim to both inform, advise and entertain viewers. The article primarily focuses on the aspects of entertainment in three main lifestyle categories and is concerned with elements that might promote viewers’ motivation for watching such programmes. Importance is attached to the competence aspect of knowledge and to the imaginative possibilities connected to the idea of lifestyle, related to daydreams, ideas of ‘the good life’, and a desire for promising change. Furthermore, the article discusses how public

Livsstil som tv-underholdning

Directory of Open Access Journals (Sweden)

Christa Lykke Christensen

2008-12-01

Full Text Available Livsstilsprogrammer har siden slutningen af 1990’erne domineret programfladen i den tidlige prime time på de danske public service-kanaler DR og TV 2. Dermed er emner, som figurerer i bl.a. magasiner og ugeblade, massivt rykket ind på tv’s sendeflade. Bolig, have, mad, ferie, krop og sundhed er indholdet i programmer, der både vil informere, give gode råd og underholde seerne. Artiklen fokuserer på underholdningsdimensionen i tre kategorier af livsstilsprogrammer og undersøger elementer, der kan tænkes at fremme motivationen for at se dem. Her lægges vægten på hhv. vidensperspektivets kompetenceaspekt og livsstilsbegrebets imaginative muligheder i relation til drømmen om ’det gode liv’ og ønsket om den forjættende forandring. Artiklen diskuterer endvidere, hvorledes public service-fjernsynet med disse livsstilsprogrammer har suppleret dets oplysende funktion med en opdaterende funktion, og hvordan dets tidligere kulturelt dannende funktion i kraft af disse programmers karakter af manual kan opfattes som en slags livsstilsguide i forhold til markedets forbrugsmuligheder. Lifestyle as TV entertainment Since the late 1990s lifestyle programmes have dominated early prime time on the Danish public service channels, DR and TV 2. For this reason topics that have traditionally figured in magazines extensively have entered television programming. House, garden, vacation, body, and health form the content of programmes which aim to both inform, advise and entertain viewers. The article primarily focuses on the aspects of entertainment in three main lifestyle categories and is concerned with elements that might promote viewers’ motivation for watching such programmes. Importance is attached to the competence aspect of knowledge and to the imaginative possibilities connected to the idea of lifestyle, related to daydreams, ideas of ‘the good life’, and a desire for promising change. Furthermore, the article discusses how public

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

Science.gov (United States)

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.

Proactive Review som læringsrum for online-ledere

DEFF Research Database (Denmark)

Kolbæk, Ditte

2017-01-01

. Kapitlet undersøger, hvordan online lederen kan lære af erfaringer skabt i hans/hendes online team. Kapitlet tager udgangspunkt i Engeströms virksomhedsteori og Proactive Review som praksisser for, hvordan ledere konkret kan lære sammen med deres team (Kolbæk 2011). Kapitlet udvikler en model for lederens...... læring på basis af ovenstående teorier. Da læring og kommunikation i teamet foregår online, er det nærliggende at benytte netnografi som metodisk tilgang. Kapitlet undersøger online lederens læringsrum i en global IT virksomhed, hvor Proactive Reviews er implementeret i flere end 40 lande......, at medarbejdere og ledere opholder sig i forskellige lande, og at de kun sjældent eller måske aldrig mødes fysisk, men altid samarbejder og kommunikerer online. Kapitlet kalder det online teams og online ledelse. Dette kapitel fokuserer på lederens læring i et læringsrum, hvor ledelse og samarbejde foregår online...

Squeezing of Collective Excitations in Spin Ensembles

DEFF Research Database (Denmark)

Kraglund Andersen, Christian; Mølmer, Klaus

2012-01-01

We analyse the possibility to create two-mode spin squeezed states of two separate spin ensembles by inverting the spins in one ensemble and allowing spin exchange between the ensembles via a near resonant cavity field. We investigate the dynamics of the system using a combination of numerical an...

AUC-Maximizing Ensembles through Metalearning.

Science.gov (United States)

LeDell, Erin; van der Laan, Mark J; Petersen, Maya

2016-05-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

Urban runoff forecasting with ensemble weather predictions

DEFF Research Database (Denmark)

Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

Directory of Open Access Journals (Sweden)

Matthew Sybeldon

2017-01-01

Full Text Available Brain–Computer Interfaces (BCI using Steady-State Visual Evoked Potentials (SSVEP are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA is seen as state-of-the-art for SSVEP BCI systems. However, this assumes that the user has full control over their covert attention, which may not be the case. This introduces high calibration requirements when using other machine learning techniques. These may be circumvented by using transfer learning to utilize data from other participants. This paper proposes a combination of ensemble learning via Learn++ for Nonstationary Environments (Learn++.NSEand similarity measures such as mutual information to identify ensembles of pre-existing data that result in higher classification. Results show that this approach performed worse than CCA in participants with typical SSVEP responses, but outperformed CCA in participants whose SSVEP responses violated CCA assumptions. This indicates that similarity measures and Learn++.NSE can introduce a transfer learning mechanism to bring SSVEP system accessibility to users unable to control their covert attention.

Bærme (DDGS) som proteinfoder til malkekøer

DEFF Research Database (Denmark)

Sehested, Jakob; Sørensen, Martin Tang; Weisbjerg, Martin Riis

2015-01-01

DDGS er et proteinrigt fodermiddel fra produktionen af bio-etanol baseret på korn og majs, og det forventes på markedet i stigende mængder. Kornbaseret DDGS kan være et godt proteinfoder som uden negativ effekt på mælkeproduktion eller -kvalitet kan indgå med 15 % af rationens tørstof og kan...

