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Sample records for semisupervised dimensionality reduction

  1. Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

    Science.gov (United States)

    Gönen, Mehmet

    2014-03-01

    Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

  2. An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

    Science.gov (United States)

    Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

    2014-01-01

    The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

  3. Central subspace dimensionality reduction using covariance operators.

    Science.gov (United States)

    Kim, Minyoung; Pavlovic, Vladimir

    2011-04-01

    We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.

  4. Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction.

    Science.gov (United States)

    Nie, Feiping; Xu, Dong; Tsang, Ivor Wai-Hung; Zhang, Changshui

    2010-07-01

    We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F(0) = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F(0). Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.

  5. Robust Semi-Supervised Manifold Learning Algorithm for Classification

    Directory of Open Access Journals (Sweden)

    Mingxia Chen

    2018-01-01

    Full Text Available In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.

  6. Semisupervised kernel marginal Fisher analysis for face recognition.

    Science.gov (United States)

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

    2013-01-01

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

  7. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

    Science.gov (United States)

    Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

    2015-01-01

    The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

  8. A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

    Science.gov (United States)

    Jiang, Wenhao; Chung, Fu-lai

    2014-01-01

    Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

    Directory of Open Access Journals (Sweden)

    Hongchao Song

    2017-01-01

    Full Text Available Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE and an ensemble k-nearest neighbor graphs- (K-NNG- based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

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

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    Chen, Ke; Wang, Shihai

    2011-01-01

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

  11. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Zhi He

    2017-10-01

    Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

  12. A regularized approach for geodesic-based semisupervised multimanifold learning.

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    Fan, Mingyu; Zhang, Xiaoqin; Lin, Zhouchen; Zhang, Zhongfei; Bao, Hujun

    2014-05-01

    Geodesic distance, as an essential measurement for data dissimilarity, has been successfully used in manifold learning. However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data points on manifolds and 2) little attention has been paid to building an explicit dimension reduction mapping for extracting the discriminative information hidden in the geodesic distances. In this paper, we regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed. The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised dimension reduction method for feature vectors. Experiments on the MNIST, USPS handwritten digit data sets, MIT CBCL face versus nonface data set, and an intelligent traffic data set show the effectiveness of the proposed algorithm.

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

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    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 ...

  14. Human semi-supervised learning.

    Science.gov (United States)

    Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

    2013-01-01

    Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

  15. Classification of gene expression data: A hubness-aware semi-supervised approach.

    Science.gov (United States)

    Buza, Krisztian

    2016-04-01

    Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. We performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. Our results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  17. Optimistic semi-supervised least squares classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant ...

  18. Cross-Domain Semi-Supervised Learning Using Feature Formulation.

    Science.gov (United States)

    Xingquan Zhu

    2011-12-01

    Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

  19. Information-theoretic semi-supervised metric learning via entropy regularization.

    Science.gov (United States)

    Niu, Gang; Dai, Bo; Yamada, Makoto; Sugiyama, Masashi

    2014-08-01

    We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Moreover, we regularize SERAPH by trace-norm regularization to encourage low-dimensional projections associated with the distance metric. The nonconvex optimization problem of SERAPH could be solved efficiently and stably by either a gradient projection algorithm or an EM-like iterative algorithm whose M-step is convex. Experiments demonstrate that SERAPH compares favorably with many well-known metric learning methods, and the learned Mahalanobis distance possesses high discriminability even under noisy environments.

  20. Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

    KAUST Repository

    Wang, Jim Jing-Yan; Almasri, Islam; Shi, Yuexiang; Gao, Xin

    2014-01-01

    of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue

  1. Constrained parameter estimation for semi-supervised learning : The case of the nearest mean classifier

    NARCIS (Netherlands)

    Loog, M.

    2011-01-01

    A rather simple semi-supervised version of the equally simple nearest mean classifier is presented. However simple, the proposed approach is of practical interest as the nearest mean classifier remains a relevant tool in biomedical applications or other areas dealing with relatively high-dimensional

  2. Projected estimators for robust semi-supervised classification

    NARCIS (Netherlands)

    Krijthe, J.H.; Loog, M.

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the

  3. Semi-supervised Learning with Deep Generative Models

    NARCIS (Netherlands)

    Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.

    2014-01-01

    The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

  4. Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

    OpenAIRE

    Satish Gajawada; Durga Toshniwal

    2012-01-01

    Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...

  5. Discriminative semi-supervised feature selection via manifold regularization.

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    Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

    2010-07-01

    Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

  6. Semi-supervised detection of intracranial pressure alarms using waveform dynamics

    International Nuclear Information System (INIS)

    Scalzo, Fabien; Hu, Xiao

    2013-01-01

    Patient monitoring systems in intensive care units (ICU) are usually set to trigger alarms when abnormal values are detected. Alarms are generated by threshold-crossing rules that lead to high false alarm rates. This is a recognized issue that causes alarm fatigue, waste of human resources, and increased patient risks. Recently developed smart alarm models require alarms to be validated by experts during the training phase. The manual annotation process involved is time-consuming and virtually impossible to achieve for the thousands of alarms recorded in the ICU every week. To tackle this problem, we investigate in this study if the use of semi-supervised learning methods, that can naturally integrate unlabeled data samples in the model, can be used to improve the accuracy of the alarm detection. As a proof of concept, the detection system is evaluated on intracranial pressure (ICP) signal alarms. Specific morphological and trending features are extracted from the ICP signal waveform to capture the dynamic of the signal prior to alarms. This study is based on a comprehensive dataset of 4791 manually labeled alarms recorded from 108 neurosurgical patients. A comparative analysis is provided between kernel spectral regression (SR-KDA) and support vector machine (SVM) both modified for the semi-supervised setting. Results obtained during the experimental evaluations indicate that the two models can significantly reduce false alarms using unlabeled samples; especially in the presence of a restrained number of labeled examples. At a true alarm recognition rate of 99%, the false alarm reduction rates improved from 9% (supervised) to 27% (semi-supervised) for SR-KDA, and from 3% (supervised) to 16% (semi-supervised) for SVM. (paper)

  7. SemiBoost: boosting for semi-supervised learning.

    Science.gov (United States)

    Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

    2009-11-01

    Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

  8. Enhanced manifold regularization for semi-supervised classification.

    Science.gov (United States)

    Gan, Haitao; Luo, Zhizeng; Fan, Yingle; Sang, Nong

    2016-06-01

    Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness regularization term. Hence the labels of labeled and unlabeled data vary smoothly along the geodesics on the manifold. However, MR has ignored the discriminative ability of the labeled and unlabeled data. To address the problem, we propose an enhanced MR framework for semi-supervised classification in which the local discriminative information of the labeled and unlabeled data is explicitly exploited. To make full use of labeled data, we firstly employ a semi-supervised clustering method to discover the underlying data space structure of the whole dataset. Then we construct a local discrimination graph to model the discriminative information of labeled and unlabeled data according to the discovered intrinsic structure. Therefore, the data points that may be from different clusters, though similar on the manifold, are enforced far away from each other. Finally, the discrimination graph is incorporated into the MR framework. In particular, we utilize semi-supervised fuzzy c-means and Laplacian regularized Kernel minimum squared error for semi-supervised clustering and classification, respectively. Experimental results on several benchmark datasets and face recognition demonstrate the effectiveness of our proposed method.

  9. Projected estimators for robust semi-supervised classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...... the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy typically considered in practice....

  10. Semi-supervised and unsupervised extreme learning machines.

    Science.gov (United States)

    Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

    2014-12-01

    Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

  11. Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

    Science.gov (United States)

    Sun, Shiliang; Xie, Xijiong

    2016-09-01

    Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

  12. Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

    Directory of Open Access Journals (Sweden)

    Mingwei Leng

    2013-01-01

    Full Text Available The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.

  13. A Semisupervised Cascade Classification Algorithm

    Directory of Open Access Journals (Sweden)

    Stamatis Karlos

    2016-01-01

    Full Text Available Classification is one of the most important tasks of data mining techniques, which have been adopted by several modern applications. The shortage of enough labeled data in the majority of these applications has shifted the interest towards using semisupervised methods. Under such schemes, the use of collected unlabeled data combined with a clearly smaller set of labeled examples leads to similar or even better classification accuracy against supervised algorithms, which use labeled examples exclusively during the training phase. A novel approach for increasing semisupervised classification using Cascade Classifier technique is presented in this paper. The main characteristic of Cascade Classifier strategy is the use of a base classifier for increasing the feature space by adding either the predicted class or the probability class distribution of the initial data. The classifier of the second level is supplied with the new dataset and extracts the decision for each instance. In this work, a self-trained NB∇C4.5 classifier algorithm is presented, which combines the characteristics of Naive Bayes as a base classifier and the speed of C4.5 for final classification. We performed an in-depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique has better accuracy in most cases.

  14. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan; Gao, Xin

    2014-01-01

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  15. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  16. Semi-supervised clustering methods.

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

  17. A Semisupervised Support Vector Machines Algorithm for BCI Systems

    Science.gov (United States)

    Qin, Jianzhao; Li, Yuanqing; Sun, Wei

    2007-01-01

    As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

  18. Label Information Guided Graph Construction for Semi-Supervised Learning.

    Science.gov (United States)

    Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

    2017-09-01

    In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

  19. Safe semi-supervised learning based on weighted likelihood.

    Science.gov (United States)

    Kawakita, Masanori; Takeuchi, Jun'ichi

    2014-05-01

    We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

  20. Moment constrained semi-supervised LDA

    DEFF Research Database (Denmark)

    Loog, Marco

    2012-01-01

    This BNAIC compressed contribution provides a summary of the work originally presented at the First IAPR Workshop on Partially Supervised Learning and published in [5]. It outlines the idea behind supervised and semi-supervised learning and highlights the major shortcoming of many current methods...

  1. Semisupervised Community Detection by Voltage Drops

    Directory of Open Access Journals (Sweden)

    Min Ji

    2016-01-01

    Full Text Available Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is OV+E for the sparse network with V vertices and E edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

  2. Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

    Science.gov (United States)

    Patel, Nihir; Wang, Jason T L

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  3. Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-05-23

    Protein-protein interactions are critically dependent on just a few residues (“hot spots”) at the interfaces. Hot spots make a dominant contribution to the binding free energy and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there exists a need for accurate and reliable computational hot spot prediction methods. Compared to the supervised hot spot prediction algorithms, the semi-supervised prediction methods can take into consideration both the labeled and unlabeled residues in the dataset during the prediction procedure. The transductive support vector machine has been utilized for this task and demonstrated a better prediction performance. To the best of our knowledge, however, none of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue prediction, by considering all the three semisupervised assumptions using nonlinear models. Our algorithm, IterPropMCS, works in an iterative manner. In each iteration, the algorithm first propagates the labels of the labeled residues to the unlabeled ones, along the shortest path between them on a graph, assuming that they lie on a nonlinear manifold. Then it selects the most confident residues as the labeled ones for the next iteration, according to the cluster and smoothness criteria, which is implemented by a nonlinear density estimator. Experiments on a benchmark dataset, using protein structure-based features, demonstrate that our approach is effective in predicting hot spots and compares favorably to other available methods. The results also show that our method outperforms the state-of-the-art transductive learning methods.

  4. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  5. Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

    Science.gov (United States)

    Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

    2014-03-01

    In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

  6. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  7. Coupled Semi-Supervised Learning

    Science.gov (United States)

    2010-05-01

    Additionally, specify the expected category of each relation argument to enable type-checking. Subsystem components and the KI can benefit from methods that...confirm that our coupled semi-supervised learning approaches can scale to hun- dreds of predicates and can benefit from using a diverse set of...organization yes California Institute of Technology vegetable food yes carrots vehicle item yes airplanes vertebrate animal yes videoGame product yes

  8. A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification.

    Science.gov (United States)

    Peikari, Mohammad; Salama, Sherine; Nofech-Mozes, Sharon; Martel, Anne L

    2018-05-08

    Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.

  9. Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

    Science.gov (United States)

    Jamshidpour, N.; Homayouni, S.; Safari, A.

    2017-09-01

    Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  10. GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

    Directory of Open Access Journals (Sweden)

    N. Jamshidpour

    2017-09-01

    Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

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

    Science.gov (United States)

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

    2016-08-26

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

  12. Semi-supervised Learning for Phenotyping Tasks.

    Science.gov (United States)

    Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K

    2015-01-01

    Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.

  13. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. 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.

  15. 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.

  16. 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.

  17. Semi-Supervised Priors for Microblog Language Identification

    NARCIS (Netherlands)

    Carter, S.; Tsagkias, E.; Weerkamp, W.; Boscarino, C.; Hofmann, K.; Jijkoun, V.; Meij, E.; de Rijke, M.; Weerkamp, W.

    2011-01-01

    Offering access to information in microblog posts requires successful language identification. Language identification on sparse and noisy data can be challenging. In this paper we explore the performance of a state-of-the-art n-gram-based language identifier, and we introduce two semi-supervised

  18. Solution path for manifold regularized semisupervised classification.

    Science.gov (United States)

    Wang, Gang; Wang, Fei; Chen, Tao; Yeung, Dit-Yan; Lochovsky, Frederick H

    2012-04-01

    Traditional learning algorithms use only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial human labeling efforts. On the other hand, unlabeled data are often relatively easy to collect. Semisupervised learning addresses this problem by using large quantities of unlabeled data with labeled data to build better learning algorithms. In this paper, we use the manifold regularization approach to formulate the semisupervised learning problem where a regularization framework which balances a tradeoff between loss and penalty is established. We investigate different implementations of the loss function and identify the methods which have the least computational expense. The regularization hyperparameter, which determines the balance between loss and penalty, is crucial to model selection. Accordingly, we derive an algorithm that can fit the entire path of solutions for every value of the hyperparameter. Its computational complexity after preprocessing is quadratic only in the number of labeled examples rather than the total number of labeled and unlabeled examples.

  19. Semi-Supervised Multitask Learning for Scene Recognition.

    Science.gov (United States)

    Lu, Xiaoqiang; Li, Xuelong; Mou, Lichao

    2015-09-01

    Scene recognition has been widely studied to understand visual information from the level of objects and their relationships. Toward scene recognition, many methods have been proposed. They, however, encounter difficulty to improve the accuracy, mainly due to two limitations: 1) lack of analysis of intrinsic relationships across different scales, say, the initial input and its down-sampled versions and 2) existence of redundant features. This paper develops a semi-supervised learning mechanism to reduce the above two limitations. To address the first limitation, we propose a multitask model to integrate scene images of different resolutions. For the second limitation, we build a model of sparse feature selection-based manifold regularization (SFSMR) to select the optimal information and preserve the underlying manifold structure of data. SFSMR coordinates the advantages of sparse feature selection and manifold regulation. Finally, we link the multitask model and SFSMR, and propose the semi-supervised learning method to reduce the two limitations. Experimental results report the improvements of the accuracy in scene recognition.

  20. Multichannel transfer function with dimensionality reduction

    KAUST Repository

    Kim, Han Suk

    2010-01-17

    The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.

  1. The dimensional reduction in a multi-dimensional cosmology

    International Nuclear Information System (INIS)

    Demianski, M.; Golda, Z.A.; Heller, M.; Szydlowski, M.

    1986-01-01

    Einstein's field equations are solved for the case of the eleven-dimensional vacuum spacetime which is the product R x Bianchi V x T 7 , where T 7 is a seven-dimensional torus. Among all possible solutions, the authors identify those in which the macroscopic space expands and the microscopic space contracts to a finite size. The solutions with this property are 'typical' within the considered class. They implement the idea of a purely dynamical dimensional reduction. (author)

  2. An alternative dimensional reduction prescription

    International Nuclear Information System (INIS)

    Edelstein, J.D.; Giambiagi, J.J.; Nunez, C.; Schaposnik, F.A.

    1995-08-01

    We propose an alternative dimensional reduction prescription which in respect with Green functions corresponds to drop the extra spatial coordinate. From this, we construct the dimensionally reduced Lagrangians both for scalars and fermions, discussing bosonization and supersymmetry in the particular 2-dimensional case. We argue that our proposal is in some situations more physical in the sense that it maintains the form of the interactions between particles thus preserving the dynamics corresponding to the higher dimensional space. (author). 12 refs

  3. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that pre-specified target region. Locally-biased problems of t...

  4. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

    OpenAIRE

    Qing Ye; Hao Pan; Changhua Liu

    2015-01-01

    A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA)...

  5. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification.

    Science.gov (United States)

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

  6. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available A novel semisupervised extreme learning machine (ELM with clustering discrimination manifold regularization (CDMR framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE. The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

  7. Tile-Based Semisupervised Classification of Large-Scale VHR Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Haikel Alhichri

    2018-01-01

    Full Text Available This paper deals with the problem of the classification of large-scale very high-resolution (VHR remote sensing (RS images in a semisupervised scenario, where we have a limited training set (less than ten training samples per class. Typical pixel-based classification methods are unfeasible for large-scale VHR images. Thus, as a practical and efficient solution, we propose to subdivide the large image into a grid of tiles and then classify the tiles instead of classifying pixels. Our proposed method uses the power of a pretrained convolutional neural network (CNN to first extract descriptive features from each tile. Next, a neural network classifier (composed of 2 fully connected layers is trained in a semisupervised fashion and used to classify all remaining tiles in the image. This basically presents a coarse classification of the image, which is sufficient for many RS application. The second contribution deals with the employment of the semisupervised learning to improve the classification accuracy. We present a novel semisupervised approach which exploits both the spectral and spatial relationships embedded in the remaining unlabelled tiles. In particular, we embed a spectral graph Laplacian in the hidden layer of the neural network. In addition, we apply regularization of the output labels using a spatial graph Laplacian and the random Walker algorithm. Experimental results obtained by testing the method on two large-scale images acquired by the IKONOS2 sensor reveal promising capabilities of this method in terms of classification accuracy even with less than ten training samples per class.

  8. Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

    Directory of Open Access Journals (Sweden)

    Sai Kiranmayee Samudrala

    2015-01-01

    Full Text Available Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

  9. Combination of supervised and semi-supervised regression models for improved unbiased estimation

    DEFF Research Database (Denmark)

    Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan

    2010-01-01

    In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...

  10. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Science.gov (United States)

    Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

    2014-01-01

    The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  11. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  12. MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....

  13. Scaling up graph-based semisupervised learning via prototype vector machines.

    Science.gov (United States)

    Zhang, Kai; Lan, Liang; Kwok, James T; Vucetic, Slobodan; Parvin, Bahram

    2015-03-01

    When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via l1 -regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.

  14. Regular graph construction for semi-supervised learning

    International Nuclear Information System (INIS)

    Vega-Oliveros, Didier A; Berton, Lilian; Eberle, Andre Mantini; Lopes, Alneu de Andrade; Zhao, Liang

    2014-01-01

    Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN

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

    Science.gov (United States)

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

    2018-03-01

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

  16. Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

    Directory of Open Access Journals (Sweden)

    Chunyang Wang

    2015-01-01

    Full Text Available Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

  17. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    improved scaling properties. We provide several empirical examples demonstrating how these semi-supervised eigenvectors can be used to perform locally-biased learning; and we discuss the relationship between our results and recent machine learning algorithms that use global eigenvectors of the graph......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might......-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing...

  18. Joint Sparse Recovery With Semisupervised MUSIC

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-01

    Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.

  19. A SURVEY OF SEMI-SUPERVISED LEARNING

    OpenAIRE

    Amrita Sadarangani *, Dr. Anjali Jivani

    2016-01-01

    Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classific...

  20. Dimensionality reduction of collective motion by principal manifolds

    Science.gov (United States)

    Gajamannage, Kelum; Butail, Sachit; Porfiri, Maurizio; Bollt, Erik M.

    2015-01-01

    While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods is not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high-dimensional space to the embedding space. Here, we propose an alternative approach that demands a two-dimensional embedding which topologically summarizes the high-dimensional data. In this sense, our approach is closely related to the construction of one-dimensional principal curves that minimize orthogonal error to data points subject to smoothness constraints. Specifically, we construct a two-dimensional principal manifold directly in the high-dimensional space using cubic smoothing splines, and define the embedding coordinates in terms of geodesic distances. Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates. Through representative examples, we show that compared to existing nonlinear dimensionality reduction methods, the principal manifold retains the original structure even in noisy and sparse datasets. The principal manifold finding algorithm is applied to configurations obtained from a dynamical system of multiple agents simulating a complex maneuver called predator mobbing, and the resulting two-dimensional embedding is compared with that of a well-established nonlinear dimensionality reduction method.

  1. Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields

    National Research Council Canada - National Science Library

    Mann, Gideon S; McCallum, Andrew

    2007-01-01

    Entropy regularization is a straightforward and successful method of semi-supervised learning that augments the traditional conditional likelihood objective function with an additional term that aims...

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

    Science.gov (United States)

    Chang, Chin-Chun; Lin, Po-Yi

    2015-03-01

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

  3. Semi-supervised rail defect detection from imbalanced image data

    NARCIS (Netherlands)

    Hajizadeh, S.; Nunez Vicencio, Alfredo; Tax, D.M.J.; Acarman, Tankut

    2016-01-01

    Rail defect detection by video cameras has recently gained much attention in both
    academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semisupervised learning

  4. Statistical mechanics of semi-supervised clustering in sparse graphs

    International Nuclear Information System (INIS)

    Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E

    2011-01-01

    We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears

  5. Graph-based semi-supervised learning

    CERN Document Server

    Subramanya, Amarnag

    2014-01-01

    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi

  6. A sparse grid based method for generative dimensionality reduction of high-dimensional data

    Science.gov (United States)

    Bohn, Bastian; Garcke, Jochen; Griebel, Michael

    2016-03-01

    Generative dimensionality reduction methods play an important role in machine learning applications because they construct an explicit mapping from a low-dimensional space to the high-dimensional data space. We discuss a general framework to describe generative dimensionality reduction methods, where the main focus lies on a regularized principal manifold learning variant. Since most generative dimensionality reduction algorithms exploit the representer theorem for reproducing kernel Hilbert spaces, their computational costs grow at least quadratically in the number n of data. Instead, we introduce a grid-based discretization approach which automatically scales just linearly in n. To circumvent the curse of dimensionality of full tensor product grids, we use the concept of sparse grids. Furthermore, in real-world applications, some embedding directions are usually more important than others and it is reasonable to refine the underlying discretization space only in these directions. To this end, we employ a dimension-adaptive algorithm which is based on the ANOVA (analysis of variance) decomposition of a function. In particular, the reconstruction error is used to measure the quality of an embedding. As an application, the study of large simulation data from an engineering application in the automotive industry (car crash simulation) is performed.

  7. Fermion masses from dimensional reduction

    International Nuclear Information System (INIS)

    Kapetanakis, D.; Zoupanos, G.

    1990-01-01

    We consider the fermion masses in gauge theories obtained from ten dimensions through dimensional reduction on coset spaces. We calculate the general fermion mass matrix and we apply the mass formula in illustrative examples. (orig.)

  8. Fermion masses from dimensional reduction

    Energy Technology Data Exchange (ETDEWEB)

    Kapetanakis, D. (National Research Centre for the Physical Sciences Democritos, Athens (Greece)); Zoupanos, G. (European Organization for Nuclear Research, Geneva (Switzerland))

    1990-10-11

    We consider the fermion masses in gauge theories obtained from ten dimensions through dimensional reduction on coset spaces. We calculate the general fermion mass matrix and we apply the mass formula in illustrative examples. (orig.).

  9. Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

    Science.gov (United States)

    Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

    2013-12-01

    Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.

  10. Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

    Science.gov (United States)

    Ochilov, S.; Alam, M. S.; Bal, A.

    2006-05-01

    Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

  11. Dimensional Reduction and Hadronic Processes

    International Nuclear Information System (INIS)

    Signer, Adrian; Stoeckinger, Dominik

    2008-01-01

    We consider the application of regularization by dimensional reduction to NLO corrections of hadronic processes. The general collinear singularity structure is discussed, the origin of the regularization-scheme dependence is identified and transition rules to other regularization schemes are derived.

  12. Optimizing area under the ROC curve using semi-supervised learning.

    Science.gov (United States)

    Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

    2015-01-01

    Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

  13. Coset space dimensional reduction of gauge theories

    Energy Technology Data Exchange (ETDEWEB)

    Kapetanakis, D. (Physik Dept., Technische Univ. Muenchen, Garching (Germany)); Zoupanos, G. (CERN, Geneva (Switzerland))

    1992-10-01

    We review the attempts to construct unified theories defined in higher dimensions which are dimensionally reduced over coset spaces. We employ the coset space dimensional reduction scheme, which permits the detailed study of the resulting four-dimensional gauge theories. In the context of this scheme we present the difficulties and the suggested ways out in the attempts to describe the observed interactions in a realistic way. (orig.).

  14. Coset space dimensional reduction of gauge theories

    International Nuclear Information System (INIS)

    Kapetanakis, D.; Zoupanos, G.

    1992-01-01

    We review the attempts to construct unified theories defined in higher dimensions which are dimensionally reduced over coset spaces. We employ the coset space dimensional reduction scheme, which permits the detailed study of the resulting four-dimensional gauge theories. In the context of this scheme we present the difficulties and the suggested ways out in the attempts to describe the observed interactions in a realistic way. (orig.)

  15. Maximum margin semi-supervised learning with irrelevant data.

    Science.gov (United States)

    Yang, Haiqin; Huang, Kaizhu; King, Irwin; Lyu, Michael R

    2015-10-01

    Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S(3)VMs), and SVMs learned from the universum (U-SVMs) as its special cases. More importantly, we deploy a concave-convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S(3)VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. Copyright

  16. Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

    Directory of Open Access Journals (Sweden)

    Yonghao Gu

    2017-01-01

    Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

  17. Visual texture perception via graph-based semi-supervised learning

    Science.gov (United States)

    Zhang, Qin; Dong, Junyu; Zhong, Guoqiang

    2018-04-01

    Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features' scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features' scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.

  18. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

    Directory of Open Access Journals (Sweden)

    Lintao Yang

    2018-01-01

    Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

  19. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  20. General dimensional reduction of ten-dimensional supergravity and superstring

    International Nuclear Information System (INIS)

    Ferrara, S.; Porrati, M.

    1986-01-01

    Dimensional reductions of supergravity theories are shown to yield to specific glasses of four-dimensional no-scale models with N=4, 2 or 1 residual supersymmetry. N=1 ''maximal'' supergravity lagrangian, corresponding to the ''untwisted'' sector of orbifold compactification of superstrings, contains nine families and has a no-scale structure based on the Kaehler manifold [SU(3, 3+3n)/SU(3)xSU(3+3n)]x[SU(1, 1)/U(1)]. The quantum consistency of the resulting theories give information on the non Kaluza-Klein (string) ''twisted'' sector. (orig.)

  1. Semi-Supervised Multiple Feature Analysis for Action Recognition

    Science.gov (United States)

    2013-11-26

    in saving la- beling costs while simultaneously achieving good performance. Most semi-supervised learning methods assume that nearby points are likely...3, 5, 10 and 15) per category in the training set, thus resulting in , , , and randomly la- beled videos, with the remaining training videos unlabeled...with the increase of la- beled training samples, the performance of all algorithms rises. Meanwhile, the performance differences between our method and

  2. Dimensional reduction of a generalized flux problem

    International Nuclear Information System (INIS)

    Moroz, A.