How the type of pyrogenic organic matter determines the SOM quality in amended soils

Science.gov (United States)

Merino, Agustin; Gartzia-Bengoetxea, Nahia; Morangues, Lur; Arias-Gonzalez, Ander

2016-04-01

Charred biomass can be used as an organic amendment and to enhance the C sink capacity of soils. There are two types of by-products containing pyrogenic OM that could be used to improve in agricultural or forestry, biochar and wood ash. Due to their different heating conditions under which it is produced (pyrolysis, combustion and different temperatures, feedstocks,..), the properties of this pyrogenic OM might be highly variable, which could affect the SOM quality and the C sink capacity of the amended soil. The purpose of this study was to assess how SOM quality is influenced by pyrogenic organic matter with different degree of carbonization. Biochar and bottom wood ash were added to two Atlantic forest soils (Pinus radiata, 12 °C, 1200 mm) with different texture, clayey loam and sandy loam. The experiment consisted in a randomized block trials, in which different doses of biochar (0, 3, 9, 18 Mh ha-1) and wood ash (0, 1.5, 4.5, and 9 Mg ha-1) were added. The Biochar applied (pH: 9.8; C: 87 %) was produced by the pyrolysis of Myscanthus sp. at 450°C in a Pyreg® pyrolysis unit. The bottom wood ash (pH: 10.6; C: 30 %) was produced by combustion in a biomass power plant. The aromatization/carbonization was lightly higher in biochar than in wood ash. This latter by-product, in addition to the black carbon, it also contained mineral ash, as well as unburnt or lightly charred plant biomass. The evolution of soil chemical and SOM properties were monitored over three years by solid state Differential Scanning Calorimetry (DSC) and 13C CPMAS NMR. These techniques were applied in bulk samples and also in fractions of different densityes. The changes in microbial activity were studied by analysis of microbial biomass C and basal respiration and soil microbial community. Three years after applications the SOM content increased lightly in the treatment receiving the highest doses of biochar and wood ash, specially in the clay loam soil. SOM in the treated soils displayed a

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Haakon

2016-01-08

The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Haakon; Chernov, Alexey; Law, Kody; Nobile, Fabio; Tempone, Raul

2016-01-01

The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

Bayesian ensemble refinement by replica simulations and reweighting

Science.gov (United States)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Protein folding simulations by generalized-ensemble algorithms.

Science.gov (United States)

Yoda, Takao; Sugita, Yuji; Okamoto, Yuko

2014-01-01

In the protein folding problem, conventional simulations in physical statistical mechanical ensembles, such as the canonical ensemble with fixed temperature, face a great difficulty. This is because there exist a huge number of local-minimum-energy states in the system and the conventional simulations tend to get trapped in these states, giving wrong results. Generalized-ensemble algorithms are based on artificial unphysical ensembles and overcome the above difficulty by performing random walks in potential energy, volume, and other physical quantities or their corresponding conjugate parameters such as temperature, pressure, etc. The advantage of generalized-ensemble simulations lies in the fact that they not only avoid getting trapped in states of energy local minima but also allows the calculations of physical quantities as functions of temperature or other parameters from a single simulation run. In this article we review the generalized-ensemble algorithms. Four examples, multicanonical algorithm, replica-exchange method, replica-exchange multicanonical algorithm, and multicanonical replica-exchange method, are described in detail. Examples of their applications to the protein folding problem are presented.

Ensembles of gustatory cortical neurons anticipate and discriminate between tastants in a single lick

Directory of Open Access Journals (Sweden)

Jennifer R Stapleton

2007-10-01

Full Text Available The gustatory cortex (GC processes chemosensory and somatosensory information and is involved in learning and anticipation. Previously we found that a subpopulation of GC neurons responded to tastants in a single lick (Stapleton et al., 2006. Here we extend this investigation to determine if small ensembles of GC neurons, obtained while rats received blocks of tastants on a fixed ratio schedule (FR5, can discriminate between tastants and their concentrations after a single 50 µL delivery. In the FR5 schedule subjects received tastants every fifth (reinforced lick and the intervening licks were unreinforced. The ensemble firing patterns were analyzed with a Bayesian generalized linear model whose parameters included the firing rates and temporal patterns of the spike trains. We found that when both the temporal and rate parameters were included, 12 of 13 ensembles correctly identified single tastant deliveries. We also found that the activity during the unreinforced licks contained signals regarding the identity of the upcoming tastant, which suggests that GC neurons contain anticipatory information about the next tastant delivery. To support this finding we performed experiments in which tastant delivery was randomized within each block and found that the neural activity following the unreinforced licks did not predict the upcoming tastant. Collectively, these results suggest that after a single lick ensembles of GC neurons can discriminate between tastants, that they may utilize both temporal and rate information, and when the tastant delivery is repetitive ensembles contain information about the identity of the upcoming tastant delivery.

Impact of Fe(III)-OM complexes and Fe(III) polymerization on SOM pools reactivity under different land uses

Science.gov (United States)

Giannetta, B.; Plaza, C.; Zaccone, C.; Siebecker, M. G.; Rovira, P.; Vischetti, C.; Sparks, D. L.

2017-12-01

Soil organic matter (SOM) protection and long-term accumulation are controlled by adsorption to mineral surfaces in different ways, depending on its molecular structure and pedo-climatic conditions. Iron (Fe) oxides are known to be key regulators of the soil carbon (C) cycle, and Fe speciation in soils is highly dependent on environmental conditions and chemical interactions with SOM. However, the molecular structure and hydrolysis of Fe species formed in association with SOM is still poorly described. We hypothesize the existence of two pools of Fe which interact with SOM: mononuclear Fe(III)-SOM complexes and precipitated Fe(III) hydroxides. To verify our hypothesis, we investigated the interactions between Fe(III) and physically isolated soil fractions by means of batch experiments at pH 7. Specifically, we examined the fine silt plus clay (FSi+C) fraction, obtained by ultrasonic dispersion and wet sieving. The soil samples spanned several land uses, including coniferous forest (CFS), grassland (GS), technosols (TS) and agricultural (AS) soils. Solid phase products and supernatants were analyzed for C and Fe content. X-ray diffraction (XRD) and Brunauer-Emmett-Teller (BET) analysis were also performed. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) was used to assess the main C functional groups involved in C complexation and desorption experiments. Preliminary linear combination fitting (LCF) of Fe K-edge extended X-ray absorption fine structure (EXAFS) spectra suggested the formation of ferrihydrite-like polymeric Fe(III) oxides in reacted CFS and GS samples, with higher C and Fe concentration. Conversely, mononuclear Fe(III) OM complexes dominated the speciation for TS and AS samples, characterized by lower C and Fe concentration, inhibiting the hydrolysis and polymerization of Fe (III). This approach will help revealing the mechanisms by which SOM pools can control Fe(III) speciation, and will elucidate how both Fe

Feature combination analysis in smart grid based using SOM for Sudan national grid

Science.gov (United States)

Bohari, Z. H.; Yusof, M. A. M.; Jali, M. H.; Sulaima, M. F.; Nasir, M. N. M.

2015-12-01

In the investigation of power grid security, the cascading failure in multicontingency situations has been a test because of its topological unpredictability and computational expense. Both system investigations and burden positioning routines have their limits. In this project, in view of sorting toward Self Organizing Maps (SOM), incorporated methodology consolidating spatial feature (distance)-based grouping with electrical attributes (load) to evaluate the vulnerability and cascading impact of various part sets in the force lattice. Utilizing the grouping result from SOM, sets of overwhelming stacked beginning victimized people to perform assault conspires and asses the consequent falling impact of their failures, and this SOM-based approach viably distinguishes the more powerless sets of substations than those from the conventional burden positioning and other bunching strategies. The robustness of power grids is a central topic in the design of the so called "smart grid". In this paper, to analyze the measures of importance of the nodes in a power grid under cascading failure. With these efforts, we can distinguish the most vulnerable nodes and protect them, improving the safety of the power grid. Also we can measure if a structure is proper for power grids.

Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

Science.gov (United States)

Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

2017-11-01

Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

Ensemble method for dengue prediction.

Science.gov (United States)

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Ensemble method for dengue prediction.

Directory of Open Access Journals (Sweden)

Anna L Buczak

Full Text Available In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico during four dengue seasons: 1 peak height (i.e., maximum weekly number of cases during a transmission season; 2 peak week (i.e., week in which the maximum weekly number of cases occurred; and 3 total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.Our approach used ensemble models created by combining three disparate types of component models: 1 two-dimensional Method of Analogues models incorporating both dengue and climate data; 2 additive seasonal Holt-Winters models with and without wavelet smoothing; and 3 simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Improved Discriminability of Spatiotemporal Neural Patterns in Rat Motor Cortical Areas as Directional Choice Learning Progresses

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Hongwei eMao

2015-03-01

Full Text Available Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2-3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats’ behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

Specialestuderendes læringsudfordringer i vejledningen - nudging som handlemulighed

DEFF Research Database (Denmark)

Jensen, Hanne Nexø; Juul Jensen, Christina

2015-01-01

teoretiske afsæt er generiske læringsudfordringer i vejledningsprocesser, hvor læring opstår, når de studerende passerer en læringsudfordring. Vejledere kan bruge forståelsen af læringsudfordringer til at ”nudge”- plante en idé – som en måde at få studerende til at passere læringsudfordringerne....

Contact planarization of ensemble nanowires

Science.gov (United States)

Chia, A. C. E.; LaPierre, R. R.

2011-06-01

The viability of four organic polymers (S1808, SC200, SU8 and Cyclotene) as filling materials to achieve planarization of ensemble nanowire arrays is reported. Analysis of the porosity, surface roughness and thermal stability of each filling material was performed. Sonication was used as an effective method to remove the tops of the nanowires (NWs) to achieve complete planarization. Ensemble nanowire devices were fully fabricated and I-V measurements confirmed that Cyclotene effectively planarizes the NWs while still serving the role as an insulating layer between the top and bottom contacts. These processes and analysis can be easily implemented into future characterization and fabrication of ensemble NWs for optoelectronic device applications.

Ensemble forecasting of species distributions.

Science.gov (United States)

Araújo, Miguel B; New, Mark

2007-01-01

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

Pædagogiske læreplaner som redskab til evaluering, læring og kvalitetsudvikling?

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Poul Skov Dahl

2016-02-01

Full Text Available Den nyligt afsluttede, landsdækkende evaluering af loven om pædagogiske læreplaner har vist, at pædagogiske læreplaner indtil nu har været en faglig succes i dagtilbuddene. Men den viser også, at evalueringsarbejdet endnu spiller en begrænset rolle i arbejdet. Hvis de pædagogiske læreplaner fremadrettet skal forblive en succes og medvirke til at udvikle den pædagogiske praksis, er evalueringskapacitet et vigtigt omdrejningspunkt. Med systematisk evaluering som en integreret del af arbejdet med de pædagogiske læreplaner har de - som en form for ”systemtænknings-redskab -” potentiale til at være løftestang for organisatorisk læring i dagtilbuddet - den læring, som er en forudsætning for kvalitetsudviklingen af den pædagogiske praksis. Vi sætter i denne artikel fokus på sammenhængen mellem pædagogiske læreplaner, evaluering og organisatorisk læring i dagtilbuddene.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

Science.gov (United States)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Empowerment som frigørelse?