    1992-01-01

    In this paper, a generalized flux problem with Abelian and non-Abelian fluxes is considered. In the Abelian case we shall show that the generalized flux problem for tight-binding models of noninteracting electrons on either 2n- or (2n + 1)-dimensional lattice can always be reduced to an n-dimensional hopping problem. A residual freedom in this reduction enables one to identify equivalence classes of hopping Hamiltonians which have the same spectrum. In the non-Abelian case, the reduction is not possible in general unless the flux tensor factorizes into an Abelian one times are element of the corresponding algebra

  3. Metric dimensional reduction at singularities with implications to Quantum Gravity

    International Nuclear Information System (INIS)

    Stoica, Ovidiu Cristinel

    2014-01-01

    A series of old and recent theoretical observations suggests that the quantization of gravity would be feasible, and some problems of Quantum Field Theory would go away if, somehow, the spacetime would undergo a dimensional reduction at high energy scales. But an identification of the deep mechanism causing this dimensional reduction would still be desirable. The main contribution of this article is to show that dimensional reduction effects are due to General Relativity at singularities, and do not need to be postulated ad-hoc. Recent advances in understanding the geometry of singularities do not require modification of General Relativity, being just non-singular extensions of its mathematics to the limit cases. They turn out to work fine for some known types of cosmological singularities (black holes and FLRW Big-Bang), allowing a choice of the fundamental geometric invariants and physical quantities which remain regular. The resulting equations are equivalent to the standard ones outside the singularities. One consequence of this mathematical approach to the singularities in General Relativity is a special, (geo)metric type of dimensional reduction: at singularities, the metric tensor becomes degenerate in certain spacetime directions, and some properties of the fields become independent of those directions. Effectively, it is like one or more dimensions of spacetime just vanish at singularities. This suggests that it is worth exploring the possibility that the geometry of singularities leads naturally to the spontaneous dimensional reduction needed by Quantum Gravity. - Highlights: • The singularities we introduce are described by finite geometric/physical objects. • Our singularities are accompanied by dimensional reduction effects. • They affect the metric, the measure, the topology, the gravitational DOF (Weyl = 0). • Effects proposed in other approaches to Quantum Gravity are obtained naturally. • The geometric dimensional reduction obtained

  4. Dimensional reduction in quantum gravity

    Energy Technology Data Exchange (ETDEWEB)

    Hooft, G [Rijksuniversiteit Utrecht (Netherlands). Inst. voor Theoretische Fysica

    1994-12-31

    The requirement that physical phenomena associated with gravitational collapse should be duly reconciled with the postulates of quantum mechanics implies that at a Planckian scale our world is not 3+1 dimensional. Rather, the observable degrees of freedom can best be described as if they were Boolean variables defined on a two- dimensional lattice, evolving with time. This observation, deduced from not much more than unitarity, entropy and counting arguments, implies severe restrictions on possible models of quantum gravity. Using cellular automata as an example it is argued that this dimensional reduction implies more constraints than the freedom we have in constructing models. This is the main reason why so-far no completely consistent mathematical models of quantum black holes have been found. (author). 13 refs, 2 figs.

  5. Active link selection for efficient semi-supervised community detection

    Science.gov (United States)

    Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

    2015-01-01

    Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

  6. An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

    Directory of Open Access Journals (Sweden)

    Tingquan Deng

    2016-01-01

    Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

  7. Construction of N=8 supergravity theories by dimensional reduction

    International Nuclear Information System (INIS)

    Boucher, W.

    1985-01-01

    In this paper I ask which N=8 supergravity theories in four dimensions can be obtained by dimensional reduction of the N=1 supergravity theory in eleven dimensions. Several years ago Scherk and Schwarz produced a particular class of N = 8 theories by giving a dimensional reduction scheme on the restricted class of coset spaces, G/H, with dim H=0 (and therefore dim G=7). I generalize their considerations by looking at arbitrary (seven-dimensional) coset spaces. Also, instead of giving a particular ansatz which happens to work, I set about the distinctly more difficult task of determining all ansatzes which produce N=8 theories. The basic ingredient of my dimensional reduction scheme is the demand that certain symmetries, including supersymmetry, be truncated consistently. I find the surprising result that the only N=8 theories obtainable within the contexts of my scheme are those theories already written down by Scherk and Schwarz. In particular dim H=0 and dim G=7. Independently of these considerations, I prove that any dimensional reduction scheme which consistently truncates supersymmetry must also be consistent with the equations of motion. I discuss Lorentz-invariant solutions of the theories of Scherk and Schwarz, pointing out that since the ansatz of Scherk and Schwarz consistently truncates supersymmetry, any solution of these theories is also a solution of the N=1 supergravity theory in eleven dimensions and, hence, in particular that there is a Freund-Rubin-type ansatz for these theories. However I demonstrate that for most gauge groups the ansatz must be trivial which implies that for these theories the cosmological constant of any Lorentz-invariant solution must be zero (classically). Finally, I make some comparisons with work by Manton on dimensional reduction. (orig.)

  8. Semi-supervised morphosyntactic classification of Old Icelandic.

    Science.gov (United States)

    Urban, Kryztof; Tangherlini, Timothy R; Vijūnas, Aurelijus; Broadwell, Peter M

    2014-01-01

    We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.

  9. Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; de Bakker, M.; Bouten, W.; Shamoun-Baranes, J.; Afsarmanesh, H.

    2012-01-01

    In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used

  10. Dimensionality reduction with unsupervised nearest neighbors

    CERN Document Server

    Kramer, Oliver

    2013-01-01

    This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

  11. Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.

    Science.gov (United States)

    Yang, Jiaoyun; Wang, Haipeng; Ding, Huitong; An, Ning; Alterovitz, Gil

    2017-01-19

    Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.

  12. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

    Directory of Open Access Journals (Sweden)

    Ryan C Williamson

    2016-12-01

    Full Text Available Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

  13. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

  14. A Tannakian approach to dimensional reduction of principal bundles

    Science.gov (United States)

    Álvarez-Cónsul, Luis; Biswas, Indranil; García-Prada, Oscar

    2017-08-01

    Let P be a parabolic subgroup of a connected simply connected complex semisimple Lie group G. Given a compact Kähler manifold X, the dimensional reduction of G-equivariant holomorphic vector bundles over X × G / P was carried out in Álvarez-Cónsul and García-Prada (2003). This raises the question of dimensional reduction of holomorphic principal bundles over X × G / P. The method of Álvarez-Cónsul and García-Prada (2003) is special to vector bundles; it does not generalize to principal bundles. In this paper, we adapt to equivariant principal bundles the Tannakian approach of Nori, to describe the dimensional reduction of G-equivariant principal bundles over X × G / P, and to establish a Hitchin-Kobayashi type correspondence. In order to be able to apply the Tannakian theory, we need to assume that X is a complex projective manifold.

  15. Method of dimensionality reduction in contact mechanics and friction

    CERN Document Server

    Popov, Valentin L

    2015-01-01

    This book describes for the first time a simulation method for the fast calculation of contact properties and friction between rough surfaces in a complete form. In contrast to existing simulation methods, the method of dimensionality reduction (MDR) is based on the exact mapping of various types of three-dimensional contact problems onto contacts of one-dimensional foundations. Within the confines of MDR, not only are three dimensional systems reduced to one-dimensional, but also the resulting degrees of freedom are independent from another. Therefore, MDR results in an enormous reduction of the development time for the numerical implementation of contact problems as well as the direct computation time and can ultimately assume a similar role in tribology as FEM has in structure mechanics or CFD methods, in hydrodynamics. Furthermore, it substantially simplifies analytical calculation and presents a sort of “pocket book edition” of the entirety contact mechanics. Measurements of the rheology of bodies in...

  16. Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

    International Nuclear Information System (INIS)

    Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

    2017-01-01

    Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification. (paper)

  17. A semi-supervised approach using label propagation to support citation screening.

    Science.gov (United States)

    Kontonatsios, Georgios; Brockmeier, Austin J; Przybyła, Piotr; McNaught, John; Mu, Tingting; Goulermas, John Y; Ananiadou, Sophia

    2017-08-01

    Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  18. N-Dimensional LLL Reduction Algorithm with Pivoted Reflection

    Directory of Open Access Journals (Sweden)

    Zhongliang Deng

    2018-01-01

    Full Text Available The Lenstra-Lenstra-Lovász (LLL lattice reduction algorithm and many of its variants have been widely used by cryptography, multiple-input-multiple-output (MIMO communication systems and carrier phase positioning in global navigation satellite system (GNSS to solve the integer least squares (ILS problem. In this paper, we propose an n-dimensional LLL reduction algorithm (n-LLL, expanding the Lovász condition in LLL algorithm to n-dimensional space in order to obtain a further reduced basis. We also introduce pivoted Householder reflection into the algorithm to optimize the reduction time. For an m-order positive definite matrix, analysis shows that the n-LLL reduction algorithm will converge within finite steps and always produce better results than the original LLL reduction algorithm with n > 2. The simulations clearly prove that n-LLL is better than the original LLL in reducing the condition number of an ill-conditioned input matrix with 39% improvement on average for typical cases, which can significantly reduce the searching space for solving ILS problem. The simulation results also show that the pivoted reflection has significantly declined the number of swaps in the algorithm by 57%, making n-LLL a more practical reduction algorithm.

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

    Science.gov (United States)

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

    2016-07-01

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

  20. Dimensional reduction in anomaly mediation

    International Nuclear Information System (INIS)

    Boyda, Ed; Murayama, Hitoshi; Pierce, Aaron

    2002-01-01

    We offer a guide to dimensional reduction in theories with anomaly-mediated supersymmetry breaking. Evanescent operators proportional to ε arise in the bare Lagrangian when it is reduced from d=4 to d=4-2ε dimensions. In the course of a detailed diagrammatic calculation, we show that inclusion of these operators is crucial. The evanescent operators conspire to drive the supersymmetry-breaking parameters along anomaly-mediation trajectories across heavy particle thresholds, guaranteeing the ultraviolet insensitivity

  1. Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

    DEFF Research Database (Denmark)

    Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan

    2017-01-01

    We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approxi...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.......We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...

  2. Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

    Science.gov (United States)

    Yamazaki, Keisuke

    2015-09-01

    Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. A semi-supervised learning approach for RNA secondary structure prediction.

    Science.gov (United States)

    Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

    2015-08-01

    RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  5. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

    Science.gov (United States)

    Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

    2016-03-01

    One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised

  6. Dimensional reduction from entanglement in Minkowski space

    International Nuclear Information System (INIS)

    Brustein, Ram; Yarom, Amos

    2005-01-01

    Using a quantum field theoretic setting, we present evidence for dimensional reduction of any sub-volume of Minkowksi space. First, we show that correlation functions of a class of operators restricted to a sub-volume of D-dimensional Minkowski space scale as its surface area. A simple example of such area scaling is provided by the energy fluctuations of a free massless quantum field in its vacuum state. This is reminiscent of area scaling of entanglement entropy but applies to quantum expectation values in a pure state, rather than to statistical averages over a mixed state. We then show, in a specific case, that fluctuations in the bulk have a lower-dimensional representation in terms of a boundary theory at high temperature. (author)

  7. Dimensional reduction in field theory and hidden symmetries in extended supergravity

    International Nuclear Information System (INIS)

    Kremmer, E.

    1985-01-01

    Dimensional reduction in field theories is discussed both in theories which do not include gravity and in gravity theories. In particular, 11-dimensional supergravity and its reduction to 4 dimensions is considered. Hidden symmetries of supergravity with N=8 in 4 dimensions, global E 7 and local SU(8)-invariances in particular are detected. The hidden symmmetries permit to interpret geometrically the scalar fields

  8. Supersymmetry and the Parisi-Sourlas dimensional reduction: A rigorous proof

    International Nuclear Information System (INIS)

    Klein, A.; Landau, L.J.; Perez, J.F.

    1984-01-01

    Functional integrals that are formally related to the average correlation functions of a classical field theory in the presence of random external sources are given a rigorous meaning. Their dimensional reduction to the Schwinger functions of the corresponding quantum field theory in two fewer dimensions is proven. This is done by reexpressing those functional integrals as expectations of a supersymmetric field theory. The Parisi-Sourlas dimensional reduction of a supersymmetric field theory to a usual quantum field theory in two fewer dimensions is proven. (orig.)

  9. Dimensional reduction in Bose-Einstein-condensed alkali-metal vapors

    International Nuclear Information System (INIS)

    Salasnich, L.; Reatto, L.; Parola, A.

    2004-01-01

    We investigate the effects of dimensional reduction in atomic Bose-Einstein condensates (BECs) induced by a strong harmonic confinement in the cylindric radial direction or in the cylindric axial direction. The former case corresponds to a transition from three dimensions (3D) to 1D in cigar-shaped BECs, while the latter case corresponds to a transition from 3D to 2D in disk-shaped BECs. We analyze the first sound velocity in axially homogeneous cigar-shaped BECs and in radially homogeneous disk-shaped BECs. We consider also the dimensional reduction in a BEC confined by a harmonic potential both in the radial direction and in the axial direction. By using a variational approach, we calculate monopole and quadrupole collective oscillations of the BEC. We find that the frequencies of these collective oscillations are related to the dimensionality and to the repulsive or attractive interatomic interaction

  10. Pole masses of quarks in dimensional reduction

    International Nuclear Information System (INIS)

    Avdeev, L.V.; Kalmykov, M.Yu.

    1997-01-01

    Pole masses of quarks in quantum chromodynamics are calculated to the two-loop order in the framework of the regularization by dimensional reduction. For the diagram with a light quark loop, the non-Euclidean asymptotic expansion is constructed with the external momentum on the mass shell of a heavy quark

  11. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  12. Improving head and body pose estimation through semi-supervised manifold alignment

    KAUST Repository

    Heili, Alexandre

    2014-10-27

    In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head pose. While this previous approach showed promising results, the learning of the underlying manifold structure of the features in the train and target data and the need to align them were not explored despite the fact that the pose features between two datasets may vary according to the scene, e.g. due to different camera point of view or perspective. In this paper, we propose to use a semi-supervised manifold alignment method to bring the train and target samples closer within the resulting embedded space. To this end, we consider an adaptation set from the target data and rely on (weak) labels, given for example by the velocity direction whenever they are reliable. These labels, along with the training labels are used to bias the manifold distance within each manifold and to establish correspondences for alignment.

  13. Perturbative QCD Lagrangian at large distances and stochastic dimensionality reduction. Pt. 2

    International Nuclear Information System (INIS)

    Shintani, M.

    1986-11-01

    Using the method of stochastic dimensional reduction, we derive a four-dimensional quantum effective Lagrangian for the classical Yang-Mills system coupled to the Gaussian white noise. It is found that the Lagrangian coincides with the perturbative QCD at large distances constructed in our previous paper. That formalism is based on the local covariant operator formalism which maintains the unitarity of the S-matrix. Furthermore, we show the non-perturbative equivalence between super-Lorentz invariant sectors of the effective Lagrangian and two dimensional QCD coupled to the adjoint pseudo-scalars. This implies that stochastic dimensionality reduction by two is approximately operative in QCD at large distances. (orig.)

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

    Science.gov (United States)

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

    2012-04-01

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

  15. TPSLVM: a dimensionality reduction algorithm based on thin plate splines.

    Science.gov (United States)

    Jiang, Xinwei; Gao, Junbin; Wang, Tianjiang; Shi, Daming

    2014-10-01

    Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model (GPLVM), our proposed TPSLVM is more powerful especially when the dimensionality of the latent space is low. Also, TPSLVM is robust to shift and rotation. This paper investigates two extensions of TPSLVM, i.e., the back-constrained TPSLVM (BC-TPSLVM) and TPSLVM with dynamics (TPSLVM-DM) as well as their combination BC-TPSLVM-DM. Experimental results show that TPSLVM and its extensions provide better data visualization and more efficient dimensionality reduction compared to PCA, GPLVM, ISOMAP, etc.

  16. Symmetries, integrals, and three-dimensional reductions of Plebanski's second heavenly equation

    International Nuclear Information System (INIS)

    Neyzi, F.; Sheftel, M. B.; Yazici, D.

    2007-01-01

    We study symmetries and conservation laws for Plebanski's second heavenly equation written as a first-order nonlinear evolutionary system which admits a multi-Hamiltonian structure. We construct an optimal system of one-dimensional subalgebras and all inequivalent three-dimensional symmetry reductions of the original four-dimensional system. We consider these two-component evolutionary systems in three dimensions as natural candidates for integrable systems

  17. A finite-dimensional reduction method for slightly supercritical elliptic problems

    Directory of Open Access Journals (Sweden)

    Riccardo Molle

    2004-01-01

    Full Text Available We describe a finite-dimensional reduction method to find solutions for a class of slightly supercritical elliptic problems. A suitable truncation argument allows us to work in the usual Sobolev space even in the presence of supercritical nonlinearities: we modify the supercritical term in such a way to have subcritical approximating problems; for these problems, the finite-dimensional reduction can be obtained applying the methods already developed in the subcritical case; finally, we show that, if the truncation is realized at a sufficiently large level, then the solutions of the approximating problems, given by these methods, also solve the supercritical problems when the parameter is small enough.

  18. The (2+1)-dimensional axial universes—solutions to the Einstein equations, dimensional reduction points and Klein–Fock–Gordon waves

    International Nuclear Information System (INIS)

    Fiziev, P P; Shirkov, D V

    2012-01-01

    The paper presents a generalization and further development of our recent publications, where solutions of the Klein–Fock–Gordon equation defined on a few particular D = (2 + 1)-dimensional static spacetime manifolds were considered. The latter involve toy models of two-dimensional spaces with axial symmetry, including dimensional reduction to the one-dimensional space as a singular limiting case. Here, the non-static models of space geometry with axial symmetry are under consideration. To make these models closer to physical reality, we define a set of ‘admissible’ shape functions ρ(t, z) as the (2 + 1)-dimensional Einstein equation solutions in the vacuum spacetime, in the presence of the Λ-term and for the spacetime filled with the standard ‘dust’. It is curious that in the last case the Einstein equations reduce to the well-known Monge–Ampère equation, thus enabling one to obtain the general solution of the Cauchy problem, as well as a set of other specific solutions involving one arbitrary function. A few explicit solutions of the Klein–Fock–Gordon equation in this set are given. An interesting qualitative feature of these solutions relates to the dimensional reduction points, their classification and time behavior. In particular, these new entities could provide us with novel insight into the nature of P- and T-violations and of the Big Bang. A short comparison with other attempts to utilize the dimensional reduction of the spacetime is given. (paper)

  19. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Fei Gao

    Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

  20. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning.

    Science.gov (United States)

    Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

    2016-06-29

    The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

  1. Reduction formalism for dimensionally regulated one-loop N-point integrals

    International Nuclear Information System (INIS)

    Binoth, T.; Guillet, J.Ph.; Heinrich, G.

    2000-01-01

    We consider one-loop scalar and tensor integrals with an arbitrary number of external legs relevant for multi-parton processes in massless theories. We present a procedure to reduce N-point scalar functions with generic 4-dimensional external momenta to box integrals in (4-2ε) dimensions. We derive a formula valid for arbitrary N and give an explicit expression for N=6. Further a tensor reduction method for N-point tensor integrals is presented. We prove that generically higher dimensional integrals contribute only to order ε for N≥5. The tensor reduction can be solved iteratively such that any tensor integral is expressible in terms of scalar integrals. Explicit formulas are given up to N=6

  2. Kantowski-Sachs multidimensional cosmological models and dynamical dimensional reduction

    International Nuclear Information System (INIS)

    Demianski, M.; Rome Univ.; Golda, Z.A.; Heller, M.; Szydlowski, M.

    1988-01-01

    Einstein's field equations are solved for a multidimensional spacetime (KS) x Tsup(m), where (KS) is a four-dimensional Kantowski-Sachs spacetime and Tsup(m) is an m-dimensional torus. Among all possible vacuum solutions there is a large class of spacetimes in which the macroscopic space expands and the microscopic space contracts to a finite volume. We also consider a non-vacuum case and we explicitly solve the field equations for the matter satisfying the Zel'dovich equation of state. In non-vacuum models, with matter satisfying an equation of state p = γρ, O ≤ γ < 1, at a sufficiently late stage of evolution the microspace always expands and the dynamical dimensional reduction does not occur. (author)

  3. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

    Science.gov (United States)

    Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

    2017-12-01

    Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

  4. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

  5. Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

    Science.gov (United States)

    Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

    2017-01-01

    An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

  6. Center-vortex dominance after dimensional reduction of SU(2) lattice gauge theory

    OpenAIRE

    Gattnar, J.; Langfeld, K.; Schafke, A.; Reinhardt, H.

    2000-01-01

    The high-temperature phase of SU(2) Yang-Mills theory is addressed by means of dimensional reduction with a special emphasis on the properties of center vortices. For this purpose, the vortex vacuum which arises from center projection is studied in pure 3-dimensional Yang-Mills theory as well as in the 3-dimensional adjoint Higgs model which describes the high temperature phase of the 4-dimensional SU(2) gauge theory. We find center-dominance within the numerical accuracy of 10%.

  7. Semi-Supervised Generation with Cluster-aware Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Fraccaro, Marco; Winther, Ole

    2017-01-01

    Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Clust...... a log-likelihood of −79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods.......Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster...

  8. Chaotic oscillator containing memcapacitor and meminductor and its dimensionality reduction analysis.

    Science.gov (United States)

    Yuan, Fang; Wang, Guangyi; Wang, Xiaowei

    2017-03-01

    In this paper, smooth curve models of meminductor and memcapacitor are designed, which are generalized from a memristor. Based on these models, a new five-dimensional chaotic oscillator that contains a meminductor and memcapacitor is proposed. By dimensionality reducing, this five-dimensional system can be transformed into a three-dimensional system. The main work of this paper is to give the comparisons between the five-dimensional system and its dimensionality reduction model. To investigate dynamics behaviors of the two systems, equilibrium points and stabilities are analyzed. And the bifurcation diagrams and Lyapunov exponent spectrums are used to explore their properties. In addition, digital signal processing technologies are used to realize this chaotic oscillator, and chaotic sequences are generated by the experimental device, which can be used in encryption applications.

  9. Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

    Directory of Open Access Journals (Sweden)

    N.R. Sakthivel

    2014-03-01

    Full Text Available Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

  10. SSC-EKE: Semi-Supervised Classification with Extensive Knowledge Exploitation.

    Science.gov (United States)

    Qian, Pengjiang; Xi, Chen; Xu, Min; Jiang, Yizhang; Su, Kuan-Hao; Wang, Shitong; Muzic, Raymond F

    2018-01-01

    We introduce a new, semi-supervised classification method that extensively exploits knowledge. The method has three steps. First, the manifold regularization mechanism, adapted from the Laplacian support vector machine (LapSVM), is adopted to mine the manifold structure embedded in all training data, especially in numerous label-unknown data. Meanwhile, by converting the labels into pairwise constraints, the pairwise constraint regularization formula (PCRF) is designed to compensate for the few but valuable labelled data. Second, by further combining the PCRF with the manifold regularization, the precise manifold and pairwise constraint jointly regularized formula (MPCJRF) is achieved. Third, by incorporating the MPCJRF into the framework of the conventional SVM, our approach, referred to as semi-supervised classification with extensive knowledge exploitation (SSC-EKE), is developed. The significance of our research is fourfold: 1) The MPCJRF is an underlying adjustment, with respect to the pairwise constraints, to the graph Laplacian enlisted for approximating the potential data manifold. This type of adjustment plays the correction role, as an unbiased estimation of the data manifold is difficult to obtain, whereas the pairwise constraints, converted from the given labels, have an overall high confidence level. 2) By transforming the values of the two terms in the MPCJRF such that they have the same range, with a trade-off factor varying within the invariant interval [0, 1), the appropriate impact of the pairwise constraints to the graph Laplacian can be self-adaptively determined. 3) The implication regarding extensive knowledge exploitation is embodied in SSC-EKE. That is, the labelled examples are used not only to control the empirical risk but also to constitute the MPCJRF. Moreover, all data, both labelled and unlabelled, are recruited for the model smoothness and manifold regularization. 4) The complete framework of SSC-EKE organically incorporates multiple

  11. Restoration of dimensional reduction in the random-field Ising model at five dimensions

    Science.gov (United States)

    Fytas, Nikolaos G.; Martín-Mayor, Víctor; Picco, Marco; Sourlas, Nicolas

    2017-04-01

    The random-field Ising model is one of the few disordered systems where the perturbative renormalization group can be carried out to all orders of perturbation theory. This analysis predicts dimensional reduction, i.e., that the critical properties of the random-field Ising model in D dimensions are identical to those of the pure Ising ferromagnet in D -2 dimensions. It is well known that dimensional reduction is not true in three dimensions, thus invalidating the perturbative renormalization group prediction. Here, we report high-precision numerical simulations of the 5D random-field Ising model at zero temperature. We illustrate universality by comparing different probability distributions for the random fields. We compute all the relevant critical exponents (including the critical slowing down exponent for the ground-state finding algorithm), as well as several other renormalization-group invariants. The estimated values of the critical exponents of the 5D random-field Ising model are statistically compatible to those of the pure 3D Ising ferromagnet. These results support the restoration of dimensional reduction at D =5 . We thus conclude that the failure of the perturbative renormalization group is a low-dimensional phenomenon. We close our contribution by comparing universal quantities for the random-field problem at dimensions 3 ≤D equality at all studied dimensions.

  12. Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

    Science.gov (United States)

    Honnorat, Nicolas; Dong, Aoyan; Meisenzahl-Lechner, Eva; Koutsouleris, Nikolaos; Davatzikos, Christos

    2017-12-20

    Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Dimensionality Reduction Methods: Comparative Analysis of methods PCA, PPCA and KPCA

    Directory of Open Access Journals (Sweden)

    Jorge Arroyo-Hernández

    2016-01-01

    Full Text Available The dimensionality reduction methods are algorithms mapping the set of data in subspaces derived from the original space, of fewer dimensions, that allow a description of the data at a lower cost. Due to their importance, they are widely used in processes associated with learning machine. This article presents a comparative analysis of PCA, PPCA and KPCA dimensionality reduction methods. A reconstruction experiment of worm-shape data was performed through structures of landmarks located in the body contour, with methods having different number of main components. The results showed that all methods can be seen as alternative processes. Nevertheless, thanks to the potential for analysis in the features space and the method for calculation of its preimage presented, KPCA offers a better method for recognition process and pattern extraction

  14. Semi-Supervised Learning to Identify UMLS Semantic Relations.

    Science.gov (United States)

    Luo, Yuan; Uzuner, Ozlem

    2014-01-01

    The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

  15. Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

    Science.gov (United States)

    Ma, Xiaoke; Wang, Bingbo; Yu, Liang

    2018-01-01

    Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

  16. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Science.gov (United States)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  17. Superfluid hydrodynamics of polytropic gases: dimensional reduction and sound velocity

    International Nuclear Information System (INIS)

    Bellomo, N; Mazzarella, G; Salasnich, L

    2014-01-01

    Motivated by the fact that two-component confined fermionic gases in Bardeen–Cooper–Schrieffer–Bose–Einstein condensate (BCS–BEC) crossover can be described through an hydrodynamical approach, we study these systems—both in the cigar-shaped configuration and in the disc-shaped one—by using a polytropic Lagrangian density. We start from the Popov Lagrangian density and obtain, after a dimensional reduction process, the equations that control the dynamics of such systems. By solving these equations we study the sound velocity as a function of the density by analyzing how the dimensionality affects this velocity. (paper)

  18. WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning

    Directory of Open Access Journals (Sweden)

    Chunjing Song

    2017-11-01

    Full Text Available Wireless local area network (WLAN fingerprint positioning is an indoor localization technique with high accuracy and low hardware requirements. However, collecting received signal strength (RSS samples for the fingerprint database is time-consuming and labor-intensive, hindering the use of this technique. The popular crowdsourcing sampling technique has been introduced to reduce the workload of sample collection, but has two challenges: one is the heterogeneity of devices, which can significantly affect the positioning accuracy; the other is the requirement of users’ intervention in traditional crowdsourcing, which reduces the practicality of the system. In response to these challenges, we have proposed a new WLAN indoor positioning strategy, which incorporates a new preprocessing method for RSS samples, the implicit crowdsourcing sampling technique, and a semi-supervised learning algorithm. First, implicit crowdsourcing does not require users’ intervention. The acquisition program silently collects unlabeled samples, the RSS samples, without information about the position. Secondly, to cope with the heterogeneity of devices, the preprocessing method maps all the RSS values of samples to a uniform range and discretizes them. Finally, by using a large number of unlabeled samples with some labeled samples, Co-Forest, the introduced semi-supervised learning algorithm, creates and repeatedly refines a random forest ensemble classifier that performs well for location estimation. The results of experiments conducted in a real indoor environment show that the proposed strategy reduces the demand for large quantities of labeled samples and achieves good positioning accuracy.

  19. Ultraviolet finiteness of N = 8 supergravity, spontaneously broken by dimensional reduction

    International Nuclear Information System (INIS)

    Sezgin, E.; Nieuwenhuizen, P. van

    1982-06-01

    The one-loop corrections to scalar-scalar scattering in N = 8 supergravity with 4 masses from dimensional reduction, are finite. We discuss various mechanisms that cancel the cosmological constant and infra-red divergences due to finite but non-vanishing tadpoles. (author)

  20. Anisotropic inflation in a 5D standing wave braneworld and effective dimensional reduction

    Energy Technology Data Exchange (ETDEWEB)

    Gogberashvili, Merab, E-mail: gogber@gmail.com [Andronikashvili Institute of Physics, 6 Tamarashvili St., Tbilisi 0177, Georgia (United States); Javakhishvili State University, 3 Chavchavadze Ave., Tbilisi 0128, Georgia (United States); Herrera-Aguilar, Alfredo, E-mail: aha@fis.unam.mx [Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Apdo. Postal 48-3, 62251 Cuernavaca, Morelos (Mexico); Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Edificio C-3, Ciudad Universitaria, CP 58040, Morelia, Michoacán (Mexico); Malagón-Morejón, Dagoberto, E-mail: malagon@fis.unam.mx [Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Apdo. Postal 48-3, 62251 Cuernavaca, Morelos (Mexico); Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Edificio C-3, Ciudad Universitaria, CP 58040, Morelia, Michoacán (Mexico); Mora-Luna, Refugio Rigel, E-mail: rigel@ifm.umich.mx [Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, Edificio C-3, Ciudad Universitaria, CP 58040, Morelia, Michoacán (Mexico)

    2013-10-01

    We investigate a cosmological solution within the framework of a 5D standing wave braneworld model generated by gravity coupled to a massless scalar phantom-like field. By obtaining a full exact solution of the model we found a novel dynamical mechanism in which the anisotropic nature of the primordial metric gives rise to (i) inflation along certain spatial dimensions, and (ii) deflation and a shrinking reduction of the number of spatial dimensions along other directions. This dynamical mechanism can be relevant for dimensional reduction in string and other higher-dimensional theories in the attempt of getting a 4D isotropic expanding space–time.