DEFF Research Database (Denmark)

Andersen, Pernille Tanggaard

2010-01-01

Empowerment er et at tidens modeord, og sættes i forbindelse med vidt forskellige termer eksempelvis personlighedsudvikling, borgerinddragelse og styringsideologi. Begrebet empowerment rummer efterhånden så mange betydninger, at man kan diskuterer selve essensen – i dette kapitel gøres dog et...... forsøg og forskellige dele af begrebet udfoldes. Direkte oversat betyder empowermemt at bemyndige eller sætter i stand til. Empowerment er et ofte anvendt begreb indenfor sundhedsvidenskab og i tilrettelæggelse af sundhedsfremmende initiativer. Begrebet bliver ofte defineret og benyttet forskelligt alt...... efter det bagvedliggende ideologiske perspektiv, og derfor er udgangspunktet i dette kapitel også at illustrerer forskellige ”blikke på empowerment”. Derudover inddrages to eksempler; studier af lokalsamfund og borgerinddragelse og ’den motiverende samtale’ som illustration på, hvilke udfordringer, der...

Med øjeblikket som udgangspunkt

DEFF Research Database (Denmark)

Bo, Inger Glavind

Artiklen er motiveret af, at Meads temporale analyser tilsyneladende ikke har fået den plads og opmærksomhed, de fortjener, og formålet er at argumentere for, at disse er afgørende for forståelsen af helt centrale aspekter i hans tænkning. Meads udgangspunkt for sin analyse af temporalitet er en...... perspektiv på identitet og social interaktion i hans socialpsykologi. Denne artikels formål er således dobbelt og er for det første at præsentere og diskutere Meads temporale perspektiv som en relevant indgang og nøgle til hans socialpsykologi og sideløbende hermed for det andet at argumentere for, at de...... temporale analyser er centrale for at få greb om Meads genialitet....

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors

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Runtao Yang

2015-09-01

Full Text Available Antifreeze proteins (AFPs play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.

Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

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Zhibin Xiao

2017-02-01

Full Text Available Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.

Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

Science.gov (United States)

Erfanian, A.; Fomenko, L.; Wang, G.

2016-12-01

Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

Det retlige sprog som virkelighedsskabende - graviditetsvært kontra surrogatmoder

DEFF Research Database (Denmark)

Pedersen, Frank Høgholm

2018-01-01

I dag er det undtagelsen, at kvinden, der bærer graviditeten og føder barnet i et surrogati-arrangement, er genetisk relateret til barnet. I artiklen foreslås som en supplerende betegnelse til surrogatmoder derfor betegnelsen: graviditetsvært. Valget af en retlig betegnelse til beskrivelse af fak...

An ensemble classification approach for improved Land use/cover change detection

Science.gov (United States)

Chellasamy, M.; Ferré, T. P. A.; Humlekrog Greve, M.; Larsen, R.; Chinnasamy, U.

2014-11-01

Change Detection (CD) methods based on post-classification comparison approaches are claimed to provide potentially reliable results. They are considered to be most obvious quantitative method in the analysis of Land Use Land Cover (LULC) changes which provides from - to change information. But, the performance of post-classification comparison approaches highly depends on the accuracy of classification of individual images used for comparison. Hence, we present a classification approach that produce accurate classified results which aids to obtain improved change detection results. Machine learning is a part of broader framework in change detection, where neural networks have drawn much attention. Neural network algorithms adaptively estimate continuous functions from input data without mathematical representation of output dependence on input. A common practice for classification is to use Multi-Layer-Perceptron (MLP) neural network with backpropogation learning algorithm for prediction. To increase the ability of learning and prediction, multiple inputs (spectral, texture, topography, and multi-temporal information) are generally stacked to incorporate diversity of information. On the other hand literatures claims backpropagation algorithm to exhibit weak and unstable learning in use of multiple inputs, while dealing with complex datasets characterized by mixed uncertainty levels. To address the problem of learning complex information, we propose an ensemble classification technique that incorporates multiple inputs for classification unlike traditional stacking of multiple input data. In this paper, we present an Endorsement Theory based ensemble classification that integrates multiple information, in terms of prediction probabilities, to produce final classification results. Three different input datasets are used in this study: spectral, texture and indices, from SPOT-4 multispectral imagery captured on 1998 and 2003. Each SPOT image is classified

Yrkesforberedelse eller fagopplæring med fagbrev? Med design og håndverk som kontekst

Directory of Open Access Journals (Sweden)

Bjørn Magne Aakre

2013-09-01

Full Text Available Artikkelen drøfter forholdet mellom yrkesforberedelse og fagopplæring med utgangspunkt i den kombinerte studieretningen formgivingsfag som ble innført i Norge i 1994. I 2006 ble den delt i et programfag under studiespesialiserende fag, og et nytt yrkesfaglig program med betegnelse design og håndverk. Hvilke interesser lå til grunn for endringene, hvor dyptgripende ble de og hvilke overveieleser kan en gjøre i ettertid om forholdet mellom yrkesforberedelse og fagopplæring? Artikkelen søker å svare på spørsmålene ut fra relevante dokumenter og to kvantitative undersøkelser med elever og lærere som informanter. Artikkelen konkluderer med at innholdet forble nokså likt, antall elever ble halvert og at frafallet økte. Det konkluderes videre med at mange forhold bidrar til å legitimere et fag og dets innhold som henholdsvis skolefag, vitenskapsfag eller yrkesfag. Legitimeringen preges ofte av motstridende motiver og interesser, og sjelden bare faglige begrunnelser.

Conductor gestures influence evaluations of ensemble performance.

Science.gov (United States)

Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D

2014-01-01

Previous research has found that listener evaluations of ensemble performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of ensemble performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber ensemble in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the ensemble's articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble's performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall ensemble expressivity.

Litteraturarbete i ett svenska som andraspråksklassrum

Directory of Open Access Journals (Sweden)

Catarina Economou

2015-06-01

Full Text Available AbstractThis article considers the role of reading fiction in Swedish as a second language instruction. The study examines how a group of advanced second language learners in a Swedish upper secondary school read, interpret and discuss a contemporary Swedish novel, how they interact with the text and with each other in relation to the text. Furthermore, it analyses which forms of reading the students use. It is a qualitative, empirical study based on field studies, transcriptions of tape recorded interaction and of written texts. The results indicate that second language learners in this context have a positive attitude towards reading and discussing what they read using several forms of reading. They often compare the content of the text to their own lives. One conclusion is that literature teaching and literature can be integrated into one Swedish subject in order to create even more meaningful interactions between students from different backgrounds. Another is that literature can be a means of language development as well as personal development.Keywords: Second language learners, literature pedagogy and didactics, forms of reading, democracy. SammandragDenna artikel handlar om skönlitteraturens roll i skolämnet svenska som andraspråk. Studien undersöker hur en grupp avancerade andraspråksinlärare i en svensk gymnasieskola läser, tolkar och diskuterar en modern svensk roman och hur de interagerar med texten och med varandra i relation till texten. Dessutom analyseras vilka läsarter som eleverna använde sig av. Den är en kvalitativ, empirisk studie som baseras på fältstudier, transkriptioner av inspelade boksamtal och elevtexter. Resultaten visar att andraspråkseleverna hade en positiv attityd till boksamtal och de använde sig av flera läsarter. De jämförde ofta texten till sina egna liv och erfarenheter.  En slutsats är att litteraturundervisning och litteraturarbete skulle kunna integreras i ett gemensamt ämne så att �

Disinhibition in learning and memory circuits: New vistas for somatostatin interneurons and long-term synaptic plasticity.

Science.gov (United States)

Artinian, Julien; Lacaille, Jean-Claude

2017-11-23

Neural circuit functions involve finely controlled excitation/inhibition interactions that allow complex neuronal computations and support high order brain functions such as learning and memory. Disinhibition, defined as a transient brake on inhibition that favors excitation, recently appeared to be a conserved circuit mechanism implicated in various functions such as sensory processing, learning and memory. Although vasoactive intestinal polypeptide (VIP) interneurons are considered to be the main disinhibitory cells, recent studies highlighted a pivotal role of somatostatin (SOM) interneurons in inhibiting GABAergic interneurons and promoting principal cell activation. Interestingly, long-term potentiation of excitatory input synapses onto hippocampal SOM interneurons is proposed as a lasting mechanism for regulation of disinhibition of principal neurons. Such regulation of network metaplasticity may be important for hippocampal-dependent learning and memory. Copyright © 2017 Elsevier Inc. All rights reserved.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

Energy Technology Data Exchange (ETDEWEB)

Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

Faktorer som förklarar miljövänligt beteende hos unga vuxna

OpenAIRE

Rikner, Amanda

2009-01-01

Människors miljörelaterade beteenden behöver förbättras. I studien undersöktes unga vuxna universitetsstuderandes syn på klimatproblematiken. En enkät delades ut som mätte miljövänligt beteende, ansvarskänsla, tillit till forskning, upplevelse av att kunna påverka och kunskap när det gäller klimatproblematiken. Deltagarna fick också beskriva anledningar som möjliggör eller förhindrar en förbättring av klimatproblematiken. Deltog gjorde 97 personer. Resultatet visade på signifikanta samband me...

BIM som Informationsbärare in i Förvaltningen : En studie vid Forsmarks Kraftgrupp

OpenAIRE

Svens, Therése

2013-01-01

BIM, Building Information Modeling, börjar vinna mark inom byggbranschen i Sverige och är en vedertagen process i vart och vartannat byggprojekt. Forsmarks Kraftgrupp står inför både upprustningar av sina anläggningar och nyproduktion av bland annat kontor, verkstad och hotell. BIM framstår nu som en lukrativ metod för att dra ner på projektkostnaderna, men även för att få ytterligare ordning och struktur på den enorma mängd dokumentation som ackumulerat under de dryga trettio åren av drift. ...

Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

Science.gov (United States)

Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

2017-12-01

This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

Modality-Driven Classification and Visualization of Ensemble Variance

Energy Technology Data Exchange (ETDEWEB)

Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.

2016-10-01

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.

Asymmetric similarity-weighted ensembles for image segmentation

DEFF Research Database (Denmark)

Cheplygina, V.; Van Opbroek, A.; Ikram, M. A.

2016-01-01

Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between...... the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted...... and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task....

Forskeres arbejde med oplevelser af børns tegninger som forskningsmetode

DEFF Research Database (Denmark)

Nielsen, Anne Maj

2012-01-01

, experiences, intentions and engagement that may be difficult to articulate verbally. Research can include children’s art and drawings to study phenomena such as children’s feelings, emotions, experiences, intentions and engagements. Transparency of the ways in which art and drawings in specific ways...... at gøre rede for hvordan forskerens sanselige og æstetiske oplevelser af børns tegninger kan gøres transparente ved at tegninger betragtes som ’æstetiske objekter’ som led i forskningsprocessen. In art and drawing children can visually express phenomena in their mind such as feelings, emotions...... contribute to knowledge is a challenge in research. The aim of this article is to describe and discuss how the construct ‘aesthetic object’ may offer researchers an approach to children’s art and drawings that can explicitly include the researcher’s sensory and aesthetic experiences as knowledge....