  1. Anisotropic inflation in a 5D standing wave braneworld and effective dimensional reduction

    International Nuclear Information System (INIS)

    Gogberashvili, Merab; Herrera-Aguilar, Alfredo; Malagón-Morejón, Dagoberto; Mora-Luna, Refugio Rigel

    2013-01-01

    We investigate a cosmological solution within the framework of a 5D standing wave braneworld model generated by gravity coupled to a massless scalar phantom-like field. By obtaining a full exact solution of the model we found a novel dynamical mechanism in which the anisotropic nature of the primordial metric gives rise to (i) inflation along certain spatial dimensions, and (ii) deflation and a shrinking reduction of the number of spatial dimensions along other directions. This dynamical mechanism can be relevant for dimensional reduction in string and other higher-dimensional theories in the attempt of getting a 4D isotropic expanding space–time

  2. Use of dimensionality reduction for structural mapping of hip joint osteoarthritis data

    International Nuclear Information System (INIS)

    Theoharatos, C; Fotopoulos, S; Boniatis, I; Panayiotakis, G; Panagiotopoulos, E

    2009-01-01

    A visualization-based, computer-oriented, classification scheme is proposed for assessing the severity of hip osteoarthritis (OA) using dimensionality reduction techniques. The introduced methodology tries to cope with the confined ability of physicians to structurally organize the entire available set of medical data into semantically similar categories and provide the capability to make visual observations among the ensemble of data using low-dimensional biplots. In this work, 18 pelvic radiographs of patients with verified unilateral hip OA are evaluated by experienced physicians and assessed into Normal, Mild and Severe following the Kellgren and Lawrence scale. Two regions of interest corresponding to radiographic hip joint spaces are determined and representative features are extracted using a typical texture analysis technique. The structural organization of all hip OA data is accomplished using distance and topology preservation-based dimensionality reduction techniques. The resulting map is a low-dimensional biplot that reflects the intrinsic organization of the ensemble of available data and which can be directly accessed by the physician. The conceivable visualization scheme can potentially reveal critical data similarities and help the operator to visually estimate their initial diagnosis. In addition, it can be used to detect putative clustering tendencies, examine the presence of data similarities and indicate the existence of possible false alarms in the initial perceptual evaluation

  3. Optimal dimensionality reduction of complex dynamics: the chess game as diffusion on a free-energy landscape.

    Science.gov (United States)

    Krivov, Sergei V

    2011-07-01

    Dimensionality reduction is ubiquitous in the analysis of complex dynamics. The conventional dimensionality reduction techniques, however, focus on reproducing the underlying configuration space, rather than the dynamics itself. The constructed low-dimensional space does not provide a complete and accurate description of the dynamics. Here I describe how to perform dimensionality reduction while preserving the essential properties of the dynamics. The approach is illustrated by analyzing the chess game--the archetype of complex dynamics. A variable that provides complete and accurate description of chess dynamics is constructed. The winning probability is predicted by describing the game as a random walk on the free-energy landscape associated with the variable. The approach suggests a possible way of obtaining a simple yet accurate description of many important complex phenomena. The analysis of the chess game shows that the approach can quantitatively describe the dynamics of processes where human decision-making plays a central role, e.g., financial and social dynamics.

  4. Dimensional reduction and BRST approach to the description of a Regge trajectory

    International Nuclear Information System (INIS)

    Pashnev, A.I.; Tsulaya, M.M.

    1997-01-01

    The local free field theory for Regge trajectory is described in the framework of the BRST-quantization method. The corresponding BRST-charge is constructed with the help of the method of dimensional reduction

  5. Perturbative QCD lagrangian at large distances and stochastic dimensionality reduction

    International Nuclear Information System (INIS)

    Shintani, M.

    1986-10-01

    We construct a Lagrangian for perturbative QCD at large distances within the covariant operator formalism which explains the color confinement of quarks and gluons while maintaining unitarity of the S-matrix. It is also shown that when interactions are switched off, the mechanism of stochastic dimensionality reduction is operative in the system due to exact super-Lorentz symmetries. (orig.)

  6. Semi-Supervised Half-Quadratic Nonnegative Matrix Factorization for Face Recognition

    KAUST Repository

    Alghamdi, Masheal M.

    2014-05-01

    Face recognition is a challenging problem in computer vision. Difficulties such as slight differences between similar faces of different people, changes in facial expressions, light and illumination condition, and pose variations add extra complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method. Different versions of NMF have been proposed. Wang et al. proposed the graph-based semi-supervised nonnegative learning (S2N2L) algorithm that uses labeled data in constructing intrinsic and penalty graph to enforce separability of labeled data, which leads to a greater discriminating power. Moreover the geometrical structure of labeled and unlabeled data is preserved through using the smoothness assumption by creating a similarity graph that conserves the neighboring information for all labeled and unlabeled data. However, S2N2L is sensitive to light changes, illumination, and partial occlusion. In this thesis, we propose a Semi-Supervised Half-Quadratic NMF (SSHQNMF) algorithm that combines the benefits of S2N2L and the robust NMF by the half- quadratic minimization (HQNMF) algorithm.Our algorithm improves upon the S2N2L algorithm by replacing the Frobenius norm with a robust M-Estimator loss function. A multiplicative update solution for our SSHQNMF algorithmis driven using the half- 4 quadratic (HQ) theory. Extensive experiments on ORL, Yale-A and a subset of the PIE data sets for nine M-estimator loss functions for both SSHQNMF and HQNMF algorithms are investigated, and compared with several state-of-the-art supervised and unsupervised algorithms, along with the original S2N2L algorithm in the context of classification, clustering, and robustness against partial occlusion. The proposed algorithm outperformed the other algorithms. Furthermore, SSHQNMF with Maximum Correntropy

  7. Object-based Dimensionality Reduction in Land Surface Phenology Classification

    Directory of Open Access Journals (Sweden)

    Brian E. Bunker

    2016-11-01

    Full Text Available Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.

  8. Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

    Science.gov (United States)

    Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

    2017-02-01

    Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

  9. One-loop dimensional reduction of the linear σ model

    International Nuclear Information System (INIS)

    Malbouisson, A.P.C.; Silva-Neto, M.B.; Svaiter, N.F.

    1997-05-01

    We perform the dimensional reduction of the linear σ model at one-loop level. The effective of the reduced theory obtained from the integration over the nonzero Matsubara frequencies is exhibited. Thermal mass and coupling constant renormalization constants are given, as well as the thermal renormalization group which controls the dependence of the counterterms on the temperature. We also recover, for the reduced theory, the vacuum instability of the model for large N. (author)

  10. Prototype Vector Machine for Large Scale Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Kai; Kwok, James T.; Parvin, Bahram

    2009-04-29

    Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

  11. Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

    Science.gov (United States)

    Sumner, Jeremy G

    2017-03-01

    We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

  12. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  13. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    International Nuclear Information System (INIS)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  14. Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

    Directory of Open Access Journals (Sweden)

    Lan Shu

    2008-07-01

    Full Text Available Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE’s KNN algorithm. In addition, kernel method based support vector machine (SVM will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.

  15. Lifetime of rho meson in correlation with magnetic-dimensional reduction

    Energy Technology Data Exchange (ETDEWEB)

    Kawaguchi, Mamiya [Nagoya University, Department of Physics, Nagoya (Japan); Matsuzaki, Shinya [Nagoya University, Department of Physics, Nagoya (Japan); Nagoya University, Institute for Advanced Research, Nagoya (Japan)

    2017-04-15

    It is naively expected that in a strong magnetic configuration, the Landau quantization ceases the neutral rho meson to decay to the charged pion pair, so the neutral rho meson will be long-lived. To closely access this naive observation, we explicitly compute the charged pion loop in the magnetic field at the one-loop level, to evaluate the magnetic dependence of the lifetime for the neutral rho meson as well as its mass. Due to the dimensional reduction induced by the magnetic field (violation of the Lorentz invariance), the polarization (spin s{sub z} = 0, ±1) modes of the rho meson, as well as the corresponding pole mass and width, are decomposed in a nontrivial manner compared to the vacuum case. To see the significance of the reduction effect, we simply take the lowest Landau level approximation to analyze the spin-dependent rho masses and widths. We find that the ''fate'' of the rho meson may be more complicated because of the magnetic-dimensional reduction: as the magnetic field increases, the rho width for the spin s{sub z} = 0 starts to develop, reaches a peak, then vanishes at the critical magnetic field to which the folklore refers. On the other side, the decay rates of the other rhos for s{sub z} = ±1 monotonically increase as the magnetic field develops. The correlation between the polarization dependence and the Landau level truncation is also addressed. (orig.)

  16. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-12-01

    Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

  18. Relation between the pole and the minimally subtracted mass in dimensional regularization and dimensional reduction to three-loop order

    Energy Technology Data Exchange (ETDEWEB)

    Marquard, P.; Mihaila, L.; Steinhauser, M. [Karlsruhe Univ. (T.H.) (Germany). Inst. fuer Theoretische Teilchenphysik; Piclum, J.H. [Karlsruhe Univ. (T.H.) (Germany). Inst. fuer Theoretische Teilchenphysik]|[Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik

    2007-02-15

    We compute the relation between the pole quark mass and the minimally subtracted quark mass in the framework of QCD applying dimensional reduction as a regularization scheme. Special emphasis is put on the evanescent couplings and the renormalization of the {epsilon}-scalar mass. As a by-product we obtain the three-loop on-shell renormalization constants Z{sub m}{sup OS} and Z{sub 2}{sup OS} in dimensional regularization and thus provide the first independent check of the analytical results computed several years ago. (orig.)

  19. Participatory three dimensional mapping for the preparation of landslide disaster risk reduction program

    Science.gov (United States)

    Kusratmoko, Eko; Wibowo, Adi; Cholid, Sofyan; Pin, Tjiong Giok

    2017-07-01

    This paper presents the results of applications of participatory three dimensional mapping (P3DM) method for fqcilitating the people of Cibanteng' village to compile a landslide disaster risk reduction program. Physical factors, as high rainfall, topography, geology and land use, and coupled with the condition of demographic and social-economic factors, make up the Cibanteng region highly susceptible to landslides. During the years 2013-2014 has happened 2 times landslides which caused economic losses, as a result of damage to homes and farmland. Participatory mapping is one part of the activities of community-based disaster risk reduction (CBDRR)), because of the involvement of local communities is a prerequisite for sustainable disaster risk reduction. In this activity, participatory mapping method are done in two ways, namely participatory two-dimensional mapping (P2DM) with a focus on mapping of disaster areas and participatory three-dimensional mapping (P3DM) with a focus on the entire territory of the village. Based on the results P3DM, the ability of the communities in understanding the village environment spatially well-tested and honed, so as to facilitate the preparation of the CBDRR programs. Furthermore, the P3DM method can be applied to another disaster areas, due to it becomes a medium of effective dialogue between all levels of involved communities.

  20. Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

    International Nuclear Information System (INIS)

    Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial

    2016-01-01

    Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range of physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the

  1. Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

    Science.gov (United States)

    Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial

    2016-09-01

    Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range of physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the

  2. Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

    Energy Technology Data Exchange (ETDEWEB)

    Tripathy, Rohit, E-mail: rtripath@purdue.edu; Bilionis, Ilias, E-mail: ibilion@purdue.edu; Gonzalez, Marcial, E-mail: marcial-gonzalez@purdue.edu

    2016-09-15

    Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range of physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the

  3. Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets

    Science.gov (United States)

    Kim, S. K.; Prabhat, M.; Williams, D. N.

    2017-12-01

    Deep neural networks have been successfully applied to solve problem to detect extreme weather events in large scale climate datasets and attend superior performance that overshadows all previous hand-crafted methods. Recent work has shown that multichannel spatiotemporal encoder-decoder CNN architecture is able to localize events in semi-supervised bounding box. Motivated by this work, we propose new learning metric based on Variational Auto-Encoders (VAE) and Long-Short-Term-Memory (LSTM) to track extreme weather events in spatio-temporal dataset. We consider spatio-temporal object tracking problems as learning probabilistic distribution of continuous latent features of auto-encoder using stochastic variational inference. For this, we assume that our datasets are i.i.d and latent features is able to be modeled by Gaussian distribution. In proposed metric, we first train VAE to generate approximate posterior given multichannel climate input with an extreme climate event at fixed time. Then, we predict bounding box, location and class of extreme climate events using convolutional layers given input concatenating three features including embedding, sampled mean and standard deviation. Lastly, we train LSTM with concatenated input to learn timely information of dataset by recurrently feeding output back to next time-step's input of VAE. Our contribution is two-fold. First, we show the first semi-supervised end-to-end architecture based on VAE to track extreme weather events which can apply to massive scaled unlabeled climate datasets. Second, the information of timely movement of events is considered for bounding box prediction using LSTM which can improve accuracy of localization. To our knowledge, this technique has not been explored neither in climate community or in Machine Learning community.

  4. A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network.

    Science.gov (United States)

    Zhang, Xiaotian; Yin, Jian; Zhang, Xu

    2018-03-02

    Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types.

  5. Some remarks on dimensional reduction of Gauge theories and model building

    International Nuclear Information System (INIS)

    Rudolph, G.; Karl-Marx-Universitaet, Leipzig; Volobujev, I.P.

    1989-01-01

    We study the group-theoretical aspect of dimensional reduction of pure gauge theories and propose a method of solving the constraint equations for scalar fields. We show that there are possibilities of model building which differ from those commonly used. In particular, we give examples in which the resulting potential is not of Higgs type. (orig.)

  6. Lie symmetry analysis and reduction for exact solution of (2+1)-dimensional Bogoyavlensky-Konopelchenko equation by geometric approach

    Science.gov (United States)

    Ray, S. Saha

    2018-04-01

    In this paper, the symmetry analysis and similarity reduction of the (2+1)-dimensional Bogoyavlensky-Konopelchenko (B-K) equation are investigated by means of the geometric approach of an invariance group, which is equivalent to the classical Lie symmetry method. Using the extended Harrison and Estabrook’s differential forms approach, the infinitesimal generators for (2+1)-dimensional B-K equation are obtained. Firstly, the vector field associated with the Lie group of transformation is derived. Then the symmetry reduction and the corresponding explicit exact solution of (2+1)-dimensional B-K equation is obtained.

  7. On symmetry reduction and exact solutions of the linear one-dimensional Schroedinger equation

    International Nuclear Information System (INIS)

    Barannik, L.L.

    1996-01-01

    Symmetry reduction of the Schroedinger equation with potential is carried out on subalgebras of the Lie algebra which is the direct sum of the special Galilei algebra and one-dimensional algebra. Some new exact solutions are obtained

  8. Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification

    Science.gov (United States)

    Iyigun, Cem; Ben-Israel, Adi

    Semi-supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data). These two modes of data analysis are combined in a parameterized model, the parameter θ ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (cluster centers, classification rule) depend on the parameter θ, an insensitivity to θ indicates that the prior information is in agreement with the intrinsic cluster structure, and is otherwise redundant. This explains why some data sets (such as the Wisconsin breast cancer data, Merz and Murphy, UCI repository of machine learning databases, University of California, Irvine, CA) give good results for all reasonable classification methods. The uncertainty of classification is represented here by the geometric mean of the membership probabilities, shown to be an entropic distance related to the Kullback-Leibler divergence.

  9. A method of integration of atomistic simulations and continuum mechanics by collecting of dynamical systems with dimensional reduction

    International Nuclear Information System (INIS)

    Kaczmarek, J.

    2002-01-01

    Elementary processes responsible for phenomena in material are frequently related to scale close to atomic one. Therefore atomistic simulations are important for material sciences. On the other hand continuum mechanics is widely applied in mechanics of materials. It seems inevitable that both methods will gradually integrate. A multiscale method of integration of these approaches called collection of dynamical systems with dimensional reduction is introduced in this work. The dimensional reduction procedure realizes transition between various scale models from an elementary dynamical system (EDS) to a reduced dynamical system (RDS). Mappings which transform variables and forces, skeletal dynamical system (SDS) and a set of approximation and identification methods are main components of this procedure. The skeletal dynamical system is a set of dynamical systems parameterized by some constants and has variables related to the dimensionally reduced model. These constants are identified with the aid of solutions of the elementary dynamical system. As a result we obtain a dimensionally reduced dynamical system which describes phenomena in an averaged way in comparison with the EDS. Concept of integration of atomistic simulations with continuum mechanics consists in using a dynamical system describing evolution of atoms as an elementary dynamical system. Then, we introduce a continuum skeletal dynamical system within the dimensional reduction procedure. In order to construct such a system we have to modify a continuum mechanics formulation to some degree. Namely, we formalize scale of averaging for continuum theory and as a result we consider continuum with finite-dimensional fields only. Then, realization of dimensional reduction is possible. A numerical example of realization of the dimensional reduction procedure is shown. We consider a one dimensional chain of atoms interacting by Lennard-Jones potential. Evolution of this system is described by an elementary

  10. Wideband radar cross section reduction using two-dimensional phase gradient metasurfaces

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yongfeng; Qu, Shaobo; Wang, Jiafu; Chen, Hongya [College of Science, Air Force Engineering University, Xi' an, Shaanxi 710051 (China); Zhang, Jieqiu [College of Science, Air Force Engineering University, Xi' an, Shaanxi 710051 (China); Electronic Materials Research Laboratory, Key Laboratory of Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China); Xu, Zhuo [Electronic Materials Research Laboratory, Key Laboratory of Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China); Zhang, Anxue [School of Electronics and Information Engineering, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China)

    2014-06-02

    Phase gradient metasurface (PGMs) are artificial surfaces that can provide pre-defined in-plane wave-vectors to manipulate the directions of refracted/reflected waves. In this Letter, we propose to achieve wideband radar cross section (RCS) reduction using two-dimensional (2D) PGMs. A 2D PGM was designed using a square combination of 49 split-ring sub-unit cells. The PGM can provide additional wave-vectors along the two in-plane directions simultaneously, leading to either surface wave conversion, deflected reflection, or diffuse reflection. Both the simulation and experiment results verified the wide-band, polarization-independent, high-efficiency RCS reduction induced by the 2D PGM.

  11. Wideband radar cross section reduction using two-dimensional phase gradient metasurfaces

    International Nuclear Information System (INIS)

    Li, Yongfeng; Qu, Shaobo; Wang, Jiafu; Chen, Hongya; Zhang, Jieqiu; Xu, Zhuo; Zhang, Anxue

    2014-01-01

    Phase gradient metasurface (PGMs) are artificial surfaces that can provide pre-defined in-plane wave-vectors to manipulate the directions of refracted/reflected waves. In this Letter, we propose to achieve wideband radar cross section (RCS) reduction using two-dimensional (2D) PGMs. A 2D PGM was designed using a square combination of 49 split-ring sub-unit cells. The PGM can provide additional wave-vectors along the two in-plane directions simultaneously, leading to either surface wave conversion, deflected reflection, or diffuse reflection. Both the simulation and experiment results verified the wide-band, polarization-independent, high-efficiency RCS reduction induced by the 2D PGM.

  12. A semi-supervised learning framework for biomedical event extraction based on hidden topics.

    Science.gov (United States)

    Zhou, Deyu; Zhong, Dayou

    2015-05-01

    Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely

  13. Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.

    Science.gov (United States)

    Glaser, Joshua I; Zamft, Bradley M; Church, George M; Kording, Konrad P

    2015-01-01

    Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, "puzzle imaging," that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

  14. The helpfulness of category labels in semi-supervised learning depends on category structure.

    Science.gov (United States)

    Vong, Wai Keen; Navarro, Daniel J; Perfors, Amy

    2016-02-01

    The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people's responses are driven by the specific set of labels they see. We present an extension of Anderson's Rational Model of Categorization that captures this effect.

  15. Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

    Directory of Open Access Journals (Sweden)

    Fawaz H.H. Mahyoub

    2014-12-01

    Full Text Available Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.

  16. Semi-Supervised Learning for Classification of Protein Sequence Data

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

    Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.

  17. Cosmological string solutions by dimensional reduction

    International Nuclear Information System (INIS)

    Behrndt, K.; Foerste, S.

    1993-12-01

    We obtain cosmological four dimensional solutions of the low energy effective string theory by reducing a five dimensional black hole, and black hole-de Sitter solution of the Einstein gravity down to four dimensions. The appearance of a cosmological constant in the five dimensional Einstein-Hilbert produces a special dilaton potential in the four dimensional effective string action. Cosmological scenarios implement by our solutions are discussed

  18. Computerized breast cancer analysis system using three stage semi-supervised learning method.

    Science.gov (United States)

    Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

    2016-10-01

    A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. In this study we developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data weighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features were incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy were computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who underwent routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions were extracted and used in this study, and among them 90 regions were treated as labeled while the rest were treated as unlabeled. Using our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference between using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. This study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system

  19. Dimensional reduction for D3-brane moduli

    International Nuclear Information System (INIS)

    Cownden, Brad; Frey, Andrew R.; Marsh, M.C. David; Underwood, Bret

    2016-01-01

    Warped string compactifications are central to many attempts to stabilize moduli and connect string theory with cosmology and particle phenomenology. We present a first-principles derivation of the low-energy 4D effective theory from dimensional reduction of a D3-brane in a warped Calabi-Yau compactification of type IIB string theory with imaginary self-dual 3-form flux, including effects of D3-brane motion beyond the probe approximation, and find the metric on the moduli space of brane positions, the universal volume modulus, and axions descending from the 4-form potential. As D3-branes may be considered as carrying either electric or magnetic charges for the self-dual 5-form field strength, we present calculations in both duality frames. Our results are consistent with, but extend significantly, earlier results on the low-energy effective theory arising from D3-branes in string compactifications.

  20. Three-dimensional assessment of unilateral subcondylar fracture using computed tomography after open reduction

    Directory of Open Access Journals (Sweden)

    Sathya Kumar Devireddy

    2014-01-01

    Full Text Available Objective: The aim was to assess the accuracy of three-dimensional anatomical reductions achieved by open method of treatment in cases of displaced unilateral mandibular subcondylar fractures using preoperative (pre op and postoperative (post op computed tomography (CT scans. Materials and Methods: In this prospective study, 10 patients with unilateral sub condylar fractures confirmed by an orthopantomogram were included. A pre op and post op CT after 1 week of surgical procedure was taken in axial, coronal and sagittal plane along with three-dimensional reconstruction. Standard anatomical parameters, which undergo changes due to fractures of the mandibular condyle were measured in pre and post op CT scans in three planes and statistically analysed for the accuracy of the reduction comparing the following variables: (a Pre op fractured and nonfractured side (b post op fractured and nonfractured side (c pre op fractured and post op fractured side. P < 0.05 was considered as significant. Results: Three-dimensional anatomical reduction was possible in 9 out of 10 cases (90%. The statistical analysis of each parameter in three variables revealed (P < 0.05 that there was a gross change in the dimensions of the parameters obtained in pre op fractured and nonfractured side. When these parameters were assessed in post op CT for the three variables there was no statistical difference between the post op fractured side and non fractured side. The same parameters were analysed for the three variables in pre op fractured and post op fractured side and found significant statistical difference suggesting a considerable change in the dimensions of the fractured side post operatively. Conclusion: The statistical and clinical results in our study emphasised that it is possible to fix the condyle in three-dimensional anatomical positions with open method of treatment and avoid post op degenerative joint changes. CT is the ideal imaging tool and should be used on

  1. Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

    International Nuclear Information System (INIS)

    Del Frate, F.; Iapaolo, M.; Casadio, S.; Godin-Beekmann, S.; Petitdidier, M.

    2005-01-01

    Dimensionality reduction can be of crucial importance in the application of inversion schemes to atmospheric remote sensing data. In this study the problem of dimensionality reduction in the retrieval of ozone concentration profiles from the radiance measurements provided by the instrument Global Ozone Monitoring Experiment (GOME) on board of ESA satellite ERS-2 is considered. By means of radiative transfer modelling, neural networks and pruning algorithms, a complete procedure has been designed to extract the GOME spectral ranges most crucial for the inversion. The quality of the resulting retrieval algorithm has been evaluated by comparing its performance to that yielded by other schemes and co-located profiles obtained with lidar measurements

  2. Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error.

    Science.gov (United States)

    Kazmierczak, Steven C; Leen, Todd K; Erdogmus, Deniz; Carreira-Perpinan, Miguel A

    2007-01-01

    The clinical laboratory generates large amounts of patient-specific data. Detection of errors that arise during pre-analytical, analytical, and post-analytical processes is difficult. We performed a pilot study, utilizing a multidimensional data reduction technique, to assess the utility of this method for identifying errors in laboratory data. We evaluated 13,670 individual patient records collected over a 2-month period from hospital inpatients and outpatients. We utilized those patient records that contained a complete set of 14 different biochemical analytes. We used two-dimensional generative topographic mapping to project the 14-dimensional record to a two-dimensional space. The use of a two-dimensional generative topographic mapping technique to plot multi-analyte patient data as a two-dimensional graph allows for the rapid identification of potentially anomalous data. Although we performed a retrospective analysis, this technique has the benefit of being able to assess laboratory-generated data in real time, allowing for the rapid identification and correction of anomalous data before they are released to the physician. In addition, serial laboratory multi-analyte data for an individual patient can also be plotted as a two-dimensional plot. This tool might also be useful for assessing patient wellbeing and prognosis.

  3. Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

    Directory of Open Access Journals (Sweden)

    Fubiao Feng

    2017-03-01

    Full Text Available Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA.

  4. ANALYSIS OF IMPACT ON COMPOSITE STRUCTURES WITH THE METHOD OF DIMENSIONALITY REDUCTION

    Directory of Open Access Journals (Sweden)

    Valentin L. Popov

    2015-04-01

    Full Text Available In the present paper, we discuss the impact of rigid profiles on continua with non-local criteria for plastic yield. For the important case of media whose hardness is inversely proportional to the indentation radius, we suggest a rigorous treatment based on the method of dimensionality reduction (MDR and study the example of indentation by a conical profile.

  5. Dimensional reduction of 10d heterotic string effective lagrangian with higher derivative terms

    International Nuclear Information System (INIS)

    Lalak, Z.; Pawelczyk, J.

    1989-11-01

    Dimensional reduction of the 10d Supergravity-Yang-Mills theories containing up to four derivatives is described. Unexpected nondiagonal corrections to 4d gauge kinetic function and negative contributions to scalar potential are found. We analyzed the general structure of the resulting lagrangian and discuss the possible phenomenological consequences. (author)

  6. The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.

    Directory of Open Access Journals (Sweden)

    Ross S Williamson

    2015-04-01

    Full Text Available Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID, uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.

  7. Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

    Science.gov (United States)

    Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

    2017-08-20

    Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93

  8. Multi-Label Classification by Semi-Supervised Singular Value Decomposition.

    Science.gov (United States)

    Jing, Liping; Shen, Chenyang; Yang, Liu; Yu, Jian; Ng, Michael K

    2017-10-01

    Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.

  9. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  10. Target discrimination method for SAR images based on semisupervised co-training

    Science.gov (United States)

    Wang, Yan; Du, Lan; Dai, Hui

    2018-01-01

    Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

  11. Porosity estimation by semi-supervised learning with sparsely available labeled samples

    Science.gov (United States)

    Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

    2017-09-01

    This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

  12. Semi-supervised vibration-based classification and condition monitoring of compressors

    Science.gov (United States)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

    Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

  13. The N=4 supersymmetric E8 gauge theory and coset space dimensional reduction

    International Nuclear Information System (INIS)

    Olive, D.; West, P.

    1983-01-01

    Reasons are given to suggest that the N=4 supersymmetric E 8 gauge theory be considered as a serious candidate for a physical theory. The symmetries of this theory are broken by a scheme based on coset space dimensional reduction. The resulting theory possesses four conventional generations of low-mass fermions together with their mirror particles. (orig.)