Betyder modellering det samme i naturfag som i matematik?

DEFF Research Database (Denmark)

Ejersbo, Lisser Rye

2014-01-01

Med de nye målformuleringer og læseplaner bruges udtrykket modellering nu både indenfor naturfag og matematik. Matematiklærere, som er vant til at arbejde med modellering tror måske, at det betyder det samme begge steder, men det behøver slet ikke at være tilfældet. Måske vi trænger til en...

Creating Weather System Ensembles Through Synergistic Process Modeling and Machine Learning

Science.gov (United States)

Chen, B.; Posselt, D. J.; Nguyen, H.; Wu, L.; Su, H.; Braverman, A. J.

2017-12-01

Earth's weather and climate are sensitive to a variety of control factors (e.g., initial state, forcing functions, etc). Characterizing the response of the atmosphere to a change in initial conditions or model forcing is critical for weather forecasting (ensemble prediction) and climate change assessment. Input - response relationships can be quantified by generating an ensemble of multiple (100s to 1000s) realistic realizations of weather and climate states. Atmospheric numerical models generate simulated data through discretized numerical approximation of the partial differential equations (PDEs) governing the underlying physics. However, the computational expense of running high resolution atmospheric state models makes generation of more than a few simulations infeasible. Here, we discuss an experiment wherein we approximate the numerical PDE solver within the Weather Research and Forecasting (WRF) Model using neural networks trained on a subset of model run outputs. Once trained, these neural nets can produce large number of realization of weather states from a small number of deterministic simulations with speeds that are orders of magnitude faster than the underlying PDE solver. Our neural network architecture is inspired by the governing partial differential equations. These equations are location-invariant, and consist of first and second derivations. As such, we use a 3x3 lon-lat grid of atmospheric profiles as the predictor in the neural net to provide the network the information necessary to compute the first and second moments. Results indicate that the neural network algorithm can approximate the PDE outputs with high degree of accuracy (less than 1% error), and that this error increases as a function of the prediction time lag.

Bioactive focus in conformational ensembles: a pluralistic approach

Science.gov (United States)

Habgood, Matthew

2017-12-01

Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment's Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre's conformation generator software.

Development of quantification methods for the myocardial blood flow using ensemble independent component analysis for dynamic H215O PET

International Nuclear Information System (INIS)

Lee, Byeong Il; Lee, Jae Sung; Lee, Dong Soo; Kang, Won Jun; Lee, Jong Jin; Kim, Soo Jin; Chung, June Key; Lee, Myung Chul; Choi, Seung Jin

2004-01-01

Factor analysis and independent component analysis (lCA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial H 2 15 O PET data. In this study, we quantified patients, blood flow using the ensemble ICA method. Twenty subjects underwent H 2 15 O PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of 555∼740 MBq H 2 15 O. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was 1.2±0.40 ml/min/g in rest, 1.85±1.12 ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between

Demonstrating the value of larger ensembles in forecasting physical systems

Directory of Open Access Journals (Sweden)

Reason L. Machete

2016-12-01

Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by

Ensemble Weight Enumerators for Protograph LDPC Codes

Science.gov (United States)

Divsalar, Dariush

2006-01-01

Recently LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length ensemble weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular we are interested in obtaining ensemble average weight enumerators for protograph LDPC codes which have minimum distance that grows linearly with block size. As with irregular ensembles, linear minimum distance property is sensitive to the proportion of degree-2 variable nodes. In this paper the derived results on ensemble weight enumerators show that linear minimum distance condition on degree distribution of unstructured irregular LDPC codes is a sufficient but not a necessary condition for protograph LDPC codes.

Using Self-Organizing Map (SOM) Clusters of Ozonesonde Profiles to Evaluate Climatologies and Create Linkages between Meteorology and Pollution

Science.gov (United States)

Stauffer, R. M.; Thompson, A. M.; Young, G. S.; Oltmans, S. J.; Johnson, B.

2016-12-01

Ozone (O3) climatologies are typically created by averaging ozonesonde profiles on a monthly or seasonal basis, either for specific regions or zonally. We demonstrate the advantages of using a statistical clustering technique, self-organizing maps (SOM), over this simple averaging, through analysis of more than 4500 sonde profiles taken from the long-term US sites at Boulder, CO; Huntsville, AL; Trinidad Head, CA; and Wallops Island, VA. First, we apply SOM to O3 mixing ratios from surface to 12 km amsl. At all four sites, profiles in SOM clusters exhibit similar tropopause height, 500 hPa height and temperature, and total and tropospheric column O3. Second, when profiles from each SOM cluster are compared to monthly O3 means, near-tropopause O3 in three of the clusters is double (over +100 ppbv) the climatological O3 mixing ratio. The three clusters include 13-16% of all profiles, mostly from winter and spring. Large mid-tropospheric deviations from monthly means are found in two highly-populated clusters that represent either distinctly polluted (summer) or clean O3 (fall-winter, high tropopause) profiles. Thus, SOM indeed appear to represent US O3 profile statistics better than conventional climatologies. In the case of Trinidad Head, SOM clusters of O3 profile data from the lower troposphere (surface-6 km amsl) can discriminate background vs polluted O3 and the meteorology associated with each. Two of nine O3 clusters exhibit thin layers ( 100s of m thick) of high O3, typically between 1 and 4 km. Comparisons between clusters and downwind, high-altitude surface O3 measurements display a marked impact of the elevated tropospheric O­­3. Days corresponding to the high O3 clusters exhibit hourly surface O3 anomalies at surface sites of +5 -10 ppbv compared to a climatology; the anomalies can last up to four days. We also explore applications of SOM to tropical ozonesonde profiles, where tropospheric O3 variability is generally smaller.