  14. Denoising and dimensionality reduction of genomic data

    Science.gov (United States)

    Capobianco, Enrico

    2005-05-01

    Genomics represents a challenging research field for many quantitative scientists, and recently a vast variety of statistical techniques and machine learning algorithms have been proposed and inspired by cross-disciplinary work with computational and systems biologists. In genomic applications, the researcher deals with noisy and complex high-dimensional feature spaces; a wealth of genes whose expression levels are experimentally measured, can often be observed for just a few time points, thus limiting the available samples. This unbalanced combination suggests that it might be hard for standard statistical inference techniques to come up with good general solutions, likewise for machine learning algorithms to avoid heavy computational work. Thus, one naturally turns to two major aspects of the problem: sparsity and intrinsic dimensionality. These two aspects are studied in this paper, where for both denoising and dimensionality reduction, a very efficient technique, i.e., Independent Component Analysis, is used. The numerical results are very promising, and lead to a very good quality of gene feature selection, due to the signal separation power enabled by the decomposition technique. We investigate how the use of replicates can improve these results, and deal with noise through a stabilization strategy which combines the estimated components and extracts the most informative biological information from them. Exploiting the inherent level of sparsity is a key issue in genetic regulatory networks, where the connectivity matrix needs to account for the real links among genes and discard many redundancies. Most experimental evidence suggests that real gene-gene connections represent indeed a subset of what is usually mapped onto either a huge gene vector or a typically dense and highly structured network. Inferring gene network connectivity from the expression levels represents a challenging inverse problem that is at present stimulating key research in biomedical

  15. Three-dimensional assemblies of graphene prepared by a novel chemical reduction-induced self-assembly method

    KAUST Repository

    Zhang, Lianbin; Chen, Guoying; Hedhili, Mohamed N.; Zhang, Hongnan; Wang, Peng

    2012-01-01

    In this study, three-dimensional (3D) graphene assemblies are prepared from graphene oxide (GO) by a facile in situ reduction-assembly method, using a novel, low-cost, and environment-friendly reducing medium which is a combination of oxalic acid

  16. Contaminant source identification using semi-supervised machine learning

    International Nuclear Information System (INIS)

    Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

    2017-01-01

    Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

  17. Exploring the effects of dimensionality reduction in deep networks for force estimation in robotic-assisted surgery

    Science.gov (United States)

    Aviles, Angelica I.; Alsaleh, Samar; Sobrevilla, Pilar; Casals, Alicia

    2016-03-01

    Robotic-Assisted Surgery approach overcomes the limitations of the traditional laparoscopic and open surgeries. However, one of its major limitations is the lack of force feedback. Since there is no direct interaction between the surgeon and the tissue, there is no way of knowing how much force the surgeon is applying which can result in irreversible injuries. The use of force sensors is not practical since they impose different constraints. Thus, we make use of a neuro-visual approach to estimate the applied forces, in which the 3D shape recovery together with the geometry of motion are used as input to a deep network based on LSTM-RNN architecture. When deep networks are used in real time, pre-processing of data is a key factor to reduce complexity and improve the network performance. A common pre-processing step is dimensionality reduction which attempts to eliminate redundant and insignificant information by selecting a subset of relevant features to use in model construction. In this work, we show the effects of dimensionality reduction in a real-time application: estimating the applied force in Robotic-Assisted Surgeries. According to the results, we demonstrated positive effects of doing dimensionality reduction on deep networks including: faster training, improved network performance, and overfitting prevention. We also show a significant accuracy improvement, ranging from about 33% to 86%, over existing approaches related to force estimation.

  18. A Novel Four-Dimensional Energy-Saving and Emission-Reduction System and Its Linear Feedback Control

    Directory of Open Access Journals (Sweden)

    Minggang Wang

    2012-01-01

    Full Text Available This paper reports a new four-dimensional energy-saving and emission-reduction chaotic system. The system is obtained in accordance with the complicated relationship between energy saving and emission reduction, carbon emission, economic growth, and new energy development. The dynamics behavior of the system will be analyzed by means of Lyapunov exponents and equilibrium points. Linear feedback control methods are used to suppress chaos to unstable equilibrium. Numerical simulations are presented to show these results.

  19. Rhythmic dynamics and synchronization via dimensionality reduction: application to human gait.

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    Full Text Available Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.

  20. Reduction of infinite dimensional equations

    Directory of Open Access Journals (Sweden)

    Zhongding Li

    2006-02-01

    Full Text Available In this paper, we use the general Legendre transformation to show the infinite dimensional integrable equations can be reduced to a finite dimensional integrable Hamiltonian system on an invariant set under the flow of the integrable equations. Then we obtain the periodic or quasi-periodic solution of the equation. This generalizes the results of Lax and Novikov regarding the periodic or quasi-periodic solution of the KdV equation to the general case of isospectral Hamiltonian integrable equation. And finally, we discuss the AKNS hierarchy as a special example.

  1. Supervised linear dimensionality reduction with robust margins for object recognition

    Science.gov (United States)

    Dornaika, F.; Assoum, A.

    2013-01-01

    Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

  2. The supersymmetric Adler-Bardeen theorem and regularization by dimensional reduction

    International Nuclear Information System (INIS)

    Ensign, P.; Mahanthappa, K.T.

    1987-01-01

    We examine the subtraction scheme dependence of the anomaly of the supersymmetric, gauge singlet axial current in pure and coupled supersymmetric Yang-Mills theories. Preserving supersymmetry and gauge invariance explicitly by using supersymmetric background field theory and dimensional reduction, we show that only the one-loop value of the axial anomaly is subtraction scheme independent, and that one can always define a subtraction scheme in which the Adler-Bardeen theorem is satisfied to all orders in perturbation theory. In general this subtraction scheme may be non-minimal, but in both the pure and the coupled theories, the Adler-Bardeen theorem is satisfied to two loops in minimal subtraction. (orig.)

  3. On dimensional reduction over coset spaces

    International Nuclear Information System (INIS)

    Kapetanakis, D.; Zoupanos, G.

    1990-01-01

    Gauge theories defined in higher dimensions can be dimensionally reduced over coset spaces giving definite predictions for the resulting four-dimensional theory. We present the most interesting features of these theories as well as an attempt to construct a model with realistic low energy behaviour within this framework. (author)

  4. Visual Vehicle Tracking Based on Deep Representation and Semisupervised Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2017-01-01

    Full Text Available Discriminative tracking methods use binary classification to discriminate between the foreground and background and have achieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking. Hence, discriminative classifiers must use their own classification results to update themselves, which may lead to feedback-induced tracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and transfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear mapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer train and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the background images. Finally, a particle filter is used to estimate vehicle position. We demonstrate experimentally that our proposed vehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with similar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.

  5. Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews

    Directory of Open Access Journals (Sweden)

    Mita K. Dalal

    2013-01-01

    Full Text Available The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%.

  6. Fractional exclusion and braid statistics in one dimension: a study via dimensional reduction of Chern–Simons theory

    International Nuclear Information System (INIS)

    Ye, Fei; Marchetti, P A; Su, Z B; Yu, L

    2017-01-01

    The relation between braid and exclusion statistics is examined in one-dimensional systems, within the framework of Chern–Simons statistical transmutation in gauge invariant form with an appropriate dimensional reduction. If the matter action is anomalous, as for chiral fermions, a relation between braid and exclusion statistics can be established explicitly for both mutual and nonmutual cases. However, if it is not anomalous, the exclusion statistics of emergent low energy excitations is not necessarily connected to the braid statistics of the physical charged fields of the system. Finally, we also discuss the bosonization of one-dimensional anyonic systems through T-duality. (paper)

  7. Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

    Science.gov (United States)

    Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

    2013-01-01

    Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2

  8. Discrete symmetries and coset space dimensional reduction

    International Nuclear Information System (INIS)

    Kapetanakis, D.; Zoupanos, G.

    1989-01-01

    We consider the discrete symmetries of all the six-dimensional coset spaces and we apply them in gauge theories defined in ten dimensions which are dimensionally reduced over these homogeneous spaces. Particular emphasis is given in the consequences of the discrete symmetries on the particle content as well as on the symmetry breaking a la Hosotani of the resulting four-dimensional theory. (orig.)

  9. Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

    Science.gov (United States)

    2013-01-01

    Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

  10. Three-dimensional porous hollow fibre copper electrodes for efficient and high-rate electrochemical carbon dioxide reduction

    NARCIS (Netherlands)

    Kas, Recep; Hummadi, Khalid Khazzal; Kortlever, Ruud; de Wit, Patrick; Milbrat, Alexander; Luiten-Olieman, Maria W.J.; Benes, Nieck Edwin; Koper, Marc T.M.; Mul, Guido

    2016-01-01

    Aqueous-phase electrochemical reduction of carbon dioxide requires an active, earth-abundant electrocatalyst, as well as highly efficient mass transport. Here we report the design of a porous hollow fibre copper electrode with a compact three-dimensional geometry, which provides a large area,

  11. Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

    Science.gov (United States)

    Yao, Chen; Zhu, Xiaojin; Weigel, Kent A

    2016-11-07

    Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with

  12. Fractional exclusion and braid statistics in one dimension: a study via dimensional reduction of Chern-Simons theory

    Science.gov (United States)

    Ye, Fei; Marchetti, P. A.; Su, Z. B.; Yu, L.

    2017-09-01

    The relation between braid and exclusion statistics is examined in one-dimensional systems, within the framework of Chern-Simons statistical transmutation in gauge invariant form with an appropriate dimensional reduction. If the matter action is anomalous, as for chiral fermions, a relation between braid and exclusion statistics can be established explicitly for both mutual and nonmutual cases. However, if it is not anomalous, the exclusion statistics of emergent low energy excitations is not necessarily connected to the braid statistics of the physical charged fields of the system. Finally, we also discuss the bosonization of one-dimensional anyonic systems through T-duality. Dedicated to the memory of Mario Tonin.

  13. Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction.

    Science.gov (United States)

    Karayianni, Katerina N; Grimaldi, Keith A; Nikita, Konstantina S; Valavanis, Ioannis K

    2015-01-01

    This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.

  14. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-01-07

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  15. Application of semi-supervised deep learning to lung sound analysis.

    Science.gov (United States)

    Chamberlain, Daniel; Kodgule, Rahul; Ganelin, Daniela; Miglani, Vivek; Fletcher, Richard Ribon

    2016-08-01

    The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically Ndeep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.

  16. An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

    Science.gov (United States)

    Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

    2016-10-01

    Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R

    Directory of Open Access Journals (Sweden)

    Mark Culp

    2011-04-01

    Full Text Available In this paper, we present an R package that combines feature-based (X data and graph-based (G data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled and missing for the remainder (unlabeled. We examine an approach for fitting Y = Xβ + f(G where β is a coefficient vector and f is a function over the vertices of the graph. The procedure is semi-supervised in nature (trained on the labeled and unlabeled sets, requiring iterative algorithms for fitting this estimate. The package provides several key functions for fitting and evaluating an estimator of this type. The package is illustrated on a text analysis data set, where the observations are text documents (papers, the response is the category of paper (either applied or theoretical statistics, the X information is the name of the journal in which the paper resides, and the graph is a co-citation network, with each vertex an observation and each edge the number of times that the two papers cite a common paper. An application involving classification of protein location using a protein interaction graph and an application involving classification on a manifold with part of the feature data converted to a graph are also presented.

  18. Stochastic confinement and dimensional reduction. 1

    International Nuclear Information System (INIS)

    Ambjoern, J.; Olesen, P.; Peterson, C.

    1984-03-01

    By Monte Carlo calculations on a 16 4 lattice the authors investigate four dimensional SU(2) lattice guage theory with respect to the conjecture that at large distances this theory reduces approximately to two dimensional SU(2) lattice gauge theory. Good numerical evidence is found for this conjecture. As a by-product the SU(2) string tension is also measured and good agreement is found with scaling. The 'adjoint string tension' is also found to have a reasonable scaling behaviour. (Auth.)

  19. METHOD OF DIMENSIONALITY REDUCTION IN CONTACT MECHANICS AND FRICTION: A USERS HANDBOOK. I. AXIALLY-SYMMETRIC CONTACTS

    Directory of Open Access Journals (Sweden)

    Valentin L. Popov

    2014-04-01

    Full Text Available The Method of Dimensionality Reduction (MDR is a method of calculation and simulation of contacts of elastic and viscoelastic bodies. It consists essentially of two simple steps: (a substitution of the three-dimensional continuum by a uniquely defined one-dimensional linearly elastic or viscoelastic foundation (Winkler foundation and (b transformation of the three-dimensional profile of the contacting bodies by means of the MDR-transformation. As soon as these two steps are completed, the contact problem can be considered to be solved. For axial symmetric contacts, only a small calculation by hand is required which does not exceed elementary calculus and will not be a barrier for any practically-oriented engineer. Alternatively, the MDR can be implemented numerically, which is almost trivial due to the independence of the foundation elements. In spite of their simplicity, all the results are exact. The present paper is a short practical guide to the MDR.

  20. Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain-computer interfaces

    Science.gov (United States)

    Davoudi, Alireza; Shiry Ghidary, Saeed; Sadatnejad, Khadijeh

    2017-06-01

    Objective. In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. Approach. The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. Main results. We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique—leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. Significance. The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain-computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.

  1. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  2. Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

    Science.gov (United States)

    Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

    2014-01-01

    Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of

  3. N=2-Maxwell-Chern-Simons model with anomalous magnetic moment coupling via dimensional reduction

    International Nuclear Information System (INIS)

    Christiansen, H.R.; Cunha, M.S.; Helayel Neto, Jose A.; Manssur, L.R.U; Nogueira, A.L.M.A.

    1998-02-01

    An N=1-supersymmetric version of the Cremmer-Scherk-Kalb-Ramond model with non-minimal coupling to matter is built up both in terms of superfields and in a component field formalism. By adopting a dimensional reduction procedure, the N=2-D=3 counterpart of the model comes out, with two main features: a genuine (diagonal) Chern-Simons term and an anomalous magnetic moment coupling between matter and the gauge potential. (author)

  4. Transformative piezoelectric enhancement of P(VDF-TrFE) synergistically driven by nanoscale dimensional reduction and thermal treatment.

    Science.gov (United States)

    Ico, G; Myung, A; Kim, B S; Myung, N V; Nam, J

    2018-02-08

    Despite the significant potential of organic piezoelectric materials in the electro-mechanical or mechano-electrical applications that require light and flexible material properties, the intrinsically low piezoelectric performance as compared to traditional inorganic materials has limited their full utilization. In this study, we demonstrate that dimensional reduction of poly(vinylidene fluoride trifluoroethylene) (P(VDF-TrFE)) at the nanoscale by electrospinning, combined with an appropriate thermal treatment, induces a transformative enhancement in piezoelectric performance. Specifically, the piezoelectric coefficient (d 33 ) reached up to -108 pm V -1 , approaching that of inorganic counterparts. Electrospun mats composed of thermo-treated 30 nm nanofibers with a thickness of 15 μm produced a consistent peak-to-peak voltage of 38.5 V and a power output of 74.1 μW at a strain of 0.26% while sustaining energy production over 10k repeated actuations. The exceptional piezoelectric performance was realized by the enhancement of piezoelectric dipole alignment and the materialization of flexoelectricity, both from the synergistic effects of dimensional reduction and thermal treatment. Our findings suggest that dimensionally controlled and thermally treated electrospun P(VDF-TrFE) nanofibers provide an opportunity to exploit their flexibility and durability for mechanically challenging applications while matching the piezoelectric performance of brittle, inorganic piezoelectric materials.

  5. Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Francesco Di Maio

    2017-01-01

    Full Text Available Integrated Deterministic and Probabilistic Safety Analysis (IDPSA of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs, and Prime Implicants (PIs. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers, for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG.

  6. Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

    Science.gov (United States)

    Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

    2017-12-01

    Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

  7. Deep Learning @15 Petaflops/second: Semi-supervised pattern detection for 15 Terabytes of climate data

    Science.gov (United States)

    Collins, W. D.; Wehner, M. F.; Prabhat, M.; Kurth, T.; Satish, N.; Mitliagkas, I.; Zhang, J.; Racah, E.; Patwary, M.; Sundaram, N.; Dubey, P.

    2017-12-01

    Anthropogenically-forced climate changes in the number and character of extreme storms have the potential to significantly impact human and natural systems. Current high-performance computing enables multidecadal simulations with global climate models at resolutions of 25km or finer. Such high-resolution simulations are demonstrably superior in simulating extreme storms such as tropical cyclones than the coarser simulations available in the Coupled Model Intercomparison Project (CMIP5) and provide the capability to more credibly project future changes in extreme storm statistics and properties. The identification and tracking of storms in the voluminous model output is very challenging as it is impractical to manually identify storms due to the enormous size of the datasets, and therefore automated procedures are used. Traditionally, these procedures are based on a multi-variate set of physical conditions based on known properties of the class of storms in question. In recent years, we have successfully demonstrated that Deep Learning produces state of the art results for pattern detection in climate data. We have developed supervised and semi-supervised convolutional architectures for detecting and localizing tropical cyclones, extra-tropical cyclones and atmospheric rivers in simulation data. One of the primary challenges in the applicability of Deep Learning to climate data is in the expensive training phase. Typical networks may take days to converge on 10GB-sized datasets, while the climate science community has ready access to O(10 TB)-O(PB) sized datasets. In this work, we present the most scalable implementation of Deep Learning to date. We successfully scale a unified, semi-supervised convolutional architecture on all of the Cori Phase II supercomputer at NERSC. We use IntelCaffe, MKL and MLSL libraries. We have optimized single node MKL libraries to obtain 1-4 TF on single KNL nodes. We have developed a novel hybrid parameter update strategy to improve

  8. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  9. Features in chemical kinetics. I. Signatures of self-emerging dimensional reduction from a general format of the evolution law.

    Science.gov (United States)

    Nicolini, Paolo; Frezzato, Diego

    2013-06-21

    Simplification of chemical kinetics description through dimensional reduction is particularly important to achieve an accurate numerical treatment of complex reacting systems, especially when stiff kinetics are considered and a comprehensive picture of the evolving system is required. To this aim several tools have been proposed in the past decades, such as sensitivity analysis, lumping approaches, and exploitation of time scales separation. In addition, there are methods based on the existence of the so-called slow manifolds, which are hyper-surfaces of lower dimension than the one of the whole phase-space and in whose neighborhood the slow evolution occurs after an initial fast transient. On the other hand, all tools contain to some extent a degree of subjectivity which seems to be irremovable. With reference to macroscopic and spatially homogeneous reacting systems under isothermal conditions, in this work we shall adopt a phenomenological approach to let self-emerge the dimensional reduction from the mathematical structure of the evolution law. By transforming the original system of polynomial differential equations, which describes the chemical evolution, into a universal quadratic format, and making a direct inspection of the high-order time-derivatives of the new dynamic variables, we then formulate a conjecture which leads to the concept of an "attractiveness" region in the phase-space where a well-defined state-dependent rate function ω has the simple evolution ω[over dot]=-ω(2) along any trajectory up to the stationary state. This constitutes, by itself, a drastic dimensional reduction from a system of N-dimensional equations (being N the number of chemical species) to a one-dimensional and universal evolution law for such a characteristic rate. Step-by-step numerical inspections on model kinetic schemes are presented. In the companion paper [P. Nicolini and D. Frezzato, J. Chem. Phys. 138, 234102 (2013)] this outcome will be naturally related to the

  10. Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment.

    Science.gov (United States)

    Gao, Yang; Wang, Xuesong; Cheng, Yuhu; Wang, Z Jane

    2015-08-01

    To take full advantage of hyperspectral information, to avoid data redundancy and to address the curse of dimensionality concern, dimensionality reduction (DR) becomes particularly important to analyze hyperspectral data. Exploring the tensor characteristic of hyperspectral data, a DR algorithm based on class-aware tensor neighborhood graph and patch alignment is proposed here. First, hyperspectral data are represented in the tensor form through a window field to keep the spatial information of each pixel. Second, using a tensor distance criterion, a class-aware tensor neighborhood graph containing discriminating information is obtained. In the third step, employing the patch alignment framework extended to the tensor space, we can obtain global optimal spectral-spatial information. Finally, the solution of the tensor subspace is calculated using an iterative method and low-dimensional projection matrixes for hyperspectral data are obtained accordingly. The proposed method effectively explores the spectral and spatial information in hyperspectral data simultaneously. Experimental results on 3 real hyperspectral datasets show that, compared with some popular vector- and tensor-based DR algorithms, the proposed method can yield better performance with less tensor training samples required.

  11. Semi-Supervised Learning of Lift Optimization of Multi-Element Three-Segment Variable Camber Airfoil

    Science.gov (United States)

    Kaul, Upender K.; Nguyen, Nhan T.

    2017-01-01

    This chapter describes a new intelligent platform for learning optimal designs of morphing wings based on Variable Camber Continuous Trailing Edge Flaps (VCCTEF) in conjunction with a leading edge flap called the Variable Camber Krueger (VCK). The new platform consists of a Computational Fluid Dynamics (CFD) methodology coupled with a semi-supervised learning methodology. The CFD component of the intelligent platform comprises of a full Navier-Stokes solution capability (NASA OVERFLOW solver with Spalart-Allmaras turbulence model) that computes flow over a tri-element inboard NASA Generic Transport Model (GTM) wing section. Various VCCTEF/VCK settings and configurations were considered to explore optimal design for high-lift flight during take-off and landing. To determine globally optimal design of such a system, an extremely large set of CFD simulations is needed. This is not feasible to achieve in practice. To alleviate this problem, a recourse was taken to a semi-supervised learning (SSL) methodology, which is based on manifold regularization techniques. A reasonable space of CFD solutions was populated and then the SSL methodology was used to fit this manifold in its entirety, including the gaps in the manifold where there were no CFD solutions available. The SSL methodology in conjunction with an elastodynamic solver (FiDDLE) was demonstrated in an earlier study involving structural health monitoring. These CFD-SSL methodologies define the new intelligent platform that forms the basis for our search for optimal design of wings. Although the present platform can be used in various other design and operational problems in engineering, this chapter focuses on the high-lift study of the VCK-VCCTEF system. Top few candidate design configurations were identified by solving the CFD problem in a small subset of the design space. The SSL component was trained on the design space, and was then used in a predictive mode to populate a selected set of test points outside

  12. Stochastic confinement and dimensional reduction. Pt. 1

    International Nuclear Information System (INIS)

    Ambjoern, J.; Olesen, P.; Peterson, C.

    1984-01-01

    By Monte Carlo calculations on a 12 4 lattice we investigate four-dimensional SU(2) lattice gauge theory with respect to the conjecture that at large distances this theory reduces approximately to two-dimensional SU(2) lattice gauge theory. We find good numerical evidence for this conjecture. As a by-product we also measure the SU(2) string tension and find reasonable agreement with scaling. The 'adjoint string tension' is also found to have a reasonable scaling behaviour. (orig.)

  13. A Novel Medical Freehand Sketch 3D Model Retrieval Method by Dimensionality Reduction and Feature Vector Transformation

    Directory of Open Access Journals (Sweden)

    Zhang Jing

    2016-01-01

    Full Text Available To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR and feature vector transformation (FVT method. The DR method reduces the dimensionality of feature vector; only the top M low frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods.

  14. Water-Induced Dimensionality Reduction in Metal-Halide Perovskites

    KAUST Repository

    Turedi, Bekir; Lee, Kwangjae; Dursun, Ibrahim; Alamer, Badriah Jaber; Wu, Zhennan; Alarousu, Erkki; Mohammed, Omar F.; Cho, Namchul; Bakr, Osman

    2018-01-01

    . Here we employ water to directly transform films of the three-dimensional (3D) perovskite CsPbBr3 to stable two-dimensional (2D) perovskite-related CsPb2Br5. A sequential dissolution-recrystallization process governs this water induced transformation

  15. Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

    Directory of Open Access Journals (Sweden)

    Shi He

    2015-01-01

    Full Text Available A Bayesian hierarchical model is presented to classify very high resolution (VHR images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.

  16. Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins

    Directory of Open Access Journals (Sweden)

    Yanqi Hao

    2015-07-01

    Full Text Available Alternative splicing acts on transcripts from almost all human multi-exon genes. Notwithstanding its ubiquity, fundamental ramifications of splicing on protein expression remain unresolved. The number and identity of spliced transcripts that form stably folded proteins remain the sources of considerable debate, due largely to low coverage of experimental methods and the resulting absence of negative data. We circumvent this issue by developing a semi-supervised learning algorithm, positive unlabeled learning for splicing elucidation (PULSE; http://www.kimlab.org/software/pulse, which uses 48 features spanning various categories. We validated its accuracy on sets of bona fide protein isoforms and directly on mass spectrometry (MS spectra for an overall AU-ROC of 0.85. We predict that around 32% of “exon skipping” alternative splicing events produce stable proteins, suggesting that the process engenders a significant number of previously uncharacterized proteins. We also provide insights into the distribution of positive isoforms in various functional classes and into the structural effects of alternative splicing.

  17. Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

    Directory of Open Access Journals (Sweden)

    Dai Hongying

    2013-01-01

    Full Text Available Abstract Background Multifactor Dimensionality Reduction (MDR has been widely applied to detect gene-gene (GxG interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account. Results We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy. Conclusions The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi are proposed to detect multiple GxG interactions.

  18. Tracking mobile users in wireless networks via semi-supervised colocalization.

    Science.gov (United States)

    Pan, Jeffrey Junfeng; Pan, Sinno Jialin; Yin, Jie; Ni, Lionel M; Yang, Qiang

    2012-03-01

    Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.

  19. Multichannel transfer function with dimensionality reduction

    KAUST Repository

    Kim, Han Suk; Schulze, Jü rgen P.; Cone, Angela C.; Sosinsky, Gina E.; Martone, Maryann E.

    2010-01-01

    . Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum

  20. Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

    Science.gov (United States)

    Solouki, Saeed; Pooyan, Mohammad

    2016-06-01

    Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

  1. Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control.

    Science.gov (United States)

    Sponberg, Simon; Daniel, Thomas L; Fairhall, Adrienne L

    2015-04-01

    What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies)? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke) the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS) to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional

  2. Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control.

    Directory of Open Access Journals (Sweden)

    Simon Sponberg

    2015-04-01

    Full Text Available What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in

  3. Dual Dimensionality Reduction Reveals Independent Encoding of Motor Features in a Muscle Synergy for Insect Flight Control

    Science.gov (United States)

    Sponberg, Simon; Daniel, Thomas L.; Fairhall, Adrienne L.

    2015-01-01

    What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies)? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke) the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS) to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional

  4. A semi-supervised classification algorithm using the TAD-derived background as training data

    Science.gov (United States)

    Fan, Lei; Ambeau, Brittany; Messinger, David W.

    2013-05-01

    In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

  5. An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

    Science.gov (United States)

    Kundu, Kousik; Backofen, Rolf

    2017-01-01

    Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

  6. Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

    Institute of Scientific and Technical Information of China (English)

    Tian Dongping

    2017-01-01

    In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.

  7. Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

    Directory of Open Access Journals (Sweden)

    Ernestina Martel

    2018-06-01

    Full Text Available Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA, suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.

  8. Recursions of Symmetry Orbits and Reduction without Reduction

    Directory of Open Access Journals (Sweden)

    Andrei A. Malykh

    2011-04-01

    Full Text Available We consider a four-dimensional PDE possessing partner symmetries mainly on the example of complex Monge-Ampère equation (CMA. We use simultaneously two pairs of symmetries related by a recursion relation, which are mutually complex conjugate for CMA. For both pairs of partner symmetries, using Lie equations, we introduce explicitly group parameters as additional variables, replacing symmetry characteristics and their complex conjugates by derivatives of the unknown with respect to group parameters. We study the resulting system of six equations in the eight-dimensional space, that includes CMA, four equations of the recursion between partner symmetries and one integrability condition of this system. We use point symmetries of this extended system for performing its symmetry reduction with respect to group parameters that facilitates solving the extended system. This procedure does not imply a reduction in the number of physical variables and hence we end up with orbits of non-invariant solutions of CMA, generated by one partner symmetry, not used in the reduction. These solutions are determined by six linear equations with constant coefficients in the five-dimensional space which are obtained by a three-dimensional Legendre transformation of the reduced extended system. We present algebraic and exponential examples of such solutions that govern Legendre-transformed Ricci-flat Kähler metrics with no Killing vectors. A similar procedure is briefly outlined for Husain equation.

  9. Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Ma, Jian Ping; Jiang, Jin

    2014-01-01

    Pattern classification methods have become important tools for fault diagnosis in industrial systems. However, it is normally difficult to obtain reliable labeled data to train a supervised pattern classification model for applications in a nuclear power plant (NPP). However, unlabeled data easily become available through increased deployment of supervisory, control, and data acquisition (SCADA) systems. In this paper, a fault diagnosis scheme based on semi-supervised classification (SSC) method is developed with specific applications for NPP. In this scheme, newly measured plant data are treated as unlabeled data. They are integrated with selected labeled data to train a SSC model which is then used to estimate labels of the new data. Compared to exclusive supervised approaches, the proposed scheme requires significantly less number of labeled data to train a classifier. Furthermore, it is shown that higher degree of uncertainties in the labeled data can be tolerated. The developed scheme has been validated using the data generated from a desktop NPP simulator and also from a physical NPP simulator using a graph-based SSC algorithm. Two case studies have been used in the validation process. In the first case study, three faults have been simulated on the desktop simulator. These faults have all been classified successfully with only four labeled data points per fault case. In the second case, six types of fault are simulated on the physical NPP simulator. All faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis

  10. Point based interactive image segmentation using multiquadrics splines

    Science.gov (United States)

    Meena, Sachin; Duraisamy, Prakash; Palniappan, Kannappan; Seetharaman, Guna

    2017-05-01

    Multiquadrics (MQ) are radial basis spline function that can provide an efficient interpolation of data points located in a high dimensional space. MQ were developed by Hardy to approximate geographical surfaces and terrain modelling. In this paper we frame the task of interactive image segmentation as a semi-supervised interpolation where an interpolating function learned from the user provided seed points is used to predict the labels of unlabeled pixel and the spline function used in the semi-supervised interpolation is MQ. This semi-supervised interpolation framework has a nice closed form solution which along with the fact that MQ is a radial basis spline function lead to a very fast interactive image segmentation process. Quantitative and qualitative results on the standard datasets show that MQ outperforms other regression based methods, GEBS, Ridge Regression and Logistic Regression, and popular methods like Graph Cut,4 Random Walk and Random Forest.6

  11. Three-dimensional iron, nitrogen-doped carbon foams as efficient electrocatalysts for oxygen reduction reaction in alkaline solution

    International Nuclear Information System (INIS)

    Ma, Yanjiao; Wang, Hui; Feng, Hanqing; Ji, Shan; Mao, Xuefeng; Wang, Rongfang

    2014-01-01

    Graphical abstract: Three-dimentional Fe, N-doped carbon foams prepared by two steps exhibited comparable catalytic activity for oxygen reduction reaction to commercial Pt/C due to the unique structure and the synergistic effect of Fe and N atoms. - Highlights: • Three-dimensional Fe, N-doped carbon foam (3D-CF) were prepared. • 3D-CF exhibits comparable catalytic activity to Pt/C for oxygen reduction reaction. • The enhanced activity of 3D-CF results of its unique structure. - Abstract: Three-dimensional (3D) Fe, N-doped carbon foams (3D-CF) as efficient cathode catalysts for the oxygen reduction reaction (ORR) in alkaline solution are reported. The 3D-CF exhibit interconnected hierarchical pore structure. In addition, Fe, N-doped carbon without porous strucuture (Fe-N-C) and 3D N-doped carbon without Fe (3D-CF’) are prepared to verify the electrocatalytic activity of 3D-CF. The electrocatalytic performance of as-prepared 3D-CF for ORR shows that the onset potential on 3D-CF electrode positively shifts about 41 mV than those of 3D-CF’ and Fe-N-C respectively. In addition, the onset potential on 3D-CF electrode for ORR is about 27 mV more negative than that on commercial Pt/C electrode. 3D-CF also show better methanol tolerance and durability than commercial Pt/C catalyst. These results show that to synthesize 3D hierarchical pores with high specific surface area is an efficient way to improve the ORR performance

  12. Machine Learning Based Dimensionality Reduction Facilitates Ligand Diffusion Paths Assessment: A Case of Cytochrome P450cam.

    Science.gov (United States)

    Rydzewski, J; Nowak, W

    2016-04-12

    In this work we propose an application of a nonlinear dimensionality reduction method to represent the high-dimensional configuration space of the ligand-protein dissociation process in a manner facilitating interpretation. Rugged ligand expulsion paths are mapped into 2-dimensional space. The mapping retains the main structural changes occurring during the dissociation. The topological similarity of the reduced paths may be easily studied using the Fréchet distances, and we show that this measure facilitates machine learning classification of the diffusion pathways. Further, low-dimensional configuration space allows for identification of residues active in transport during the ligand diffusion from a protein. The utility of this approach is illustrated by examination of the configuration space of cytochrome P450cam involved in expulsing camphor by means of enhanced all-atom molecular dynamics simulations. The expulsion trajectories are sampled and constructed on-the-fly during molecular dynamics simulations using the recently developed memetic algorithms [ Rydzewski, J.; Nowak, W. J. Chem. Phys. 2015 , 143 ( 12 ), 124101 ]. We show that the memetic algorithms are effective for enforcing the ligand diffusion and cavity exploration in the P450cam-camphor complex. Furthermore, we demonstrate that machine learning techniques are helpful in inspecting ligand diffusion landscapes and provide useful tools to examine structural changes accompanying rare events.

  13. Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system

    International Nuclear Information System (INIS)

    Fang, Guochang; Tian, Lixin; Sun, Mei; Fu, Min

    2012-01-01

    A novel three-dimensional energy-saving and emission-reduction chaotic system is proposed, which has not yet been reported in present literature. The system is established in accordance with the complicated relationship between energy-saving and emission-reduction, carbon emissions and economic growth. The dynamic behavior of the system is analyzed by means of Lyapunov exponents and bifurcation diagrams. With undetermined coefficient method, expressions of homoclinic orbits of the system are obtained. The Šilnikov theorem guarantees that the system has Smale horseshoes and the horseshoes chaos. Artificial neural network (ANN) is used to identify the quantitative coefficients in the simulation models according to the statistical data of China, and an empirical study of the real system is carried out with the results in perfect agreement with actual situation. It is found that the sooner and more perfect energy-saving and emission-reduction is started, the easier and sooner the maximum of the carbon emissions will be achieved so as to reduce carbon emissions and energy intensity. Numerical simulations are presented to demonstrate the results. -- Highlights: ► Use non-linear dynamical method to model the energy-saving and emission-reduction system. ► The energy-saving and emission-reduction attractor is obtained. ► Identify the unknown parameters of the energy-saving and emission-reduction system based on the statistical data. ► Evaluating the achievements of energy-saving and emission-reduction by the time-varying energy intensity calculation formula. ► Some statistical results based on the statistical data in China are presented, which are vivid and adherent to the reality.

  14. Hidden symmetries in five-dimensional supergravity

    International Nuclear Information System (INIS)

    Poessel, M.

    2003-05-01

    This thesis is concerned with the study of hidden symmetries in supergravity, which play an important role in the present picture of supergravity and string theory. Concretely, the appearance of a hidden G 2(+2) /SO(4) symmetry is studied in the dimensional reduction of d=5, N=2 supergravity to three dimensions - a parallel model to the more famous E 8(+8) /SO(16) case in eleven-dimensional supergravity. Extending previous partial results for the bosonic part, I give a derivation that includes fermionic terms. This sheds new light on the appearance of the local hidden symmetry SO(4) in the reduction, and shows up an unusual feature which follows from an analysis of the R-symmetry associated with N=4 supergravity and of the supersymmetry variations, and which has no parallel in the eleven-dimensional case: The emergence of an additional SO(3) as part of the enhanced local symmetry, invisible in the dimensional reduction of the gravitino, and corresponding to the fact that, of the SO(4) used in the coset model, only the diagonal SO(3) is visible immediately upon dimensional reduction. The uncovering of the hidden symmetries proceeds via the construction of the proper coset gravity in three dimensions, and matching it with the Lagrangian obtained from the reduction. (orig.)

  15. Effect of Intraoperative Three-Dimensional Imaging During the Reduction and Fixation of Displaced Calcaneal Fractures on Articular Congruence and Implant Fixation

    DEFF Research Database (Denmark)

    Eckardt, Henrik; Lind, Marianne

    2015-01-01

    BACKGROUND: Operative treatment of displaced calcaneal fractures should restore joint congruence, but conventional fluoroscopy is unable to fully visualize the subtalar joint. We questioned whether intraoperative 3-dimensional (3D) imaging would aid in the reduction of calcaneal fractures......, resulting in improved articular congruence and implant positioning. METHOD: Sixty-two displaced calcaneal fractures were operated on using standard fluoroscopic views. When the surgeon had achieved a satisfactory reduction, an intraoperative 3D scan was conducted, malreductions or implant imperfections were...

  16. A Comparative Study of 3-Dimensional Titanium Versus 2-Dimensional Titanium Miniplates for Open Reduction and Fixation of Mandibular Parasymphysis Fracture.

    Science.gov (United States)

    Mittal, Yogesh; Varghese, K George; Mohan, S; Jayakumar, N; Chhag, Somil

    2016-03-01

    Three dimensional titanium plating system was developed by Farmand in 1995 to meet the requirements of semi rigid fixation with lesser complication. The purpose of this in vivo prospective study was to evaluate and compare the clinical effectiveness of three dimensional and two dimensional Titanium miniplates for open reduction and fixation of mandibular parasymphysis fracture. Thirty patients with non-comminuted mandibular parasymphysis fractures were divided randomly into two equal groups and were treated with 2 mm 3D and 2D miniplate system respectively. All patients were systematically monitored at 1st, 2nd, 3rd, 6th week, 3rd and 6th month postoperatively. The outcome parameters recorded were severity of pain, infection, mobility, occlusion derangement, paresthesia and implant failure. The data so collected was analyzed using independent t test and Chi square test (α = .05). The results showed that one patient in each group had post-operative infection, occlusion derangement and mobility (p > .05). In Group A, one patient had paresthesia while in Group B, two patients had paresthesia (p > .05). None of the patients in both the groups had implant failure. There was no statistically significant difference between 3D and 2D miniplate system in all the recorded parameters at all the follow-ups (p > .05). 3D miniplates were found to be better than 2D miniplates in terms of cost, ease of surgery and operative time. However, 3D miniplates were unfavorable for cases where fracture line was oblique and in close proximity to mental foramen, where they were difficult to adapt and more chances for tooth-root damage and inadvertent injury to the mental nerve due to traction.

  17. M-Isomap: Orthogonal Constrained Marginal Isomap for Nonlinear Dimensionality Reduction.

    Science.gov (United States)

    Zhang, Zhao; Chow, Tommy W S; Zhao, Mingbo

    2013-02-01

    Isomap is a well-known nonlinear dimensionality reduction (DR) method, aiming at preserving geodesic distances of all similarity pairs for delivering highly nonlinear manifolds. Isomap is efficient in visualizing synthetic data sets, but it usually delivers unsatisfactory results in benchmark cases. This paper incorporates the pairwise constraints into Isomap and proposes a marginal Isomap (M-Isomap) for manifold learning. The pairwise Cannot-Link and Must-Link constraints are used to specify the types of neighborhoods. M-Isomap computes the shortest path distances over constrained neighborhood graphs and guides the nonlinear DR through separating the interclass neighbors. As a result, large margins between both interand intraclass clusters are delivered and enhanced compactness of intracluster points is achieved at the same time. The validity of M-Isomap is examined by extensive simulations over synthetic, University of California, Irvine, and benchmark real Olivetti Research Library, YALE, and CMU Pose, Illumination, and Expression databases. The data visualization and clustering power of M-Isomap are compared with those of six related DR methods. The visualization results show that M-Isomap is able to deliver more separate clusters. Clustering evaluations also demonstrate that M-Isomap delivers comparable or even better results than some state-of-the-art DR algorithms.

  18. A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

    OpenAIRE

    Gui, Jiang; Moore, Jason H.; Williams, Scott M.; Andrews, Peter; Hillege, Hans L.; van der Harst, Pim; Navis, Gerjan; Van Gilst, Wiek H.; Asselbergs, Folkert W.; Gilbert-Diamond, Diane

    2013-01-01

    We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR's constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to ide...

  19. Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

    Directory of Open Access Journals (Sweden)

    Liye Zhang

    2017-04-01

    Full Text Available Indoor positioning based on the received signal strength (RSS of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

  20. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

    Science.gov (United States)

    Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

    2017-07-01

    Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Water-Induced Dimensionality Reduction in Metal-Halide Perovskites

    KAUST Repository

    Turedi, Bekir

    2018-03-30

    Metal-halide perovskite materials are highly attractive materials for optoelectronic applications. However, the instability of perovskite materials caused by moisture and heat-induced degradation impairs future prospects of using these materials. Here we employ water to directly transform films of the three-dimensional (3D) perovskite CsPbBr3 to stable two-dimensional (2D) perovskite-related CsPb2Br5. A sequential dissolution-recrystallization process governs this water induced transformation under PbBr2 rich condition. We find that these post-synthesized 2D perovskite-related material films exhibit excellent stability against humidity and high photoluminescence quantum yield. We believe that our results provide a new synthetic method to generate stable 2D perovskite-related materials that could be applicable for light emitting device applications.

  2. Feature extraction with deep neural networks by a generalized discriminant analysis.

    Science.gov (United States)

    Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

    2012-04-01

    We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

  3. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  4. Dimensionality Reduction and Information-Theoretic Divergence Between Sets of Ladar Images

    National Research Council Canada - National Science Library

    Gray, David M; Principe, Jose C

    2008-01-01

    ... can be exploited while circumventing many of the problems associated with the so-called "curse of dimensionality." In this study, PCA techniques are used to find a low-dimensional sub-space representation of LADAR image sets...

  5. Dimensional reduction near the deconfinement transition

    International Nuclear Information System (INIS)

    Kurkela, A.

    2009-01-01

    It is expected that incorporating the center symmetry in the conventional dimensionally reduced effective theory for high-temperature SU(N) Yang-Mills theory, EQCD, will considerably extend its applicability towards the deconfinement transition. In this talk, I will discuss the construction of such center-symmetric effective theories and present results from their lattice simulations in the case of two colors. The simulations demonstrate that unlike EQCD, the new center symmetric theory undergoes a second order confining phase transition in complete analogy with the full theory. I will also describe the perturbative and non-perturbative matching of the parameters of the effective theory, and outline ways to further improve its description of the physics near the deconfinement transition. (author)

  6. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals.

    Directory of Open Access Journals (Sweden)

    Tom Cattaert

    Full Text Available We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR with Model-Based MDR (MB-MDR. We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR, which is a generalization of Multifactor Dimensionality Reduction (MDR to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC, an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS. This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

  7. Higher-dimensional Bianchi type-VIh cosmologies

    Science.gov (United States)

    Lorenz-Petzold, D.

    1985-09-01

    The higher-dimensional perfect fluid equations of a generalization of the (1 + 3)-dimensional Bianchi type-VIh space-time are discussed. Bianchi type-V and Bianchi type-III space-times are also included as special cases. It is shown that the Chodos-Detweiler (1980) mechanism of cosmological dimensional-reduction is possible in these cases.

  8. Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

    Science.gov (United States)

    Fatehi, Moslem; Asadi, Hooshang H.

    2017-04-01

    In this study, the application of a transductive support vector machine (TSVM), an innovative semi-supervised learning algorithm, has been proposed for mapping the potential drill targets at a detailed exploration stage. The semi-supervised learning method is a hybrid of supervised and unsupervised learning approach that simultaneously uses both training and non-training data to design a classifier. By using the TSVM algorithm, exploration layers at the Dalli porphyry Cu-Au deposit in the central Iran were integrated to locate the boundary of the Cu-Au mineralization for further drilling. By applying this algorithm on the non-training (unlabeled) and limited training (labeled) Dalli exploration data, the study area was classified in two domains of Cu-Au ore and waste. Then, the results were validated by the earlier block models created, using the available borehole and trench data. In addition to TSVM, the support vector machine (SVM) algorithm was also implemented on the study area for comparison. Thirty percent of the labeled exploration data was used to evaluate the performance of these two algorithms. The results revealed 87 percent correct recognition accuracy for the TSVM algorithm and 82 percent for the SVM algorithm. The deepest inclined borehole, recently drilled in the western part of the Dalli deposit, indicated that the boundary of Cu-Au mineralization, as identified by the TSVM algorithm, was only 15 m off from the actual boundary intersected by this borehole. According to the results of the TSVM algorithm, six new boreholes were suggested for further drilling at the Dalli deposit. This study showed that the TSVM algorithm could be a useful tool for enhancing the mineralization zones and consequently, ensuring a more accurate drill hole planning.

  9. A Corresponding Lie Algebra of a Reductive homogeneous Group and Its Applications

    International Nuclear Information System (INIS)

    Zhang Yu-Feng; Rui Wen-Juan; Wu Li-Xin

    2015-01-01

    With the help of a Lie algebra of a reductive homogeneous space G/K, where G is a Lie group and K is a resulting isotropy group, we introduce a Lax pair for which an expanding (2+1)-dimensional integrable hierarchy is obtained by applying the binormial-residue representation (BRR) method, whose Hamiltonian structure is derived from the trace identity for deducing (2+1)-dimensional integrable hierarchies, which was proposed by Tu, et al. We further consider some reductions of the expanding integrable hierarchy obtained in the paper. The first reduction is just right the (2+1)-dimensional AKNS hierarchy, the second-type reduction reveals an integrable coupling of the (2+1)-dimensional AKNS equation (also called the Davey-Stewartson hierarchy), a kind of (2+1)-dimensional Schrödinger equation, which was once reobtained by Tu, Feng and Zhang. It is interesting that a new (2+1)-dimensional integrable nonlinear coupled equation is generated from the reduction of the part of the (2+1)-dimensional integrable coupling, which is further reduced to the standard (2+1)-dimensional diffusion equation along with a parameter. In addition, the well-known (1+1)-dimensional AKNS hierarchy, the (1+1)-dimensional nonlinear Schrödinger equation are all special cases of the (2+1)-dimensional expanding integrable hierarchy. Finally, we discuss a few discrete difference equations of the diffusion equation whose stabilities are analyzed by making use of the von Neumann condition and the Fourier method. Some numerical solutions of a special stationary initial value problem of the (2+1)-dimensional diffusion equation are obtained and the resulting convergence and estimation formula are investigated. (paper)

  10. A semi-supervised method to detect seismic random noise with fuzzy GK clustering

    International Nuclear Information System (INIS)

    Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

    2008-01-01

    We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

  11. Three-dimensional MRI Analysis of Femoral Head Remodeling After Reduction in Patients With Developmental Dysplasia of the Hip.

    Science.gov (United States)

    Tsukagoshi, Yuta; Kamada, Hiroshi; Kamegaya, Makoto; Takeuchi, Ryoko; Nakagawa, Shogo; Tomaru, Yohei; Tanaka, Kenta; Onishi, Mio; Nishino, Tomofumi; Yamazaki, Masashi

    2018-05-02

    Previous reports on patients with developmental dysplasia of the hip (DDH) showed that the prereduced femoral head was notably smaller and more nonspherical than the intact head, with growth failure observed at the proximal posteromedial area. We evaluated the shape of the femoral head cartilage in patients with DDH before and after reduction, with size and sphericity assessed using 3-dimensional (3D) magnetic resonance imaging (MRI). We studied 10 patients with unilateral DDH (all female) who underwent closed reduction. Patients with avascular necrosis of the femoral head on the plain radiograph 1 year after reduction were excluded. 3D MRI was performed before reduction and after reduction, at 2 years of age. 3D-image analysis software was used to reconstruct the multiplanes. After setting the axial, coronal, and sagittal planes in the software (based on the femoral shaft and neck axes), the smallest sphere that included the femoral head cartilage was drawn, the diameter was measured, and the center of the sphere was defined as the femoral head center. We measured the distance between the center and cartilage surface every 30 degrees on the 3 reconstructed planes. Sphericity of the femoral head was calculated using a ratio (the distance divided by each radius) and compared between prereduction and postreduction. The mean patient age was 7±3 and 26±3 months at the first and second MRI, respectively. The mean duration between the reduction and second MRI was 18±3 months. The femoral head diameter was 26.7±1.5 and 26.0±1.6 mm on the diseased and intact sides, respectively (P=0.069). The ratios of the posteromedial area on the axial plane and the proximoposterior area on the sagittal plane after reduction were significantly larger than before reduction (P<0.01). We demonstrated that the size of the reduced femoral head was nearly equal to that of the intact femoral head and that the growth failure area of the head before reduction, in the proximal posteromedial

  12. Four-dimensional Hall mechanics as a particle on CP3

    International Nuclear Information System (INIS)

    Bellucci, Stefano; Casteill, Pierre-Yves; Nersessian, Armen

    2003-01-01

    In order to establish an explicit connection between four-dimensional Hall effect on S 4 and six-dimensional Hall effect on CP 3 , we perform the Hamiltonian reduction of a particle moving on CP 3 in a constant magnetic field to the four-dimensional Hall mechanics (i.e., a-bar particle on S 4 in a SU(2) instanton field). This reduction corresponds to fixing the isospin of the latter system

  13. Kaluza-Klein cosmology from five-dimensional Lovelock-Cartan theory

    Science.gov (United States)

    Castillo-Felisola, Oscar; Corral, Cristóbal; del Pino, Simón; Ramírez, Francisca

    2016-12-01

    We study the Kaluza-Klein dimensional reduction of the Lovelock-Cartan theory in five-dimensional spacetime, with a compact dimension of S1 topology. We find cosmological solutions of the Friedmann-Robertson-Walker class in the reduced spacetime. The torsion and the fields arising from the dimensional reduction induce a nonvanishing energy-momentum tensor in four dimensions. We find solutions describing expanding, contracting, and bouncing universes. The model shows a dynamical compactification of the extra dimension in some regions of the parameter space.

  14. Dimensional reduction of exceptional E6,E8 gauge groups and flavour chirality

    International Nuclear Information System (INIS)

    Koca, M.

    1984-01-01

    Ten-dimensional Yang - Mills gauge theories based on the exceptional groups E 6 and E 8 are reduced to four-dimensional flavour-chiral Yang - Mills - Higgs theories where the extra six dimensions are identified with the compact G 2 /SU(3) and SO(7)/SO(6) coset spaces. A ten-dimensional E 8 theory leads to three families of SU(5), one of which lies in the 144-dimensional representation of SO(10)

  15. Dimensional reduction of a general advection–diffusion equation in 2D channels

    Science.gov (United States)

    Kalinay, Pavol; Slanina, František

    2018-06-01

    Diffusion of point-like particles in a two-dimensional channel of varying width is studied. The particles are driven by an arbitrary space dependent force. We construct a general recurrence procedure mapping the corresponding two-dimensional advection-diffusion equation onto the longitudinal coordinate x. Unlike the previous specific cases, the presented procedure enables us to find the one-dimensional description of the confined diffusion even for non-conservative (vortex) forces, e.g. caused by flowing solvent dragging the particles. We show that the result is again the generalized Fick–Jacobs equation. Despite of non existing scalar potential in the case of vortex forces, the effective one-dimensional scalar potential, as well as the corresponding quasi-equilibrium and the effective diffusion coefficient can be always found.

  16. Three-dimensional assemblies of graphene prepared by a novel chemical reduction-induced self-assembly method

    KAUST Repository

    Zhang, Lianbin

    2012-01-01

    In this study, three-dimensional (3D) graphene assemblies are prepared from graphene oxide (GO) by a facile in situ reduction-assembly method, using a novel, low-cost, and environment-friendly reducing medium which is a combination of oxalic acid (OA) and sodium iodide (NaI). It is demonstrated that the combination of a reducing acid, OA, and NaI is indispensable for effective reduction of GO in the current study and this unique combination (1) allows for tunable control over the volume of the thus-prepared graphene assemblies and (2) enables 3D graphene assemblies to be prepared from the GO suspension with a wide range of concentrations (0.1 to 4.5 mg mL-1). To the best of our knowledge, the GO concentration of 0.1 mg mL-1 is the lowest GO concentration ever reported for preparation of 3D graphene assemblies. The thus-prepared 3D graphene assemblies exhibit low density, highly porous structures, and electrically conducting properties. As a proof of concept, we show that by infiltrating a responsive polymer of polydimethylsiloxane (PDMS) into the as-resulted 3D conducting network of graphene, a conducting composite is obtained, which can be used as a sensing device for differentiating organic solvents with different polarity. © 2012 The Royal Society of Chemistry.

  17. Supersymmetric dimensional regularization

    International Nuclear Information System (INIS)

    Siegel, W.; Townsend, P.K.; van Nieuwenhuizen, P.

    1980-01-01

    There is a simple modification of dimension regularization which preserves supersymmetry: dimensional reduction to real D < 4, followed by analytic continuation to complex D. In terms of component fields, this means fixing the ranges of all indices on the fields (and therefore the numbers of Fermi and Bose components). For superfields, it means continuing in the dimensionality of x-space while fixing the dimensionality of theta-space. This regularization procedure allows the simple manipulation of spinor derivatives in supergraph calculations. The resulting rules are: (1) First do all algebra exactly as in D = 4; (2) Then do the momentum integrals as in ordinary dimensional regularization. This regularization procedure needs extra rules before one can say that it is consistent. Such extra rules needed for superconformal anomalies are discussed. Problems associated with renormalizability and higher order loops are also discussed

  18. New results for algebraic tensor reduction of Feynman integrals

    Energy Technology Data Exchange (ETDEWEB)

    Fleischer, Jochem [Bielefeld Univ. (Germany). Fakultaet fuer Physik; Riemann, Tord [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Yundin, Valery [Copenhagen Univ. (Denmark). Niels Bohr International Academy and Discovery Center

    2012-02-15

    We report on some recent developments in algebraic tensor reduction of one-loop Feynman integrals. For 5-point functions, an efficient tensor reduction was worked out recently and is now available as numerical C++ package, PJFry, covering tensor ranks until five. It is free of inverse 5- point Gram determinants and inverse small 4-point Gram determinants are treated by expansions in higher-dimensional 3-point functions. By exploiting sums over signed minors, weighted with scalar products of chords (or, equivalently, external momenta), extremely efficient expressions for tensor integrals contracted with external momenta were derived. The evaluation of 7-point functions is discussed. In the present approach one needs for the reductions a (d +2)-dimensional scalar 5-point function in addition to the usual scalar basis of 1- to 4-point functions in the generic dimension d=4-2{epsilon}. When exploiting the four-dimensionality of the kinematics, this basis is sufficient. We indicate how the (d+2)-dimensional 5-point function can be evaluated. (orig.)

  19. New results for algebraic tensor reduction of Feynman integrals

    International Nuclear Information System (INIS)

    Fleischer, Jochem; Yundin, Valery

    2012-02-01

    We report on some recent developments in algebraic tensor reduction of one-loop Feynman integrals. For 5-point functions, an efficient tensor reduction was worked out recently and is now available as numerical C++ package, PJFry, covering tensor ranks until five. It is free of inverse 5- point Gram determinants and inverse small 4-point Gram determinants are treated by expansions in higher-dimensional 3-point functions. By exploiting sums over signed minors, weighted with scalar products of chords (or, equivalently, external momenta), extremely efficient expressions for tensor integrals contracted with external momenta were derived. The evaluation of 7-point functions is discussed. In the present approach one needs for the reductions a (d +2)-dimensional scalar 5-point function in addition to the usual scalar basis of 1- to 4-point functions in the generic dimension d=4-2ε. When exploiting the four-dimensionality of the kinematics, this basis is sufficient. We indicate how the (d+2)-dimensional 5-point function can be evaluated. (orig.)

  20. Extended supersymmetry in four-dimensional Euclidean space

    International Nuclear Information System (INIS)

    McKeon, D.G.C.; Sherry, T.N.

    2000-01-01

    Since the generators of the two SU(2) groups which comprise SO(4) are not Hermitian conjugates of each other, the simplest supersymmetry algebra in four-dimensional Euclidean space more closely resembles the N=2 than the N=1 supersymmetry algebra in four-dimensional Minkowski space. An extended supersymmetry algebra in four-dimensional Euclidean space is considered in this paper; its structure resembles that of N=4 supersymmetry in four-dimensional Minkowski space. The relationship of this algebra to the algebra found by dimensionally reducing the N=1 supersymmetry algebra in ten-dimensional Euclidean space to four-dimensional Euclidean space is examined. The dimensional reduction of N=1 super Yang-Mills theory in ten-dimensional Minkowski space to four-dimensional Euclidean space is also considered

  1. Semi-Supervised Clustering for High-Dimensional and Sparse Features

    Science.gov (United States)

    Yan, Su

    2010-01-01

    Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

  2. Hamiltonian reduction and supersymmetric mechanics with Dirac monopole

    International Nuclear Information System (INIS)

    Bellucci, Stefano; Nersessian, Armen; Yeranyan, Armen

    2006-01-01

    We apply the technique of Hamiltonian reduction for the construction of three-dimensional N=4 supersymmetric mechanics specified by the presence of a Dirac monopole. For this purpose we take the conventional N=4 supersymmetric mechanics on the four-dimensional conformally-flat spaces and perform its Hamiltonian reduction to three-dimensional system. We formulate the final system in the canonical coordinates, and present, in these terms, the explicit expressions of the Hamiltonian and supercharges. We show that, besides a magnetic monopole field, the resulting system is specified by the presence of a spin-orbit coupling term. A comparision with previous work is also carried out

  3. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Bin Hou

    2016-08-01

    Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  4. Unlocking the Electrocatalytic Activity of Antimony for CO2 Reduction by Two-Dimensional Engineering of the Bulk Material.