Ensemble atmospheric dispersion calculations for decision support systems

International Nuclear Information System (INIS)

Borysiewicz, M.; Potempski, S.; Galkowski, A.; Zelazny, R.

2003-01-01

This document describes two approaches to long-range atmospheric dispersion of pollutants based on the ensemble concept. In the first part of the report some experiences related to the exercises undertaken under the ENSEMBLE project of the European Union are presented. The second part is devoted to the implementation of mesoscale numerical prediction models RAMS and atmospheric dispersion model HYPACT on Beowulf cluster and theirs usage for ensemble forecasting and long range atmospheric ensemble dispersion calculations based on available meteorological data from NCEO, NOAA (USA). (author)

Multi-digit handwritten sindhi numerals recognition using som neural network

International Nuclear Information System (INIS)

Chandio, A.A.; Jalbani, A.H.; Awan, S.A.

2017-01-01

In this research paper a multi-digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. Only one sample is required to train the network for each pair of multi-digit numerals. A database consisting of 4000 samples of multi-digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved. (author)

Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

Science.gov (United States)

Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

2017-11-01

Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

Directory of Open Access Journals (Sweden)

Seungpil Jung

2018-02-01

Full Text Available This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF and ensemble smoother (ES as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.

Addressing uncertainty in atomistic machine learning

DEFF Research Database (Denmark)

Peterson, Andrew A.; Christensen, Rune; Khorshidi, Alireza

2017-01-01

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predi......Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility...... of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We...... suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate...

Various multistage ensembles for prediction of heating energy consumption

Directory of Open Access Journals (Sweden)

Radisa Jovanovic

2015-04-01

Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

Minihusfellesskap som verkemiddel i stadsutvikling av Undredal

OpenAIRE

Underdal, Brita

2017-01-01

Små bygdesamfunn i Noreg slit med nedgang i folketalet, og det går ut over utvikling, handel og trivsel hjå dei som vert verande i bygda. I verste fall blir bygdene heilt fråflytta, husa forfell og naturressursane går til spille. Dette kan vere framtidsbiletet til Undredal om ikkje noko vert gjort. På 20 år har folketalet gått ned frå 130 innbyggjarar til 70. Dersom utviklinga ikkje stagnerer, vil bygda snart vere tom for fastbuande. Oppgåva stiller difor spørsmålet: Korleis kan ein auk...

Esplenectomia subtotal para tratamento do hipodesenvolvimento somático e sexual secundário a esplenomegalia

Directory of Open Access Journals (Sweden)

Andy Petroianu

Full Text Available OBJETIVO: "Nanismo esplênico" é condição clínica decorrente de processo imunitário relacionado ao baço e tem como tratamento preconizado a esplenectomia total. Seguindo uma linha de pesquisa voltada ao estudo da esplenectomia subtotal em diferentes afecções, o objetivo do presente trabalho foi avaliar o crescimento somático de pacientes portadores deste hipodesenvolvimento esplenomegálico, submetidos à esplenectomia subtotal com preservação do pólo superior esplênico irrigado apenas pelos vasos esplenogástricos. MÉTODO: Quatro pacientes masculinos (14, 14, 16 e 17 anos com esplenomegalia (três de etiologia esquistossomática e um com cirrose auto-imune e retardo do desenvolvimento somático e sexual foram submetidos à esplenectomia subtotal. As indicações para cirurgia foram sangramento de varizes do esôfago e pancitopenia. RESULTADOS: Em todos os casos houve retomada do crescimento e após um ano eles já se encontravam dentro da faixa de desenvolvimento somático e sexual compatível com a idade. CONCLUSÃO: O hipodesenvolvimento esplenomegálico não é decorrente da presença do baço, mas de seu crescimento; a esplenectomia subtotal é um procedimento adequado para tratar o retardo de desenvolvimento somático e sexual por esplenomegalia.

Sundhed som tema på den pædagogiske dagsorden

DEFF Research Database (Denmark)

Pedersen, Ulla; Schulz, Anette

2015-01-01

Dagtilbud og skole fremhæves i de nationale mål for danskernes sundhed som vigtige indsatsmiljøer (Regeringen, 2014). I dette kapitel berøres indledningsvist dagtilbuddets og skolens sundhedsfremmende rolle. Herefter præsenteres en række cases, hvor to faktorer har været særligt afgørende for at ...

Det moderne projekt – og portfolioen som en af mange succesteknologier

DEFF Research Database (Denmark)

Helms, Niels Henrik

2010-01-01

I artiklen diskuteres portfolioen i lyset af den canadiske fi losof Taylors begreber om rationel selvkontrol og ekspressiv artikulering som forskellige udtryk for det moderne. En række pædagogiske succesteknologier problematiseres og afsluttende foreslås en kommunitarisk forståelse af det...