    Science.gov (United States)

    Li, Fengwang; Xue, Mianqi; Li, Jiezhen; Ma, Xinlei; Chen, Lu; Zhang, Xueji; MacFarlane, Douglas R; Zhang, Jie

    2017-11-13

    Two-dimensional (2D) materials are known to be useful in catalysis. Engineering 3D bulk materials into the 2D form can enhance the exposure of the active edge sites, which are believed to be the origin of the high catalytic activity. Reported herein is the production of 2D "few-layer" antimony (Sb) nanosheets by cathodic exfoliation. Application of this 2D engineering method turns Sb, an inactive material for CO 2 reduction in its bulk form, into an active 2D electrocatalyst for reduction of CO 2 to formate with high efficiency. The high activity is attributed to the exposure of a large number of catalytically active edge sites. Moreover, this cathodic exfoliation process can be coupled with the anodic exfoliation of graphite in a single-compartment cell for in situ production of a few-layer Sb nanosheets and graphene composite. The observed increased activity of this composite is attributed to the strong electronic interaction between graphene and Sb. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Dimensional reduction of U(1) x SU(2) Chern-Simons bosonization: Application to the t - J model

    International Nuclear Information System (INIS)

    Marchetti, P.A.

    1996-09-01

    We perform a dimensional reduction of the U(1) x SU(2) Chern-Simons bosonization and apply it to the t - J model, relevant for high T c superconductors. This procedure yields a decomposition of the electron field into a product of two ''semionic'' fields, i.e. fields obeying Abelian braid statistics with statistics parameter θ = 1/4, one carrying the charge and the other the spin degrees of freedom. A mean field theory is then shown to reproduce correctly the large distance behaviour of the correlation functions of the 1D t - J model at >> J. This result shows that to capture the essential physical properties of the model one needs a specific ''semionic'' form of spin-charge separation. (author). 31 refs

  6. On the dimensional reduction of a gravitational theory containing higher-derivative terms

    International Nuclear Information System (INIS)

    Pollock, M.D.

    1990-02-01

    From the higher-dimensional gravitational theory L-circumflex=R-circumflex-2Λ-circumflex-α-circumflex 1 R-circumflex 2 =α-circumflex 2 R-circumflex AB R-circumflex AB -α-circumflex 3 R-circumflex ABCD R-circumflex ABCD , we derive the effective four-dimensional Lagrangian L. (author). 12 refs

  7. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Akhbardeh, Alireza; Jacobs, Michael A. [Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States); Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States) and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States)

    2012-04-15

    Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment

  8. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

    International Nuclear Information System (INIS)

    Akhbardeh, Alireza; Jacobs, Michael A.

    2012-01-01

    Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B 1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both

  9. Assessment of Schrodinger Eigenmaps for target detection

    Science.gov (United States)

    Dorado Munoz, Leidy P.; Messinger, David W.; Czaja, Wojtek

    2014-06-01

    Non-linear dimensionality reduction methods have been widely applied to hyperspectral imagery due to its structure as the information can be represented in a lower dimension without losing information, and because the non-linear methods preserve the local geometry of the data while the dimension is reduced. One of these methods is Laplacian Eigenmaps (LE), which assumes that the data lies on a low dimensional manifold embedded in a high dimensional space. LE builds a nearest neighbor graph, computes its Laplacian and performs the eigendecomposition of the Laplacian. These eigenfunctions constitute a basis for the lower dimensional space in which the geometry of the manifold is preserved. In addition to the reduction problem, LE has been widely used in tasks such as segmentation, clustering, and classification. In this regard, a new Schrodinger Eigenmaps (SE) method was developed and presented as a semi-supervised classification scheme in order to improve the classification performance and take advantage of the labeled data. SE is an algorithm built upon LE, where the former Laplacian operator is replaced by the Schrodinger operator. The Schrodinger operator includes a potential term V, that, taking advantage of the additional information such as labeled data, allows clustering of similar points. In this paper, we explore the idea of using SE in target detection. In this way, we present a framework where the potential term V is defined as a barrier potential: a diagonal matrix encoding the spatial position of the target, and the detection performance is evaluated by using different targets and different hyperspectral scenes.

  10. Robust methods for data reduction

    CERN Document Server

    Farcomeni, Alessio

    2015-01-01

    Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis.The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analy

  11. On the integrability of a Hamiltonian reduction of a 2+1-dimensional non-isothermal rotating gas cloud system

    International Nuclear Information System (INIS)

    Rogers, C; Schief, W K

    2011-01-01

    A 2+1-dimensional version of a non-isothermal gas dynamic system with origins in the work of Ovsiannikov and Dyson on spinning gas clouds is shown to admit a Hamiltonian reduction which is completely integrable when the adiabatic index γ = 2. This nonlinear dynamical subsystem is obtained via an elliptic vortex ansatz which is intimately related to the construction of a Lax pair in the integrable case. The general solution of the gas dynamic system is derived in terms of Weierstrass (elliptic) functions. The latter derivation makes use of a connection with a stationary nonlinear Schrödinger equation and a Steen–Ermakov–Pinney equation, the superposition principle of which is based on the classical Lamé equation

  12. Congruent reduction and mode conversion in 4-dimensional plasmas

    International Nuclear Information System (INIS)

    Friedland, L.; Kaufman, A.N.

    1987-04-01

    Standard eikonal theory reduces, to N=1, the order of the system of equations underlying wave propagation in inhomogeneous plasmas. The condition for this remarkable reducibility is that only one eigenvalue of the unreduced NxN dispersion matrix D(k,x) vanishes at a time. If, however, two or more eigenvalues of D become simultaneously small, the geometric optics reduction scheme becomes singular. These regions are associated with linear mode conversion, and are described by higher order systems. A new reduction scheme based on congruent transformations of D is developed, and it is shown that, in ''degenerate'' plasma regions, a partial reduction of order is possible. The method comprises a constructive step-by-step procedure, which, in the most frequent (doubly) degenerate case, yields a second order system, describing the pairwise mode conversion problems, the solution of which in general geometry has been found recently

  13. Dimensional degression in AdSd

    International Nuclear Information System (INIS)

    Artsukevich, A. Yu.; Vasiliev, M. A.

    2009-01-01

    We analyze the pattern of fields in (d+1)-dimensional anti-de Sitter space in terms of those in d-dimensional anti-de Sitter space. The procedure, which is neither dimensional reduction nor dimensional compactification, is called dimensional degression. The analysis is performed group theoretically for all totally symmetric bosonic and fermionic representations of the anti-de Sitter algebra. The field-theoretical analysis is done for a massive scalar field in AdS d+d ' and massless spin-one-half, spin-one, and spin-two fields in AdS d+1 . The mass spectra of the resulting towers of fields in AdS d are found. For the scalar field case, the obtained results extend to the shadow sector those obtained by Metsaev [Nucl. Phys. B, Proc. Suppl. 102, 100 (2001)] by a different method.

  14. arXiv Supersymmetric gauged matrix models from dimensional reduction on a sphere

    CERN Document Server

    Closset, Cyril; Seong, Rak-Kyeong

    2018-05-04

    It was recently proposed that $ \\mathcal{N} $ = 1 supersymmetric gauged matrix models have a duality of order four — that is, a quadrality — reminiscent of infrared dualities of SQCD theories in higher dimensions. In this note, we show that the zero-dimensional quadrality proposal can be inferred from the two-dimensional Gadde-Gukov-Putrov triality. We consider two-dimensional $ \\mathcal{N} $ = (0, 2) SQCD compactified on a sphere with the half-topological twist. For a convenient choice of R-charge, the zero-mode sector on the sphere gives rise to a simple $ \\mathcal{N} $ = 1 gauged matrix model. Triality on the sphere then implies a triality relation for the supersymmetric matrix model, which can be completed to the full quadrality.

  15. Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

    Directory of Open Access Journals (Sweden)

    Jeroen van Roy

    2018-03-01

    Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.

  16. A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

    Science.gov (United States)

    Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

    2017-10-12

    Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

  17. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

    Science.gov (United States)

    Doostparast Torshizi, Abolfazl; Petzold, Linda R

    2018-01-01

    Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  18. Infinite dimensional gauge structure of Kaluza-Klein theories II: D>5

    International Nuclear Information System (INIS)

    Aulakh, C.S.; Sahdev, D.

    1985-12-01

    We carry out the dimensional reduction of the pure gravity sector of Kaluza Klein theories without making truncations of any sort. This generalizes our previous result for the 5-dimensional case to 4+d(>1) dimensions. The effective 4-dimensional action has the structure of an infinite dimensional gauge theory

  19. A three-dimensional water quality modeling approach for exploring the eutrophication responses to load reduction scenarios in Lake Yilong (China)

    International Nuclear Information System (INIS)

    Zhao, Lei; Li, Yuzhao; Zou, Rui; He, Bin; Zhu, Xiang; Liu, Yong; Wang, Junsong; Zhu, Yongguan

    2013-01-01

    Lake Yilong in Southwestern China has been under serious eutrophication threat during the past decades; however, the lake water remained clear until sudden sharp increase in Chlorophyll a (Chl a) and turbidity in 2009 without apparent change in external loading levels. To investigate the causes as well as examining the underlying mechanism, a three-dimensional hydrodynamic and water quality model was developed, simulating the flow circulation, pollutant fate and transport, and the interactions between nutrients, phytoplankton and macrophytes. The calibrated and validated model was used to conduct three sets of scenarios for understanding the water quality responses to various load reduction intensities and ecological restoration measures. The results showed that (a) even if the nutrient loads is reduced by as much as 77%, the Chl a concentration decreased only by 50%; and (b) aquatic vegetation has strong interaction with phytoplankton, therefore requiring combined watershed and in-lake management for lake restoration. -- Highlights: ► We quantitatively investigated the non-linear lake responses to load reduction. ► The aquatic ecological condition had a great impact on algal blooms. ► Only water quality improvement cannot ensure the aquatic ecology restoration. -- The lake water quality responds to watershed load reduction in a nonlinear way, which requires combined watershed and in-lake management for lake restoration

  20. A Fast and High-precision Orientation Algorithm for BeiDou Based on Dimensionality Reduction

    Directory of Open Access Journals (Sweden)

    ZHAO Jiaojiao

    2015-05-01

    Full Text Available A fast and high-precision orientation algorithm for BeiDou is proposed by deeply analyzing the constellation characteristics of BeiDou and GEO satellites features.With the advantage of good east-west geometry, the baseline vector candidate values were solved by the GEO satellites observations combined with the dimensionality reduction theory at first.Then, we use the ambiguity function to judge the values in order to obtain the optical baseline vector and get the wide lane integer ambiguities. On this basis, the B1 ambiguities were solved. Finally, the high-precision orientation was estimated by the determinating B1 ambiguities. This new algorithm not only can improve the ill-condition of traditional algorithm, but also can reduce the ambiguity search region to a great extent, thus calculating the integer ambiguities in a single-epoch.The algorithm is simulated by the actual BeiDou ephemeris and the result shows that the method is efficient and fast for orientation. It is capable of very high single-epoch success rate(99.31% and accurate attitude angle (the standard deviation of pitch and heading is respectively 0.07°and 0.13°in a real time and dynamic environment.

  1. Three New (2+1)-dimensional Integrable Systems and Some Related Darboux Transformations

    International Nuclear Information System (INIS)

    Guo Xiu-Rong

    2016-01-01

    We introduce two operator commutators by using different-degree loop algebras of the Lie algebra A 1 , then under the framework of zero curvature equations we generate two (2+1)-dimensional integrable hierarchies, including the (2+1)-dimensional shallow water wave (SWW) hierarchy and the (2+1)-dimensional Kaup-Newell (KN) hierarchy. Through reduction of the (2+1)-dimensional hierarchies, we get a (2+1)-dimensional SWW equation and a (2+1)-dimensional KN equation. Furthermore, we obtain two Darboux transformations of the (2+1)-dimensional SWW equation. Similarly, the Darboux transformations of the (2+1)-dimensional KN equation could be deduced. Finally, with the help of the spatial spectral matrix of SWW hierarchy, we generate a (2+1) heat equation and a (2+1) nonlinear generalized SWW system containing inverse operators with respect to the variables x and y by using a reduction spectral problem from the self-dual Yang-Mills equations. (paper)

  2. Hidden symmetries in minimal five-dimensional supergravity

    International Nuclear Information System (INIS)

    Poessel, Markus; Silva, Sebastian

    2004-01-01

    We study the hidden symmetries arising in the dimensional reduction of d=5, N=2 supergravity to three dimensions. Extending previous partial results for the bosonic part, we give a derivation that includes fermionic terms, shedding light on the appearance of the local hidden symmetry SO(4) in the reduction

  3. Development of a two-dimensional high-performance liquid chromatography system coupled with on-line reduction as a new efficient analytical method of 3-nitrobenzanthrone, a potential human carcinogen.

    Science.gov (United States)

    Hasei, Tomohiro; Nakanishi, Haruka; Toda, Yumiko; Watanabe, Tetsushi

    2012-08-31

    3-Nitrobenzanthrone (3-NBA) is an extremely strong mutagen and carcinogen in rats inducing squamous cell carcinoma and adenocarcinoma. We developed a new sensitive analytical method, a two-dimensional HPLC system coupled with on-line reduction, to quantify non-fluorescent 3-NBA as fluorescent 3-aminobenzanthrone (3-ABA). The two-dimensional HPLC system consisted of reversed-phase HPLC and normal-phase HPLC, which were connected with a switch valve. 3-NBA was purified by reversed-phase HPLC and reduced to 3-ABA with a catalyst column, packed with alumina coated with platinum, in ethanol. An alcoholic solvent is necessary for reduction of 3-NBA, but 3-ABA is not fluorescent in the alcoholic solvent. Therefore, 3-ABA was separated from alcohol and impurities by normal-phase HPLC and detected with a fluorescence detector. Extracts from surface soil, airborne particles, classified airborne particles, and incinerator dust were applied to the two-dimensional HPLC system after clean-up with a silica gel column. 3-NBA, detected as 3-ABA, in the extracts was found as a single peak on the chromatograms without any interfering peaks. 3-NBA was detected in 4 incinerator dust samples (n=5). When classified airborne particles, that is, those 7.0 μm in size, were applied to the two-dimensional HPLC system after purified using a silica gel column, 3-NBA was detected in those particles with particle sizes NBA in airborne particles and the detection of 3-NBA in incinerator dust. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. Four-dimensional anti-de Sitter toroidal black holes from a three-dimensional perspective: Full complexity

    International Nuclear Information System (INIS)

    Zanchin, Vilson T.; Kleber, Antares; Lemos, Jose P.S.

    2002-01-01

    The dimensional reduction of black hole solutions in four-dimensional (4D) general relativity is performed and new 3D black hole solutions are obtained. Considering a 4D spacetime with one spacelike Killing vector, it is possible to split the Einstein-Hilbert-Maxwell action with a cosmological term in terms of 3D quantities. Definitions of quasilocal mass and charges in 3D spacetimes are reviewed. The analysis is then particularized to the toroidal charged rotating anti-de Sitter black hole. The reinterpretation of the fields and charges in terms of a three-dimensional point of view is given in each case, and the causal structure analyzed

  5. Green functions and dimensional reduction of quantum fields on product manifolds

    International Nuclear Information System (INIS)

    Haba, Z

    2008-01-01

    We discuss Euclidean Green functions on product manifolds P=N x M. We show that if M is compact and N is not compact then the Euclidean field on P can be approximated by its zero mode which is a Euclidean field on N. We estimate the remainder of this approximation. We show that for large distances on N the remainder is small. If P=R D-1 x S β , where S β is a circle of radius β, then the result reduces to the well-known approximation of the D-dimensional finite temperature quantum field theory by (D - 1)-dimensional one in the high-temperature limit. Analytic continuation of Euclidean fields is discussed briefly

  6. Hamiltonian formalism of two-dimensional Vlasov kinetic equation.

    Science.gov (United States)

    Pavlov, Maxim V

    2014-12-08

    In this paper, the two-dimensional Benney system describing long wave propagation of a finite depth fluid motion and the multi-dimensional Russo-Smereka kinetic equation describing a bubbly flow are considered. The Hamiltonian approach established by J. Gibbons for the one-dimensional Vlasov kinetic equation is extended to a multi-dimensional case. A local Hamiltonian structure associated with the hydrodynamic lattice of moments derived by D. J. Benney is constructed. A relationship between this hydrodynamic lattice of moments and the two-dimensional Vlasov kinetic equation is found. In the two-dimensional case, a Hamiltonian hydrodynamic lattice for the Russo-Smereka kinetic model is constructed. Simple hydrodynamic reductions are presented.

  7. Dimensionality reduction of quality of life indicators

    Directory of Open Access Journals (Sweden)

    Andrea Jindrová

    2012-01-01

    Full Text Available Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defined indicators that would give comprehensive information about the quality of life on a local level. In this paper we focus on the determination (selection of groups of indicators that can be interpreted, on the basis of studied literature, as factors characterizing the quality of life. Furthermore, on the application of methods to reduce the dimensionality of these indicators, from the source of the database CULS KROK, which provides statistics on the regional and districts level. To reduce the number of indicators and the subsequent creation of derived variables that capture the relationships between selected indicators multivariate statistical analysis methods, especially method of principal components and factor analysis were used. This paper also indicates the methodology grant project “Methodological Approaches to assess Subjective Aspects of the life quality in regions of the Czech Republic”.

  8. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

    Science.gov (United States)

    Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

    2015-06-07

    As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Symmetry Reductions of a 1.5-Layer Ocean Circulation Model

    International Nuclear Information System (INIS)

    Huang Fei; Lou Senyue

    2007-01-01

    The (2+1)-dimensional nonlinear 1.5-layer ocean circulation model without external wind stress forcing is analyzed by using the classical Lie group approach. Some Lie point symmetries and their corresponding two-dimensional reduction equations are obtained.

  10. Study of the X-Ray Diagnosis of Unstable Pelvic Fracture Displacements in Three-Dimensional Space and its Application in Closed Reduction.

    Science.gov (United States)

    Shi, Chengdi; Cai, Leyi; Hu, Wei; Sun, Junying

    2017-09-19

    ABSTRACTS Objective: To study the method of X-ray diagnosis of unstable pelvic fractures displaced in three-dimensional (3D) space and its clinical application in closed reduction. Five models of hemipelvic displacement were made in an adult pelvic specimen. Anteroposterior radiographs of the pelvis were analyzed in PACS. The method of X-ray diagnosis was applied in closed reductions. From February 2012 to June 2016, 23 patients (15 men, 8 women; mean age, 43.4 years) with unstable pelvic fractures were included. All patients were treated by closed reduction and percutaneous cannulate screw fixation of the pelvic ring. According to Tile's classification, the patients were classified into type B1 in 7 cases, B2 in 3, B3 in 3, C1 in 5, C2 in 3, and C3 in 2. The operation time and intraoperative blood loss were recorded. Postoperative images were evaluated by Matta radiographic standards. Five models of displacement were made successfully. The X-ray features of the models were analyzed. For clinical patients, the average operation time was 44.8 min (range, 20-90 min) and the average intraoperative blood loss was 35.7 (range, 20-100) mL. According to the Matta standards, 7 cases were excellent, 12 cases were good, and 4 were fair. The displacements in 3D space of unstable pelvic fractures can be diagnosed rapidly by X-ray analysis to guide closed reduction, with a satisfactory clinical outcome.

  11. Coset Space Dimensional Reduction approach to the Standard Model

    International Nuclear Information System (INIS)

    Farakos, K.; Kapetanakis, D.; Koutsoumbas, G.; Zoupanos, G.

    1988-01-01

    We present a unified theory in ten dimensions based on the gauge group E 8 , which is dimensionally reduced to the Standard Mode SU 3c xSU 2 -LxU 1 , which breaks further spontaneously to SU 3L xU 1em . The model gives similar predictions for sin 2 θ w and proton decay as the minimal SU 5 G.U.T., while a natural choice of the coset space radii predicts light Higgs masses a la Coleman-Weinberg

  12. Unitarity cuts and Reduction to master integrals in d dimensions for one-loop amplitudes

    CERN Document Server

    Anastasiou, C; Feng, B; Kunszt, Z; Mastrolia, Pierpaolo; Anastasiou, Charalampos; Britto, Ruth; Feng, Bo; Kunszt, Zoltan; Mastrolia, Pierpaolo

    2007-01-01

    We present an alternative reduction to master integrals for one-loop amplitudes using a unitarity cut method in arbitrary dimensions. We carry out the reduction in two steps. The first step is a pure four-dimensional cut-integration of tree amplitudes with a mass parameter, and the second step is applying dimensional shift identities to master integrals. This reduction is performed at the integrand level, so that coefficients can be read out algebraically.

  13. Reduced order methods for modeling and computational reduction

    CERN Document Server

    Rozza, Gianluigi

    2014-01-01

    This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics.  Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This...

  14. Reduction of metal artifact in three-dimensional computed tomography (3D CT) with dental impression materials.

    Science.gov (United States)

    Park, W S; Kim, K D; Shin, H K; Lee, S H

    2007-01-01

    Metal Artifact still remains one of the main drawbacks in craniofacial Three-Dimensional Computed Tomography (3D CT). In this study, we tried to test the efficacy of additional silicone dental impression materials as a "tooth shield" for the reduction of metal artifact caused by metal restorations and orthodontic appliances. 6 phantoms with 4 teeth were prepared for this in vitro study. Orthodontic bracket, bands and amalgam restorations were placed in each tooth to reproduce various intraoral conditions. Standardized silicone shields were fabricated and placed around the teeth. CT image acquisition was performed with and without silicone shields. Maximum value, mean, and standard deviation of Hounsfield Units (HU) were compared with the presence of silicone shields. In every situation, metal artifacts were reduced in quality and quantity when silicone shields are used. Amalgam restoration made most serious metal artifact. Silicone shields made by dental impression material might be effective way to reduce the metal artifact caused by dental restoration and orthodontic appliances. This will help more excellent 3D image from 3D CT in craniofacial area.

  15. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

    OpenAIRE

    Zekić-Sušac, Marijana; Pfeifer, Sanja; Šarlija, Nataša

    2014-01-01

    Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART ...

  16. One-dimensional reduction of viscous jets. I. Theory

    Science.gov (United States)

    Pitrou, Cyril

    2018-04-01

    We build a general formalism to describe thin viscous jets as one-dimensional objects with an internal structure. We present in full generality the steps needed to describe the viscous jets around their central line, and we argue that the Taylor expansion of all fields around that line is conveniently expressed in terms of symmetric trace-free tensors living in the two dimensions of the fiber sections. We recover the standard results of axisymmetric jets and we report the first and second corrections to the lowest order description, also allowing for a rotational component around the axis of symmetry. When applied to generally curved fibers, the lowest order description corresponds to a viscous string model whose sections are circular. However, when including the first corrections, we find that curved jets generically develop elliptic sections. Several subtle effects imply that the first corrections cannot be described by a rod model since it amounts to selectively discard some corrections. However, in a fast rotating frame, we find that the dominant effects induced by inertial and Coriolis forces should be correctly described by rod models. For completeness, we also recover the constitutive relations for forces and torques in rod models and exhibit a missing term in the lowest order expression of viscous torque. Given that our method is based on tensors, the complexity of all computations has been beaten down by using an appropriate tensor algebra package such as xAct, allowing us to obtain a one-dimensional description of curved viscous jets with all the first order corrections consistently included. Finally, we find a description for straight fibers with elliptic sections as a special case of these results, and recover that ellipticity is dynamically damped by surface tension. An application to toroidal viscous fibers is presented in the companion paper [Pitrou, Phys. Rev. E 97, 043116 (2018), 10.1103/PhysRevE.97.043116].

  17. Parametric embedding for class visualization.

    Science.gov (United States)

    Iwata, Tomoharu; Saito, Kazumi; Ueda, Naonori; Stromsten, Sean; Griffiths, Thomas L; Tenenbaum, Joshua B

    2007-09-01

    We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

  18. Two-dimensional dynamics of elasto-inertial turbulence and its role in polymer drag reduction

    Science.gov (United States)

    Sid, S.; Terrapon, V. E.; Dubief, Y.

    2018-02-01

    The goal of the present study is threefold: (i) to demonstrate the two-dimensional nature of the elasto-inertial instability in elasto-inertial turbulence (EIT), (ii) to identify the role of the bidimensional instability in three-dimensional EIT flows, and (iii) to establish the role of the small elastic scales in the mechanism of self-sustained EIT. Direct numerical simulations of viscoelastic fluid flows are performed in both two- and three-dimensional straight periodic channels using the Peterlin finitely extensible nonlinear elastic model (FENE-P). The Reynolds number is set to Reτ=85 , which is subcritical for two-dimensional flows but beyond the transition for three-dimensional ones. The polymer properties selected correspond to those of typical dilute polymer solutions, and two moderate Weissenberg numbers, Wiτ=40 ,100 , are considered. The simulation results show that sustained turbulence can be observed in two-dimensional subcritical flows, confirming the existence of a bidimensional elasto-inertial instability. The same type of instability is also observed in three-dimensional simulations where both Newtonian and elasto-inertial turbulent structures coexist. Depending on the Wi number, one type of structure can dominate and drive the flow. For large Wi values, the elasto-inertial instability tends to prevail over the Newtonian turbulence. This statement is supported by (i) the absence of typical Newtonian near-wall vortices and (ii) strong similarities between two- and three-dimensional flows when considering larger Wi numbers. The role of small elastic scales is investigated by introducing global artificial diffusion (GAD) in the hyperbolic transport equation for polymers. The aim is to measure how the flow reacts when the smallest elastic scales are progressively filtered out. The study results show that the introduction of large polymer diffusion in the system strongly damps a significant part of the elastic scales that are necessary to feed

  19. The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2010-05-01

    This paper compares the application of three different dimension reduction techniques to the problem of locating cryptography in compiled object code. A simple classi?er is used to compare dimension reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classi?cation accuracy as the number of dimensions is increased.

  20. Bubble dynamic templated deposition of three-dimensional palladium nanostructure catalysts: Approach to oxygen reduction using macro-, micro-, and nano-architectures on electrode surfaces

    International Nuclear Information System (INIS)

    Yang Guimei; Chen Xing; Li Jie; Guo Zheng; Liu Jinhuai; Huang Xingjiu

    2011-01-01

    Highlights: → We synthesize the Pd nanostructures by bubbles dynamic templated. → We obtain Pd nanobuds and Pd nanodendrites by changing the reaction precursor. → We obtain Pd macroelectrode voltammertric behavior using small amount of Pd materials. → We proved a ECE process. → The Pd nanostructures/GCE for O 2 reduction is a 2-step 4-electron process. - Abstract: Three-dimensional (3D) palladium (Pd) nanostructures (that is, nano-buds or nano-dendrites) are fabricated by bubble dynamic templated deposition of Pd onto a glassy carbon electrode (GCE). The morphology can be tailored by changing the precursor concentration and reaction time. Scanning electron microscopy images reveal that nano-buds or nano-dendrites consist of nanoparticles of 40-70 nm in diameter. The electrochemical reduction of oxygen is reported at such kinds of 3D nanostructure electrodes in aqueous solution. Data were collected using cyclic voltammetry. We demonstrate the Pd macroelectrode behavior of Pd nanostructure modified electrode by exploiting the diffusion model of macro-, micro-, and nano-architectures. In contrast to bare GCE, a significant positive shift and splitting of the oxygen reduction peak (vs Ag/AgCl/saturated KCl) at Pd nanostructure modified GCE was observed.

  1. Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI

    Science.gov (United States)

    Wang, Yan; Ma, Guangkai; An, Le; Shi, Feng; Zhang, Pei; Lalush, David S.; Wu, Xi; Pu, Yifei; Zhou, Jiliu; Shen, Dinggang

    2017-01-01

    Objective To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semi-supervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion This work proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients. PMID:27187939

  2. Effective Image Database Search via Dimensionality Reduction

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Aanæs, Henrik

    2008-01-01

    Image search using the bag-of-words image representation is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps involved in the bag-of-words approach are feature extraction, vocabul......Image search using the bag-of-words image representation is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps involved in the bag-of-words approach are feature extraction......, vocabulary building, and searching with a query image. It is important to keep the computational cost low through all steps. In this paper we focus on the efficiency of the technique. To do that we substantially reduce the dimensionality of the features by the use of PCA and addition of color. Building...... of the visual vocabulary is typically done using k-means. We investigate a clustering algorithm based on the leader follower principle (LF-clustering), in which the number of clusters is not fixed. The adaptive nature of LF-clustering is shown to improve the quality of the visual vocabulary using this...

  3. Reduction of Large Dynamical Systems by Minimization of Evolution Rate

    Science.gov (United States)

    Girimaji, Sharath S.

    1999-01-01

    Reduction of a large system of equations to a lower-dimensional system of similar dynamics is investigated. For dynamical systems with disparate timescales, a criterion for determining redundant dimensions and a general reduction method based on the minimization of evolution rate are proposed.

  4. The one-loop Green's functions of dimensionally reduced gauge theories

    International Nuclear Information System (INIS)

    Ketov, S.V.; Prager, Y.S.

    1988-01-01

    The dimensional regularization technique as well as that by dimensional reduction is applied to the calculation of the regularized one-loop Green's functions in dsub(o)-dimensional Yang-Mills theory with real massless scalars and spinors in arbitrary (real) representations of a gauge group G. As a particular example, the super-symmetrically regularized one-loop Green's functions of the N=4 supersymmetric Yang-Mills model are derived. (author). 17 refs

  5. One-dimensional structures behind twisted and untwisted superYang-Mills theory

    CERN Document Server

    Baulieu, Laurent

    2011-01-01

    We give a one-dimensional interpretation of the four-dimensional twisted N=1 superYang-Mills theory on a Kaehler manifold by performing an appropriate dimensional reduction. We prove the existence of a 6-generator superalgebra, which does not possess any invariant Lagrangian but contains two different subalgebras that determine the twisted and untwisted formulations of the N=1 superYang-Mills theory.

  6. Can Semi-Supervised Learning Explain Incorrect Beliefs about Categories?

    Science.gov (United States)

    Kalish, Charles W.; Rogers, Timothy T.; Lang, Jonathan; Zhu, Xiaojin

    2011-01-01

    Three experiments with 88 college-aged participants explored how unlabeled experiences--learning episodes in which people encounter objects without information about their category membership--influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then…

  7. Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs

    Energy Technology Data Exchange (ETDEWEB)

    Liao, Qifeng, E-mail: liaoqf@shanghaitech.edu.cn [School of Information Science and Technology, ShanghaiTech University, Shanghai 200031 (China); Lin, Guang, E-mail: guanglin@purdue.edu [Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States)

    2016-07-15

    In this paper we present a reduced basis ANOVA approach for partial deferential equations (PDEs) with random inputs. The ANOVA method combined with stochastic collocation methods provides model reduction in high-dimensional parameter space through decomposing high-dimensional inputs into unions of low-dimensional inputs. In this work, to further reduce the computational cost, we investigate spatial low-rank structures in the ANOVA-collocation method, and develop efficient spatial model reduction techniques using hierarchically generated reduced bases. We present a general mathematical framework of the methodology, validate its accuracy and demonstrate its efficiency with numerical experiments.

  8. Topological aspects of classical and quantum (2+1)-dimensional gravity

    International Nuclear Information System (INIS)

    Soda, Jiro.

    1990-03-01

    In order to understand (3+1)-dimensional gravity, (2+1)-dimensional gravity is studied as a toy model. Our emphasis is on its topological aspects, because (2+1)-dimensional gravity without matter fields has no local dynamical degrees of freedom. Starting from a review of the canonical ADM formalism and York's formalism for the initial value problem, we will solve the evolution equations of (2+1)-dimensional gravity with a cosmological constant in the case of g=0 and g=1, where g is the genus of Riemann surface. The dynamics of it is understood as the geodesic motion in the moduli space. This remarkable fact is the same with the case of (2+1)-dimensional pure gravity and seen more apparently from the action level. Indeed we will show the phase space reduction of (2+1)-dimensional gravity in the case of g=1. For g ≥ 2, unfortunately we are not able to explicitly perform the phase space reduction of (2+1)-dimensional gravity due to the complexity of the Hamiltonian constraint equation. Based on this result, we will attempt to incorporate matter fields into (2+1)-dimensional pure gravity. The linearization and mini-superspace methods are used for this purpose. By using the linearization method, we conclude that the transverse-traceless part of the energy-momentum tensor affects the geodesic motion. In the case of the Einstein-Maxwell theory, we observe that the Wilson lines interact with the geometry to bend the geodesic motion. We analyze the mini-superspace model of (2+1)-dimensional gravity with the matter fields in the case of g=0 and g=1. For g=0, a wormhole solution is found but for g=1 we can not find an analogous solution. Quantum gravity is also considered and we succeed to perform the phase space reduction of (2+1)-dimensional gravity in the case of g=1 at the quantum level. From this analysis we argue that the conformal rotation is not necessary in the sense that the Euclidean quantum gravity is inappropriate for the full gravity. (author)

  9. High-dimensional data in economics and their (robust) analysis

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2017-01-01

    Roč. 12, č. 1 (2017), s. 171-183 ISSN 1452-4864 R&D Projects: GA ČR GA17-07384S Institutional support: RVO:67985556 Keywords : econometrics * high-dimensional data * dimensionality reduction * linear regression * classification analysis * robustness Subject RIV: BA - General Mathematics OBOR OECD: Business and management http://library.utia.cas.cz/separaty/2017/SI/kalina-0474076.pdf

  10. Higher-order gravity in higher dimensions: geometrical origins of four-dimensional cosmology?

    Energy Technology Data Exchange (ETDEWEB)

    Troisi, Antonio [Universita degli Studi di Salerno, Dipartimento di Fisica ' ' E.R. Caianiello' ' , Salerno (Italy)

    2017-03-15

    Determining the cosmological field equations is still very much debated and led to a wide discussion around different theoretical proposals. A suitable conceptual scheme could be represented by gravity models that naturally generalize Einstein theory like higher-order gravity theories and higher-dimensional ones. Both of these two different approaches allow one to define, at the effective level, Einstein field equations equipped with source-like energy-momentum tensors of geometrical origin. In this paper, the possibility is discussed to develop a five-dimensional fourth-order gravity model whose lower-dimensional reduction could provide an interpretation of cosmological four-dimensional matter-energy components. We describe the basic concepts of the model, the complete field equations formalism and the 5-D to 4-D reduction procedure. Five-dimensional f(R) field equations turn out to be equivalent, on the four-dimensional hypersurfaces orthogonal to the extra coordinate, to an Einstein-like cosmological model with three matter-energy tensors related with higher derivative and higher-dimensional counter-terms. By considering the gravity model with f(R) = f{sub 0}R{sup n} the possibility is investigated to obtain five-dimensional power law solutions. The effective four-dimensional picture and the behaviour of the geometrically induced sources are finally outlined in correspondence to simple cases of such higher-dimensional solutions. (orig.)

  11. KK-monopoles and G-structures in M-theory/type IIA reductions

    International Nuclear Information System (INIS)

    Danielsson, Ulf; Dibitetto, Giuseppe; Guarino, Adolfo

    2015-01-01

    We argue that M-theory/massive IIA backgrounds including KK-monopoles are suitably described in the language of G-structures and their intrinsic torsion. To this end, we study classes of minimal supergravity models that admit an interpretation as twisted reductions in which the twist parameters are not restricted to satisfy the Jacobi constraints ω ω=0 required by an ordinary Scherk-Schwarz reduction. We first derive the correspondence between four-dimensional data and torsion classes of the internal space and, then, check the one-to-one correspondence between higher-dimensional and four-dimensional equations of motion. Remarkably, the whole construction holds regardless of the Jacobi constraints, thus shedding light upon the string/M-theory interpretation of (smeared) KK-monopoles.

  12. Multifactor dimensionality reduction analysis identifies specific nucleotide patterns promoting genetic polymorphisms

    Directory of Open Access Journals (Sweden)

    Arehart Eric

    2009-03-01

    Full Text Available Abstract Background The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation. Results We modeled the relationship between DNA sequence and observed polymorphisms using the novel multifactor dimensionality reduction (MDR approach. MDR was originally developed to detect synergistic interactions between multiple SNPs that are predictive of disease susceptibility. We initially assembled data from the Broad Institute as a pilot test for the hypothesis that flanking region patterns associate with mutagenesis (n = 2194. We then confirmed and expanded our inquiry with human SNPs within coding regions and their flanking sequences collected from the National Center for Biotechnology Information (NCBI database (n = 29967 and a control set of sequences (coding region not associated with SNP sites randomly selected from the NCBI database (n = 29967. We discovered seven flanking region pattern associations in the Broad dataset which reached a minimum significance level of p ≤ 0.05. Significant models (p Conclusion The present study represents the first use of this computational methodology for modeling nonlinear patterns in molecular genetics. MDR was able to identify distinct nucleotide patterning around sites of mutations dependent upon the observed nucleotide change. We discovered one flanking region set that included five nucleotides clustered around a specific type of SNP site. Based on the strongly associated patterns identified in

  13. Thermogravimetric study of the kinetics of lithium titanate reduction by hydrogen

    International Nuclear Information System (INIS)

    Sonak, Sagar; Rakesh, R.; Jain, Uttam; Mukherjee, Abhishek; Kumar, Sanjay; Krishnamurthy, Nagaiyar

    2014-01-01

    Highlights: • Li 2 TiO 3 powder is synthesized by the gel combustion route. • Activation energy of reduction of Li 2 TiO 3 by H 2 found out to be 27.45 kJ/mol H 2 . • Non-stoichiometric phase of Li 2 TiO 3 is formed in hydrogen atmosphere. • One-dimensional diffusion appears to be the most probable mechanism of reduction. - Abstract: The lithium titanate powder was synthesized by gel-combustion route. The mechanism and the kinetics of hydrogen interaction with lithium titanate powder were studied using non-isothermal thermogravimetric technique. Lithium titanate underwent reduction in hydrogen atmosphere which led to the formation of oxygen deficient non-stoichiometric compound in lithium titanate. One-dimensional diffusion appeared to be the most probable reaction mechanism. The activation energy for reduction of lithium titanate under hydrogen atmosphere was found to be 27.4 kJ/mol/K. Structural changes after hydrogen reduction in lithium titanate were observed in X-ray diffraction analysis

  14. Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.

    Science.gov (United States)

    Palmerini, Luca; Mellone, Sabato; Rocchi, Laura; Chiari, Lorenzo

    2011-01-01

    The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.

  15. One-dimensional structures behind twisted and untwisted super Yang-Mills theory

    Energy Technology Data Exchange (ETDEWEB)

    Baulieu, Laurent [CERN, Geneve (Switzerland). Theoretical Div.; Toppan, Francesco, E-mail: baulieu@lpthe.jussieu.f, E-mail: toppan@cbpf.b [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil)

    2010-07-01

    We give a one-dimensional interpretation of the four-dimensional twisted N = 1 super Yang-Mills theory on a Kaehler manifold by performing an appropriate dimensional reduction. We prove the existence of a 6-generator superalgebra, which does not possess any invariant Lagrangian but contains two different subalgebras that determine the twisted and untwisted formulations of the N = 1 super Yang-Mills theory. (author)

  16. One-dimensional structures behind twisted and untwisted super Yang-Mills theory

    International Nuclear Information System (INIS)

    Baulieu, Laurent

    2010-01-01

    We give a one-dimensional interpretation of the four-dimensional twisted N = 1 super Yang-Mills theory on a Kaehler manifold by performing an appropriate dimensional reduction. We prove the existence of a 6-generator superalgebra, which does not possess any invariant Lagrangian but contains two different subalgebras that determine the twisted and untwisted formulations of the N = 1 super Yang-Mills theory. (author)

  17. Three-dimensional oscillator and Coulomb systems reduced from Kaehler spaces

    International Nuclear Information System (INIS)

    Nersessian, Armen; Yeranyan, Armen

    2004-01-01

    We define the oscillator and Coulomb systems on four-dimensional spaces with U(2)-invariant Kaehler metric and perform their Hamiltonian reduction to the three-dimensional oscillator and Coulomb systems specified by the presence of Dirac monopoles. We find the Kaehler spaces with conic singularity, where the oscillator and Coulomb systems on three-dimensional sphere and two-sheet hyperboloid originate. Then we construct the superintegrable oscillator system on three-dimensional sphere and hyperboloid, coupled to a monopole, and find their four-dimensional origins. In the latter case the metric of configuration space is a non-Kaehler one. Finally, we extend these results to the family of Kaehler spaces with conic singularities

  18. High-dimensional Data in Economics and their (Robust) Analysis

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2017-01-01

    Roč. 12, č. 1 (2017), s. 171-183 ISSN 1452-4864 R&D Projects: GA ČR GA17-07384S Grant - others:GA ČR(CZ) GA13-01930S Institutional support: RVO:67985807 Keywords : econometrics * high-dimensional data * dimensionality reduction * linear regression * classification analysis * robustness Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability

  19. Gauged supergravities from M-theory reductions

    Science.gov (United States)

    Katmadas, Stefanos; Tomasiello, Alessandro

    2018-04-01

    In supergravity compactifications, there is in general no clear prescription on how to select a finite-dimensional family of metrics on the internal space, and a family of forms on which to expand the various potentials, such that the lower-dimensional effective theory is supersymmetric. We propose a finite-dimensional family of deformations for regular Sasaki-Einstein seven-manifolds M 7, relevant for M-theory compactifications down to four dimensions. It consists of integrable Cauchy-Riemann structures, corresponding to complex deformations of the Calabi-Yau cone M 8 over M 7. The non-harmonic forms we propose are the ones contained in one of the Kohn-Rossi cohomology groups, which is finite-dimensional and naturally controls the deformations of Cauchy-Riemann structures. The same family of deformations can be also described in terms of twisted cohomology of the base M 6, or in terms of Milnor cycles arising in deformations of M 8. Using existing results on SU(3) structure compactifications, we briefly discuss the reduction of M-theory on our class of deformed Sasaki-Einstein manifolds to four-dimensional gauged supergravity.

  20. One-dimensional reduction of viscous jets. II. Applications

    Science.gov (United States)

    Pitrou, Cyril

    2018-04-01

    In a companion paper [Phys. Rev. E 97, 043115 (2018), 10.1103/PhysRevE.97.043115], a formalism allowing to describe viscous fibers as one-dimensional objects was developed. We apply it to the special case of a viscous fluid torus. This allows to highlight the differences with the basic viscous string model and with its viscous rod model extension. In particular, an elliptic deformation of the torus section appears because of surface tension effects, and this cannot be described by viscous string nor viscous rod models. Furthermore, we study the Rayleigh-Plateau instability for periodic deformations around the perfect torus, and we show that the instability is not sufficient to lead to the torus breakup in several droplets before it collapses to a single spherical drop. Conversely, a rotating torus is dynamically attracted toward a stationary solution, around which the instability can develop freely and split the torus in multiple droplets.

  1. Facile Synthesis of Quasi-One-Dimensional Au/PtAu Heterojunction Nanotubes and Their Application as Catalysts in an Oxygen-Reduction Reaction.

    Science.gov (United States)

    Cai, Kai; Liu, Jiawei; Zhang, Huan; Huang, Zhao; Lu, Zhicheng; Foda, Mohamed F; Li, Tingting; Han, Heyou

    2015-05-11

    An intermediate-template-directed method has been developed for the synthesis of quasi-one-dimensional Au/PtAu heterojunction nanotubes by the heterogeneous nucleation and growth of Au on Te/Pt core-shell nanostructures in aqueous solution. The synthesized porous Au/PtAu bimetallic nanotubes (PABNTs) consist of porous tubular framework and attached Au nanoparticles (AuNPs). The reaction intermediates played an important role in the preparation, which fabricated the framework and provided a localized reducing agent for the reduction of the Au and Pt precursors. The Pt7 Au PABNTs showed higher electrocatalytic activity and durability in the oxygen-reduction reaction (ORR) in 0.1 M HClO4 than porous Pt nanotubes (PtNTs) and commercially available Pt/C. The mass activity of PABNTs was 218 % that of commercial Pt/C after an accelerated durability test. This study demonstrates the potential of PABNTs as highly efficient electrocatalysts. In addition, this method provides a facile strategy for the synthesis of desirable hetero-nanostructures with controlled size and shape by utilizing an intermediate template. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Facile fabrication of three-dimensional mesoporous Si/SiC composites via one-step magnesiothermic reduction at relative low temperature

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Zhihang; Ma, Yongjun [State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010 (China); Zhou, Yong [Eco-materials and Renewable Energy Research Center (ERERC), School of Physics, National Lab of Solid State Microstructure, ERERC, Nanjing University, Nanjing 210093 (China); Hu, Shanglian [School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010 (China); Han, Chaojiang [State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010 (China); Pei, Chonghua, E-mail: peichonghua@swust.edu.cn [State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010 (China)

    2013-10-15

    Graphical abstract: - Highlights: • The Si/SiC composites were synthesized by one-step magnesiothermic reduction. • The mesoporous composites have a high specific surface area (655.7 m{sup 2} g{sup −1}). • The composites exhibited a strong photoluminescence and better biocompatibility. • The mechanisms of formation and photoluminescence of sample were discussed. - Abstract: By converting modified silica aerogels to the corresponding silicon/silicon carbide (Si/SiC) without losing its nanostructure, three-dimensional mesoporous (3DM) Si/SiC composites are successfully synthesized via one-step magnesothermic reduction at relative low temperature (650 °C). The phase composition and microstructure of the resulting samples are measured by X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), Raman spectra, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). N{sub 2}-sorption isotherms results show that the products have high Brunauer–Emmett–Teller (BET) specific surface areas (up to 656 m{sup 2} g{sup −1}) and narrow pore-size distributions (1.5–30 nm). The composites exhibit a strong photoluminescence (PL) in blue-green light region (peak centered at 533 nm). We have set out work on the biocompatibility and enhancing PL of samples. As a result of excellent performances of the composites, it can be expected to have significant application in optoelectronics, biosensors, biological tracer and so on.

  3. On reductions of the discrete Kadomtsev-Petviashvili-type equations

    Science.gov (United States)

    Fu, Wei; Nijhoff, Frank W.

    2017-12-01

    The reduction by restricting the spectral parameters k and k\\prime on a generic algebraic curve of degree N is performed for the discrete AKP, BKP and CKP equations, respectively. A variety of two-dimensional discrete integrable systems possessing a more general solution structure arise from the reduction, and in each case a unified formula for the generic positive integer N≥slant 2 is given to express the corresponding reduced integrable lattice equations. The obtained extended two-dimensional lattice models give rise to many important integrable partial difference equations as special degenerations. Some new integrable lattice models such as the discrete Sawada-Kotera, Kaup-Kupershmidt and Hirota-Satsuma equations in extended form are given as examples within the framework.

  4. Effects of applying three-dimensional seismic isolation system on the seismic design of FBR

    International Nuclear Information System (INIS)

    Hirata, Kazuta; Yabana, Shuichi; Kanazawa, Kenji; Matsuda, Akihiro

    1997-01-01

    In this study conceptional three-dimensional seismic isolation system for fast breeder reactor (FBR) is proposed. Effects of applying three-dimensional seismic isolation system on the seismic design for the FBR equipment are evaluated quantitatively. From the evaluation, it is concluded following effects are expected by applying the three-dimensional seismic isolation system to the FBR and the effects are evaluated quantitatively. (1) Reduction of membrane thickness of the reactor vessel (2) Suppression of uplift of fuels by reducing vertical seismic response of the core (3) Reduction of the supports for the piping system (4) Three-dimensional base isolation system for the whole reactor building is advantageous to the combined isolation system of horizontal base isolation for the reactor building and vertical isolation for the equipment. (author)

  5. Exact solutions for the (2+1)-dimensional Boiti-Leon-Pempielli system

    International Nuclear Information System (INIS)

    Hu, Y H; Zheng, C L

    2008-01-01

    The object reduction approach is applied to the (2+1)-dimensional Boiti-Leon-Pempielli system using a special conditional similarity reduction. Abundant exact solutions of this system, including the hyperboloid function solutions, the trigonometric function solutions and a rational function solution, are obtained

  6. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

    Science.gov (United States)

    Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

    2018-05-16

    There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

  7. Super integrable four-dimensional autonomous mappings

    International Nuclear Information System (INIS)

    Capel, H W; Sahadevan, R; Rajakumar, S

    2007-01-01

    A systematic investigation of the complete integrability of a fourth-order autonomous difference equation of the type w(n + 4) = w(n)F(w(n + 1), w(n + 2), w(n + 3)) is presented. We identify seven distinct families of four-dimensional mappings which are super integrable and have three (independent) integrals via a duality relation as introduced in a recent paper by Quispel, Capel and Roberts (2005 J. Phys. A: Math. Gen. 38 3965-80). It is observed that these seven families can be related to the four-dimensional symplectic mappings with two integrals including all the four-dimensional periodic reductions of the integrable double-discrete modified Korteweg-deVries and sine-Gordon equations treated in an earlier paper by two of us (Capel and Sahadevan 2001 Physica A 289 86-106)

  8. Formulation of 11-dimensional supergravity in superspace

    International Nuclear Information System (INIS)

    Cremmer, E.; Ferrara, S.

    1980-01-01

    We formulate on-shell 11-dimensional supergravity in superspace and express its equations of motion in terms of purely geometrical quantities. All torsion and curvature components are solved in terms of a single superfield Wsub(rstu), totally antisymmetric in its (flat vector) indices. The dimensional reduction of this formulation is expected to be related to the superspace formulation of N = 8 extended supergravity and might explain the origin of the hidden (local) SU(8) and (global) E 7 symmetries present in this theory. (orig.)

  9. Wake Management Strategies for Reduction of Turbomachinery Fan Noise

    Science.gov (United States)

    Waitz, Ian A.

    1998-01-01

    The primary objective of our work was to evaluate and test several wake management schemes for the reduction of turbomachinery fan noise. Throughout the course of this work we relied on several tools. These include 1) Two-dimensional steady boundary-layer and wake analyses using MISES (a thin-shear layer Navier-Stokes code), 2) Two-dimensional unsteady wake-stator interaction simulations using UNSFLO, 3) Three-dimensional, steady Navier-Stokes rotor simulations using NEWT, 4) Internal blade passage design using quasi-one-dimensional passage flow models developed at MIT, 5) Acoustic modeling using LINSUB, 6) Acoustic modeling using VO72, 7) Experiments in a low-speed cascade wind-tunnel, and 8) ADP fan rig tests in the MIT Blowdown Compressor.

  10. Spinors and supersymmetry in four-dimensional Euclidean space

    International Nuclear Information System (INIS)

    McKeon, D.G.C.; Sherry, T.N.

    2001-01-01

    Spinors in four-dimensional Euclidean space are treated using the decomposition of the Euclidean space SO(4) symmetry group into SU(2)xSU(2). Both 2- and 4-spinor representations of this SO(4) symmetry group are shown to differ significantly from the corresponding spinor representations of the SO(3, 1) symmetry group in Minkowski space. The simplest self conjugate supersymmetry algebra allowed in four-dimensional Euclidean space is demonstrated to be an N=2 supersymmetry algebra which resembles the N=2 supersymmetry algebra in four-dimensional Minkowski space. The differences between the two supersymmetry algebras gives rise to different representations; in particular an analysis of the Clifford algebra structure shows that the momentum invariant is bounded above by the central charges in 4dE, while in 4dM the central charges bound the momentum invariant from below. Dimensional reduction of the N=1 SUSY algebra in six-dimensional Minkowski space (6dM) to 4dE reproduces our SUSY algebra in 4dE. This dimensional reduction can be used to introduce additional generators into the SUSY algebra in 4dE. Well known interpolating maps are used to relate the N=2 SUSY algebra in 4dE derived in this paper to the N=2 SUSY algebra in 4dM. The nature of the spinors in 4dE allows us to write an axially gauge invariant model which is shown to be both Hermitian and anomaly-free. No equivalent model exists in 4dM. Useful formulae in 4dE are collected together in two appendixes

  11. Multi-task Vector Field Learning.

    Science.gov (United States)

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

    2012-01-01

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

  12. Gauss-Bonnet actions and their dimensionally reduced descendants

    International Nuclear Information System (INIS)

    Mueller-Hoissen, F.

    1989-01-01

    A brief introduction to Gauss-Bonnet type generalizations of the Einstein-Hilbert gravity action in more than four dimensions is given and the structure of associated (effective) theories obtained by dimensional reduction is discussed. (author)

  13. Euclidean D-branes and higher-dimensional gauge theory

    International Nuclear Information System (INIS)

    Acharya, B.S.; Figueroa-O'Farrill, J.M.; Spence, B.; O'Loughlin, M.

    1997-07-01

    We consider euclidean D-branes wrapping around manifolds of exceptional holonomy in dimensions seven and eight. The resulting theory on the D-brane-that is, the dimensional reduction of 10-dimensional supersymmetric Yang-Mills theory-is a cohomological field theory which describes the topology of the moduli space of instantons. The 7-dimensional theory is an N T =2 (or balanced) cohomological theory given by an action potential of Chern-Simons type. As a by-product of this method, we construct a related cohomological field theory which describes the monopole moduli space on a 7-manifold of G 2 holonomy. (author). 22 refs, 3 tabs

  14. System reduction for nanoscale IC design

    CERN Document Server

    2017-01-01

    This book describes the computational challenges posed by the progression toward nanoscale electronic devices and increasingly short design cycles in the microelectronics industry, and proposes methods of model reduction which facilitate circuit and device simulation for specific tasks in the design cycle. The goal is to develop and compare methods for system reduction in the design of high dimensional nanoelectronic ICs, and to test these methods in the practice of semiconductor development. Six chapters describe the challenges for numerical simulation of nanoelectronic circuits and suggest model reduction methods for constituting equations. These include linear and nonlinear differential equations tailored to circuit equations and drift diffusion equations for semiconductor devices. The performance of these methods is illustrated with numerical experiments using real-world data. Readers will benefit from an up-to-date overview of the latest model reduction methods in computational nanoelectronics.

  15. Dimension reduction methods for microarray data: a review

    Directory of Open Access Journals (Sweden)

    Rabia Aziz

    2017-03-01

    Full Text Available Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data. Gene expression microarray data displays the maximum number of genes (features simultaneously at a molecular level with a very small number of samples. The copious numbers of genes are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, most of the times the majority of the genes are irrelevant or redundant to the learning task. It will deteriorate the learning accuracy and training speed as well as lead to the problem of overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for prediction and classification of disease. Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact on the various machine learning algorithms for classification and eliminate the remaining ones. This paper describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages. It also presents a review of numerous dimension reduction approaches for microarray data, mainly those methods that have been proposed over the past few years.

  16. AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES

    Directory of Open Access Journals (Sweden)

    L.V. Arun Shalin

    2016-01-01

    Full Text Available Clustering is a process of grouping elements together, designed in such a way that the elements assigned to similar data points in a cluster are more comparable to each other than the remaining data points in a cluster. During clustering certain difficulties related when dealing with high dimensional data are ubiquitous and abundant. Works concentrated using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction during the inclusion of non-binary databases. In this work we study methods for dimensionality reduction for non-binary database. By analyzing the behavior of dimensionality reduction for non-binary database, results in performance improvement with the help of tag based feature. An effective multi-clustering anonymization approach called Discrete Component Task Specific Multi-Clustering (DCTSM is presented for dimensionality reduction on non-binary database. To start with we present the analysis of attribute in the non-binary database and cluster projection identifies the sparseness degree of dimensions. Additionally with the quantum distribution on multi-cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided resulting in performance improvement on the basis of tag based feature. Multi-clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e. tag clusters. During training, the DCTSM approach uses multi-clusters instead of individual tag features and then during decoding individual features is replaced by corresponding multi-clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. Improved tag feature extraction and minimum error rate compared to conventional anonymization

  17. Apparent destruction of superconductivity in the disordered one-dimensional limit

    International Nuclear Information System (INIS)

    Graybeal, J.M.; Mankiewich, P.M.; Dynes, R.C.; Beasley, M.R.

    1987-01-01

    We present the results of a model-system study of the competition between superconductivity and disorder in narrow superconducting wires. As one moves from the two-dimensional regime toward the one-dimensional limit, large and systematic reductions in the superconducting transition temperature are obtained. The observed behavior extrapolates to the total destruction of superconductivity in the disordered one-dimensional limit. Our findings are in clear disagreement with a recent theoretical treatment. In addition, the superconducting fluctuations appear to be modified by disorder for the narrowest samples

  18. A polyacrylonitrile copolymer-silica template for three-dimensional hierarchical porous carbon as a Pt catalyst support for the oxygen reduction reaction.