Predicting artificailly drained areas by means of selective model ensemble

DEFF Research Database (Denmark)

Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

. The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

Praksisnær skoleudvikling og PLC som strategisk udviklingsaktør

DEFF Research Database (Denmark)

Christensen, Ole; Bach, Anna

2017-01-01

I det følgende belyses, hvorledes PLC kan udvikles som strategisk udviklingsaktør og understøtte praksisnær skoleudvikling. Vi ønsker at belyse hvorledes Bekendtgørelsens overordnede intentioner kan få retning og rammer og dermed medvirke til, at hele det pædagogiske personale indgår i skoleudvik......I det følgende belyses, hvorledes PLC kan udvikles som strategisk udviklingsaktør og understøtte praksisnær skoleudvikling. Vi ønsker at belyse hvorledes Bekendtgørelsens overordnede intentioner kan få retning og rammer og dermed medvirke til, at hele det pædagogiske personale indgår i...... skoleudviklingen. I den forbindelse er det vores erfaring, at PLC kan spille en central rolle i forbindelse med den praksisnære og lokale skoleudvikling. Erfaringerne bygger især på udviklings- og forskningsprojekter fra kommunerne Kolding, København, Roskilde og Tårnby. Der har været tale om praksisnære...

Comparison of standard resampling methods for performance estimation of artificial neural network ensembles

OpenAIRE

Green, Michael; Ohlsson, Mattias

2007-01-01

Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three cutoffs, and bootstrap using five different data sets. The results show that CV together with holdout $0.25$ and $0.50$ are the best resampl...

Popular Music and the Instrumental Ensemble.

Science.gov (United States)

Boespflug, George

1999-01-01

Discusses popular music, the role of the musical performer as a creator, and the styles of jazz and popular music. Describes the pop ensemble at the college level, focusing on improvisation, rehearsals, recording, and performance. Argues that pop ensembles be used in junior and senior high school. (CMK)

Ensemble Kalman methods for inverse problems

International Nuclear Information System (INIS)

Iglesias, Marco A; Law, Kody J H; Stuart, Andrew M

2013-01-01

The ensemble Kalman filter (EnKF) was introduced by Evensen in 1994 (Evensen 1994 J. Geophys. Res. 99 10143–62) as a novel method for data assimilation: state estimation for noisily observed time-dependent problems. Since that time it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. In this paper we propose the application of an iterative ensemble Kalman method for the solution of a wide class of inverse problems. In this context we show that the estimate of the unknown function that we obtain with the ensemble Kalman method lies in a subspace A spanned by the initial ensemble. Hence the resulting error may be bounded above by the error found from the best approximation in this subspace. We provide numerical experiments which compare the error incurred by the ensemble Kalman method for inverse problems with the error of the best approximation in A, and with variants on traditional least-squares approaches, restricted to the subspace A. In so doing we demonstrate that the ensemble Kalman method for inverse problems provides a derivative-free optimization method with comparable accuracy to that achieved by traditional least-squares approaches. Furthermore, we also demonstrate that the accuracy is of the same order of magnitude as that achieved by the best approximation. Three examples are used to demonstrate these assertions: inversion of a compact linear operator; inversion of piezometric head to determine hydraulic conductivity in a Darcy model of groundwater flow; and inversion of Eulerian velocity measurements at positive times to determine the initial condition in an incompressible fluid. (paper)

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Sinéreses cromossômicas: projeto de complementaridade semiósica entre som e cor

Directory of Open Access Journals (Sweden)

Eufrásio Prates

2008-11-01

Full Text Available O trabalho de Basbaum transita, transdisciplinarmente, pelos campos da neurologia cognitiva, arte, fí­sica, matemática, tecnologia, misticismo e teosofia, trazidos à baila para compor a teia da sinestesia ontológica do cromo-som, complexo indissociável e simultâneo de som e cor, como busca de interconexões originárias entre som e cor a partir da esfera da primeiridade peirciana, da constituição mesma do fenômeno sinestésico. Palavras-chave Sinestesia, primeiridade. Abstract The work of Basbaum as a research on the originary interconnections between sound and color based upon the Peircian firstness sphere concern, the constitution of the synesthesic phenomenon itself , goes transdisciplinarly through the fields of cognitive neurology, arts, physics, mathematics, technology, mysticism and theosophy, all of them with regard to the weaving of the ontological; synesthesia of the chromosound, an indissociable and simultaneous complex ofsound and color. Keywords Synesthesic phenomenon, Peircian firstness.

Kvalitativ og kvantitativ kortlægning af potentialer og barrierer for udvikling af blended learning-uddannelser på Ingeniørhøjskolen i København

DEFF Research Database (Denmark)

Gynther, Karsten; Christensen, Ove; Frederiksen, Jan

Denne rapport kortlægger behovet for at udvikle fleksible diplomingeniøruddannelser og et fleksibelt adgangskursus til Ingeniørhøjskolens uddannelser i Ballerup. Rapporten udpeger nye brugerprofiler og heraf følgende designprincipper for, hvordan en blended learning uddannelse kan tilrettelægges i...... tilrettelæggelse af blended learning koncepter, der matcher nye brugergruppers behov og vilkår. Som sådan kan undersøgelsen læses som et casestudium med bred relevans for alle uddannelsestyper i den danske uddannelsessektor....

Genetic Algorithm Optimized Neural Networks Ensemble as ...

African Journals Online (AJOL)

Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.

Kombineret laser Doppler flowmetri og spectrophotometri som metode til vurdering af mikrocirculation

DEFF Research Database (Denmark)

Berggren Olsen, Mette; Sørensen, Hanne Birke; Houlind, Kim Christian

Kombineret laser Doppler flowmetri og spectrophotometri som metode til vurdering af mikrocirculation Berggren, MB, reservelæge, Karkirurgisk Afdeling, Kolding, mette.marie.berggren.olsen@slb.regionsyddanmark.dk; Houlind, K, lektor, afdelingslæge, Ph.d., Karkirurgisk afdeling, Kolding, kim...