    Science.gov (United States)

    Liu, Minmin; Li, Jian; Cai, Chao; Zhou, Ziwei; Ling, Yun; Liu, Rui

    2017-08-01

    Herein, we report a novel route to construct a hierarchical three-dimensional porous carbon (3DC) through a copolymer-silica assembly. In the synthesis, silica acts as a hard template and leads to the formation of an interconnected 3D macropore, whereas styrene-co-acrylonitrile polymer has been used as both a carbon source and a soft template for micro- and meso-pores. The obtained 3DC materials possess a large surface area (∼550.5 m 2 g -1 ), which facilitates high dispersion of Pt nanoparticles on the carbon support. The 3DC-supported Pt electrocatalyst shows excellent performance in the oxygen reduction reaction (ORR). The easy processing ability along with the characteristics of hierarchical porosity offers a new strategy for the preparation of carbon nanomaterials for energy application.

  19. Facile preparation of three-dimensional Co1-xS/sulfur and nitrogen-codoped graphene/carbon foam for highly efficient oxygen reduction reaction

    Science.gov (United States)

    Liang, Hui; Li, Chenwei; Chen, Tao; Cui, Liang; Han, Jingrui; Peng, Zhi; Liu, Jingquan

    2018-02-01

    Because of the urgent need for renewable resources, oxygen reduction reaction (ORR) has been widely studied. Finding efficient and low cost non-precious metal catalyst is increasingly critical. In this study, melamine foam is used as template to obtain porous sulfur and nitrogen-codoped graphene/carbon foam with uniformly distributed cobalt sulfide nanoparticles (Co1-xS/SNG/CF) which is prepared by a simple infiltration-drying-sulfuration method. It is noteworthy that melamine foam not only works as a three-dimensional support skeleton, but also provides a nitrogen source without any environmental pollution. Such Co1-xS/SNG/CF catalyst shows excellent oxygen reduction catalytic performance with an onset potential of only 0.99 V, which is the same as that of Pt/C catalyst (Eonset = 0.99 V). Furthermore, the stability and methanol tolerance of Co1-xS/SNG/CF are more outstanding than those of Pt/C catalyst. Our work manifests a facile method to prepare S and N-codoped 3D graphene network decorated with Co1-xS nanoparticles, which may be utilized as potential alternative to the expensive Pt/C catalysts toward ORR.

  20. ldr: An R Software Package for Likelihood-Based Su?cient Dimension Reduction

    Directory of Open Access Journals (Sweden)

    Kofi Placid Adragni

    2014-11-01

    Full Text Available In regression settings, a su?cient dimension reduction (SDR method seeks the core information in a p-vector predictor that completely captures its relationship with a response. The reduced predictor may reside in a lower dimension d < p, improving ability to visualize data and predict future observations, and mitigating dimensionality issues when carrying out further analysis. We introduce ldr, a new R software package that implements three recently proposed likelihood-based methods for SDR: covariance reduction, likelihood acquired directions, and principal fitted components. All three methods reduce the dimensionality of the data by pro jection into lower dimensional subspaces. The package also implements a variable screening method built upon principal ?tted components which makes use of ?exible basis functions to capture the dependencies between the predictors and the response. Examples are given to demonstrate likelihood-based SDR analyses using ldr, including estimation of the dimension of reduction subspaces and selection of basis functions. The ldr package provides a framework that we hope to grow into a comprehensive library of likelihood-based SDR methodologies.

  1. Posterior Reduction and Monosegmental Fusion with Intraoperative Three-dimensional Navigation System in the Treatment of High-grade Developmental Spondylolisthesis

    Directory of Open Access Journals (Sweden)

    Wei Tian

    2015-01-01

    Full Text Available Background: The treatment of high-grade developmental spondylolisthesis (HGDS is still challenging and controversial. In this study, we investigated the efficacy of the posterior reduction and monosegmental fusion assisted by intraoperative three-dimensional (3D navigation system in managing the HGDS. Methods: Thirteen consecutive HGDS patients were treated with posterior decompression, reduction and monosegmental fusion of L5/S1, assisted by intraoperative 3D navigation system. The clinical and radiographic outcomes were evaluated, with a minimum follow-up of 2 years. The differences between the pre- and post-operative measures were statistically analyzed using a two-tailed, paired t-test. Results: At most recent follow-up, 12 patients were pain-free. Only 1 patient had moderate pain. There were no permanent neurological complications or pseudarthrosis. The magnetic resonance imaging showed that there was no obvious disc degeneration in the adjacent segment. All radiographic parameters were improved. Mean slippage improved from 63.2% before surgery to 12.2% after surgery and 11.0% at latest follow-up. Lumbar lordosis changed from preoperative 34.9 ± 13.3° to postoperative 50.4 ± 9.9°, and 49.3 ± 7.8° at last follow-up. L5 incidence improved from 71.0 ± 11.3° to 54.0 ± 11.9° and did not change significantly at the last follow-up 53.1 ± 15.4°. While pelvic incidence remained unchanged, sacral slip significantly decreased from preoperative 32.7 ± 12.5° to postoperative 42.6 ± 9.8°and remained constant to the last follow-up 44.4 ± 6.9°. Pelvic tilt significantly decreased from 38.4 ± 12.5° to 30.9 ± 8.1° and remained unchanged at the last follow-up 28.1 ± 11.2°. Conclusions: Posterior reduction and monosegmental fusion of L5/S1 assisted by intraoperative 3D navigation are an effective technique for managing high-grade dysplastic spondylolisthesis. A complete reduction of local deformity and excellent correction of overall

  2. Integration of metabolomics and proteomics in molecular plant physiology--coping with the complexity by data-dimensionality reduction.

    Science.gov (United States)

    Weckwerth, Wolfram

    2008-02-01

    In recent years, genomics has been extended to functional genomics. Toward the characterization of organisms or species on the genome level, changes on the metabolite and protein level have been shown to be essential to assign functions to genes and to describe the dynamic molecular phenotype. Gas chromatography (GC) and liquid chromatography coupled to mass spectrometry (GC- and LC-MS) are well suited for the fast and comprehensive analysis of ultracomplex metabolite samples. For the integration of metabolite profiles with quantitative protein profiles, a high throughput (HTP) shotgun proteomics approach using LC-MS and label-free quantification of unique proteins in a complex protein digest is described. Multivariate statistics are applied to examine sample pattern recognition based on data-dimensionality reduction and biomarker identification in plant systems biology. The integration of the data reveal multiple correlative biomarkers providing evidence for an increase of information in such holistic approaches. With computational simulation of metabolic networks and experimental measurements, it can be shown that biochemical regulation is reflected by metabolite network dynamics measured in a metabolomics approach. Examples in molecular plant physiology are presented to substantiate the integrative approach.

  3. Model reduction of parametrized systems

    CERN Document Server

    Ohlberger, Mario; Patera, Anthony; Rozza, Gianluigi; Urban, Karsten

    2017-01-01

    The special volume offers a global guide to new concepts and approaches concerning the following topics: reduced basis methods, proper orthogonal decomposition, proper generalized decomposition, approximation theory related to model reduction, learning theory and compressed sensing, stochastic and high-dimensional problems, system-theoretic methods, nonlinear model reduction, reduction of coupled problems/multiphysics, optimization and optimal control, state estimation and control, reduced order models and domain decomposition methods, Krylov-subspace and interpolatory methods, and applications to real industrial and complex problems. The book represents the state of the art in the development of reduced order methods. It contains contributions from internationally respected experts, guaranteeing a wide range of expertise and topics. Further, it reflects an important effor t, carried out over the last 12 years, to build a growing research community in this field. Though not a textbook, some of the chapters ca...

  4. Three New (2+1)-dimensional Integrable Systems and Some Related Darboux Transformations

    Science.gov (United States)

    Guo, Xiu-Rong

    2016-06-01

    We introduce two operator commutators by using different-degree loop algebras of the Lie algebra A1, then under the framework of zero curvature equations we generate two (2+1)-dimensional integrable hierarchies, including the (2+1)-dimensional shallow water wave (SWW) hierarchy and the (2+1)-dimensional Kaup-Newell (KN) hierarchy. Through reduction of the (2+1)-dimensional hierarchies, we get a (2+1)-dimensional SWW equation and a (2+1)-dimensional KN equation. Furthermore, we obtain two Darboux transformations of the (2+1)-dimensional SWW equation. Similarly, the Darboux transformations of the (2+1)-dimensional KN equation could be deduced. Finally, with the help of the spatial spectral matrix of SWW hierarchy, we generate a (2+1) heat equation and a (2+1) nonlinear generalized SWW system containing inverse operators with respect to the variables x and y by using a reduction spectral problem from the self-dual Yang-Mills equations. Supported by the National Natural Science Foundation of China under Grant No. 11371361, the Shandong Provincial Natural Science Foundation of China under Grant Nos. ZR2012AQ011, ZR2013AL016, ZR2015EM042, National Social Science Foundation of China under Grant No. 13BJY026, the Development of Science and Technology Project under Grant No. 2015NS1048 and A Project of Shandong Province Higher Educational Science and Technology Program under Grant No. J14LI58

  5. Reduction of the N-component scalar model at the two-loop level

    International Nuclear Information System (INIS)

    Jakovac, A.

    1996-01-01

    Dimensional reduction of high temperature field theories improves IR features of their perturbative treatment. A crucial question is the following: What three-dimensional theory is representing the full system the most faithful way? A careful investigation of the induced three-dimensional counterterm structure of the finite temperature 4D O(N) symmetric scalar theory at the two-loop level leads to proposing the presence of nonlocal operators in the effective theory. A three-dimensional matching process is applied for the construction of the optimal local, superrenormalizable approximation. copyright 1996 The American Physical Society

  6. Two-dimensional field theory description of a disoriented chiral condensate

    International Nuclear Information System (INIS)

    Kogan, I.I.

    1993-01-01

    We consider the effective (1+1)-dimensional chiral theory describing fluctuations of the order parameter of the disoriented chiral condensate (DCC) which can be formed in the central rapidity region in relativistic nucleus-nucleus or nucleon-nucleon collisions at high energy. Using (1+1)-dimensional reduction of QCD at high energies and assuming spin polarization of the DDC one can find the Wess-Zumino-Novikov-Witten model at the level k=3 as the effective chiral theory for the one-dimensional DDC. Some possible phenomenological consequences are briefly discussed

  7. State-space dimensionality in short-memory hidden-variable theories

    International Nuclear Information System (INIS)

    Montina, Alberto

    2011-01-01

    Recently we have presented a hidden-variable model of measurements for a qubit where the hidden-variable state-space dimension is one-half the quantum-state manifold dimension. The absence of a short memory (Markov) dynamics is the price paid for this dimensional reduction. The conflict between having the Markov property and achieving the dimensional reduction was proved by Montina [A. Montina, Phys. Rev. A 77, 022104 (2008)] using an additional hypothesis of trajectory relaxation. Here we analyze in more detail this hypothesis introducing the concept of invertible process and report a proof that makes clearer the role played by the topology of the hidden-variable space. This is accomplished by requiring suitable properties of regularity of the conditional probability governing the dynamics. In the case of minimal dimension the set of continuous hidden variables is identified with an object living an N-dimensional Hilbert space whose dynamics is described by the Schroedinger equation. A method for generating the economical non-Markovian model for the qubit is also presented.

  8. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

    Directory of Open Access Journals (Sweden)

    Huanhuan Li

    2017-08-01

    Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our

  9. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.

    Science.gov (United States)

    Li, Huanhuan; Liu, Jingxian; Liu, Ryan Wen; Xiong, Naixue; Wu, Kefeng; Kim, Tai-Hoon

    2017-08-04

    The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with

  10. DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    OpenAIRE

    Cowley, Benjamin R.; Kaufman, Matthew T.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2012-01-01

    The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data)...

  11. Density prediction and dimensionality reduction of mid-term electricity demand in China: A new semiparametric-based additive model

    International Nuclear Information System (INIS)

    Shao, Zhen; Yang, Shan-Lin; Gao, Fei

    2014-01-01

    Highlights: • A new stationary time series smoothing-based semiparametric model is established. • A novel semiparametric additive model based on piecewise smooth is proposed. • We model the uncertainty of data distribution for mid-term electricity forecasting. • We provide efficient long horizon simulation and extraction for external variables. • We provide stable and accurate density predictions for mid-term electricity demand. - Abstract: Accurate mid-term electricity demand forecasting is critical for efficient electric planning, budgeting and operating decisions. Mid-term electricity demand forecasting is notoriously complicated, since the demand is subject to a range of external drivers, such as climate change, economic development, which will exhibit monthly, seasonal, and annual complex variations. Conventional models are based on the assumption that original data is stable and normally distributed, which is generally insignificant in explaining actual demand pattern. This paper proposes a new semiparametric additive model that, in addition to considering the uncertainty of the data distribution, includes practical discussions covering the applications of the external variables. To effectively detach the multi-dimensional volatility of mid-term demand, a novel piecewise smooth method which allows reduction of the data dimensionality is developed. Besides, a semi-parametric procedure that makes use of bootstrap algorithm for density forecast and model estimation is presented. Two typical cases in China are presented to verify the effectiveness of the proposed methodology. The results suggest that both meteorological and economic variables play a critical role in mid-term electricity consumption prediction in China, while the extracted economic factor is adequate to reveal the potentially complex relationship between electricity consumption and economic fluctuation. Overall, the proposed model can be easily applied to mid-term demand forecasting, and

  12. Low-dimensional gravities as gauge theories with non-compact groups

    International Nuclear Information System (INIS)

    Cangeni, D.

    1993-01-01

    In another note presented in these Proceedings it is shown that the two main lineal gravities can be given a gauge formulation. If it is already known that one of them the Sitter model - is a dimensional reduction of a Chern-Simons model in (2+1) dimensions, it was not clear whether the other one - the extended Poincare model follows from a similar reduction. The purpose of this note is to show that this is indeed the case provide we start in 2+1 dimensions with an extension ISO(2,1) of the Poincare groups as gauge group of a Chern-Simons model. We first show that this model gives a new proposal for gravity in 2*1 dimensions, since we get classically the Einstein's equations. Performing then a dimensional reduction, we recover not only the extended Poincare model but also the de Sitter one; hence, both lineal gravities get unified in the reduced model. (Author) 6 refs

  13. The (2+1)-dimensional nonisospectral relativistic Toda hierarchy related to the generalized discrete Painleve hierarchy

    International Nuclear Information System (INIS)

    Zhu Zuonong

    2007-01-01

    In this paper, we will concentrate on the topic of integrable discrete hierarchies in 2+1 dimensions, and their connection with discrete Painleve hierarchies. By considering a (2+1)-dimensional nonisospectral discrete linear problem, two new (2+1)-dimensional nonisospectral integrable lattice hierarchies-the 2+1 nonisospectral relativistic Toda lattice hierarchy and the 2+1 nonisospectral negative relativistic Toda lattice hierarchy-are constructed. It is shown that the reductions of the two new 2+1 nonisospectral lattice hierarchies lead to the (2+1)-dimensional nonisospectral Volterra lattice hierarchy and the (2+1)-dimensional nonisospectral negative Volterra lattice hierarchy. We also obtain two new (1+1)-dimensional nonisospectral integrable lattice hierarchies and two new ordinary difference hierarchies which are direct reductions of the two 2+1 nonisospectral integrable lattice hierarchies. One of the two difference hierarchies yields our previously obtained generalized discrete first Painleve (dP I ) hierarchy and another one yields a generalized alternative discrete second Painleve (alt-dP II ) hierarchy

  14. Sharpening the weak gravity conjecture with dimensional reduction

    International Nuclear Information System (INIS)

    Heidenreich, Ben; Reece, Matthew; Rudelius, Tom

    2016-01-01

    We investigate the behavior of the Weak Gravity Conjecture (WGC) under toroidal compactification and RG flows, finding evidence that WGC bounds for single photons become weaker in the infrared. By contrast, we find that a photon satisfying the WGC will not necessarily satisfy it after toroidal compactification when black holes charged under the Kaluza-Klein photons are considered. Doing so either requires an infinite number of states of different charges to satisfy the WGC in the original theory or a restriction on allowed compactification radii. These subtleties suggest that if the Weak Gravity Conjecture is true, we must seek a stronger form of the conjecture that is robust under compactification. We propose a “Lattice Weak Gravity Conjecture” that meets this requirement: a superextremal particle should exist for every charge in the charge lattice. The perturbative heterotic string satisfies this conjecture. We also use compactification to explore the extent to which the WGC applies to axions. We argue that gravitational instanton solutions in theories of axions coupled to dilaton-like fields are analogous to extremal black holes, motivating a WGC for axions. This is further supported by a match between the instanton action and that of wrapped black branes in a higher-dimensional UV completion.

  15. Three-dimensional laparoscopy vs 2-dimensional laparoscopy with high-definition technology for abdominal surgery

    DEFF Research Database (Denmark)

    Fergo, Charlotte; Burcharth, Jakob; Pommergaard, Hans-Christian

    2017-01-01

    BACKGROUND: This systematic review investigates newer generation 3-dimensional (3D) laparoscopy vs 2-dimensional (2D) laparoscopy in terms of error rating, performance time, and subjective assessment as early comparisons have shown contradictory results due to technological shortcomings. DATA...... Central Register of Controlled Trials database. CONCLUSIONS: Of 643 articles, 13 RCTs were included, of which 2 were clinical trials. Nine of 13 trials (69%) and 10 of 13 trials (77%) found a significant reduction in performance time and error, respectively, with the use of 3D-laparoscopy. Overall, 3D......-laparoscopy was found to be superior or equal to 2D-laparoscopy. All trials featuring subjective evaluation found a superiority of 3D-laparoscopy. More clinical RCTs are still awaited for the convincing results to be reproduced....

  16. In-situ trainable intrusion detection system

    Energy Technology Data Exchange (ETDEWEB)

    Symons, Christopher T.; Beaver, Justin M.; Gillen, Rob; Potok, Thomas E.

    2016-11-15

    A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

  17. Towards realistic models from Higher-Dimensional theories with Fuzzy extra dimensions

    CERN Document Server

    Gavriil, D.; Zoupanos, G.

    2014-01-01

    We briefly review the Coset Space Dimensional Reduction (CSDR) programme and the best model constructed so far and then we present some details of the corresponding programme in the case that the extra dimensions are considered to be fuzzy. In particular, we present a four-dimensional $\\mathcal{N} = 4$ Super Yang Mills Theory, orbifolded by $\\mathbb{Z}_3$, which mimics the behaviour of a dimensionally reduced $\\mathcal{N} = 1$, 10-dimensional gauge theory over a set of fuzzy spheres at intermediate high scales and leads to the trinification GUT $SU(3)^3$ at slightly lower, which in turn can be spontaneously broken to the MSSM in low scales.

  18. Nonnegative Matrix Factorization with Rank Regularization and Hard Constraint.

    Science.gov (United States)

    Shang, Ronghua; Liu, Chiyang; Meng, Yang; Jiao, Licheng; Stolkin, Rustam

    2017-09-01

    Nonnegative matrix factorization (NMF) is well known to be an effective tool for dimensionality reduction in problems involving big data. For this reason, it frequently appears in many areas of scientific and engineering literature. This letter proposes a novel semisupervised NMF algorithm for overcoming a variety of problems associated with NMF algorithms, including poor use of prior information, negative impact on manifold structure of the sparse constraint, and inaccurate graph construction. Our proposed algorithm, nonnegative matrix factorization with rank regularization and hard constraint (NMFRC), incorporates label information into data representation as a hard constraint, which makes full use of prior information. NMFRC also measures pairwise similarity according to geodesic distance rather than Euclidean distance. This results in more accurate measurement of pairwise relationships, resulting in more effective manifold information. Furthermore, NMFRC adopts rank constraint instead of norm constraints for regularization to balance the sparseness and smoothness of data. In this way, the new data representation is more representative and has better interpretability. Experiments on real data sets suggest that NMFRC outperforms four other state-of-the-art algorithms in terms of clustering accuracy.

  19. Experiments with three-dimensional riblets as an idealized model of shark skin

    Energy Technology Data Exchange (ETDEWEB)

    Bechert, D.W.; Bruse, M.; Hage, W. [DLR Deutsches Zentrum fuer Luft- und Raumfahrt e.V., Berlin (Germany). Dept. of Turbulence Res.

    2000-05-01

    The skin of fast sharks exhibits a rather intriguing three-dimensional rib pattern. Therefore, the question arises whether or not such three-dimensional riblet surfaces may produce an equivalent or even higher drag reduction than straight two-dimensional riblets. Previously, the latter have been shown to reduce turbulent wall shear stress by up to 10%. Hence, the drag reduction by three-dimensional riblet surfaces is investigated experimentally. Our idealized 3D-surface consists of sharp-edged fin-shaped elements arranged in an interlocking array. The turbulent wall shear stress on this surface is measured using direct force balances. In a first attempt, wind tunnel experiments with about 365000 tiny fin elements per test surface have been carried out. Due to the complexity of the surface manufacturing process, a comprehensive parametric study was not possible. These initial wind tunnel data, however, hinted at an appreciable drag reduction. Subsequently, in order to have a better judgement on the potential of these 3D-surfaces, oil channel experiments are carried out. In our new oil channel, the geometrical dimensions of the fins can be magnified 10 times in size as compared to the initial wind tunnel experiments, i.e., from typically 0.5 mm to 5 mm. For these latter oil channel experiments, novel test plates with variable fin configuration have been manufactured, with 1920-4000 fins. This enhanced variability permits measurements with a comparatively large parameter range. As a result of our measurements, it can be concluded, that 3D-riblet surfaces do indeed produce an appreciable drag reduction. We found as much as 7.3% decreased turbulent shear stress, as compared to a smooth reference plate.

  20. A supersymmetric reduction on the three-sphere

    International Nuclear Information System (INIS)

    Deger, Nihat Sadik; Samtleben, Henning; Sarıoğlu, Özgür; Van den Bleeken, Dieter

    2015-01-01

    We present the embedding of three-dimensional SO(4)⋉R 6 gauged N=4 supergravity with quaternionic target space SO(4,4)/(SO(4)×SO(4)) into D=6, N=(1,0) supergravity coupled to a single chiral tensor multiplet through a consistent reduction on AdS 3 ×S 3

  1. Chronoprojective invariance of the five-dimensional Schroedinger formalism

    International Nuclear Information System (INIS)

    Perrin, M.; Burdet, G.; Duval, C.

    1984-10-01

    Invariance properties of the five-dimensional Schroedinger formalism describing a quantum test particle in the Newton-Cartan theory of gravitation are studied. The geometry which underlies these invariance properties is presented as a reduction of the 0(5,2) conformal geometry various applications are given

  2. A two-dimensional lattice equation as an extension of the Heideman-Hogan recurrence

    Science.gov (United States)

    Kamiya, Ryo; Kanki, Masataka; Mase, Takafumi; Tokihiro, Tetsuji

    2018-03-01

    We consider a two dimensional extension of the so-called linearizable mappings. In particular, we start from the Heideman-Hogan recurrence, which is known as one of the linearizable Somos-like recurrences, and introduce one of its two dimensional extensions. The two dimensional lattice equation we present is linearizable in both directions, and has the Laurent and the coprimeness properties. Moreover, its reduction produces a generalized family of the Heideman-Hogan recurrence. Higher order examples of two dimensional linearizable lattice equations related to the Dana Scott recurrence are also discussed.

  3. Dispersion in two dimensional channels—the Fick-Jacobs approximation revisited

    Science.gov (United States)

    Mangeat, M.; Guérin, T.; Dean, D. S.

    2017-12-01

    We examine the dispersion of Brownian particles in a symmetric two dimensional channel, this classical problem has been widely studied in the literature using the so called Fick-Jacobs’ approximation and its various improvements. Most studies rely on the reduction to an effective one dimensional diffusion equation, here we derive an explicit formula for the diffusion constant which avoids this reduction. Using this formula the effective diffusion constant can be evaluated numerically without resorting to Brownian simulations. In addition, a perturbation theory can be developed in \\varepsilon = h_0/L where h 0 is the characteristic channel height and L the period. This perturbation theory confirms the results of Kalinay and Percus (2006 Phys. Rev. E 74 041203), based on the reduction, to one dimensional diffusion are exact at least to {{ O}}(\\varepsilon^6) . Furthermore, we show how the Kalinay and Percus pseudo-linear approximation can be straightforwardly recovered. The approach proposed here can also be exploited to yield exact results in the limit \\varepsilon \\to ∞ , we show that here the diffusion constant remains finite and show how the result can be obtained with a simple physical argument. Moreover, we show that the correction to the effective diffusion constant is of order 1/\\varepsilon and remarkably has some universal characteristics. Numerically we compare the analytic results obtained with exact numerical calculations for a number of interesting channel geometries.

  4. Quantum transport in d -dimensional lattices

    International Nuclear Information System (INIS)

    Manzano, Daniel; Chuang, Chern; Cao, Jianshu

    2016-01-01

    We show that both fermionic and bosonic uniform d -dimensional lattices can be reduced to a set of independent one-dimensional chains. This reduction leads to the expression for ballistic energy fluxes in uniform fermionic and bosonic lattices. By the use of the Jordan–Wigner transformation we can extend our analysis to spin lattices, proving the coexistence of both ballistic and non-ballistic subspaces in any dimension and for any system size. We then relate the nature of transport to the number of excitations in the homogeneous spin lattice, indicating that a single excitation always propagates ballistically and that the non-ballistic behaviour of uniform spin lattices is a consequence of the interaction between different excitations. (paper)

  5. Extended inflation from higher-dimensional theories

    International Nuclear Information System (INIS)

    Holman, R.; Kolb, E.W.; Vadas, S.L.; Wang, Y.

    1991-01-01

    We consider the possibility that higher-dimensional theories may, upon reduction to four dimensions, allow extended inflation to occur. We analyze two separate models. One is a very simple toy model consisting of higher-dimensional gravity coupled to a scalar field whose potential allows for a first-order phase transition. The other is a more sophisticated model incorporating the effects of nontrivial field configurations (monopole, Casimir, and fermion bilinear condensate effects) that yield a nontrivial potential for the radius of the internal space. We find that extended inflation does not occur in these models. We also find that the bubble nucleation rate in these theories is time dependent unlike the case in the original version of extended inflation

  6. Algorithm for statistical noise reduction in three-dimensional ion implant simulations

    International Nuclear Information System (INIS)

    Hernandez-Mangas, J.M.; Arias, J.; Jaraiz, M.; Bailon, L.; Barbolla, J.

    2001-01-01

    As integrated circuit devices scale into the deep sub-micron regime, ion implantation will continue to be the primary means of introducing dopant atoms into silicon. Different types of impurity profiles such as ultra-shallow profiles and retrograde profiles are necessary for deep submicron devices in order to realize the desired device performance. A new algorithm to reduce the statistical noise in three-dimensional ion implant simulations both in the lateral and shallow/deep regions of the profile is presented. The computational effort in BCA Monte Carlo ion implant simulation is also reduced

  7. Dixon-Souriau equations from a 5-dimensional spinning particle in a Kaluza-Klein framework

    International Nuclear Information System (INIS)

    Cianfrani, F.; Milillo, I.; Montani, G.

    2007-01-01

    The dimensional reduction of Papapetrou equations is performed in a 5-dimensional Kaluza-Klein background and Dixon-Souriau results for the motion of a charged spinning body are obtained. The splitting provides an electric dipole moment, and, for elementary particles, the induced parity and time-reversal violations are explained

  8. On reduction and exact solutions of nonlinear many-dimensional Schroedinger equations

    International Nuclear Information System (INIS)

    Barannik, A.F.; Marchenko, V.A.; Fushchich, V.I.

    1991-01-01

    With the help of the canonical decomposition of an arbitrary subalgebra of the orthogonal algebra AO(n) the rank n and n-1 maximal subalgebras of the extended isochronous Galileo algebra, the rank n maximal subalgebras of the generalized extended classical Galileo algebra AG(a,n) the extended special Galileo algebra AG(2,n) and the extended whole Galileo algebra AG(3,n) are described. By using the rank n subalgebras, ansatze reducing the many dimensional Schroedinger equations to ordinary differential equations is found. With the help of the reduced equation solutions exact solutions of the Schroedinger equation are considered

  9. Two-dimensional filtering of SPECT images using the Metz and Wiener filters

    International Nuclear Information System (INIS)

    King, M.A.; Schwinger, R.B.; Penney, B.C.; Doherty, P.W.

    1984-01-01

    Presently, single photon emission computed tomographic (SPECT) images are usually reconstructed by arbitrarily selecting a one-dimensional ''window'' function for use in reconstruction. A better method would be to automatically choose among a family of two-dimensional image restoration filters in such a way as to produce ''optimum'' image quality. Two-dimensional image processing techniques offer the advantages of a larger statistical sampling of the data for better noise reduction, and two-dimensional image deconvolution to correct for blurring during acquisition. An investigation of two such ''optimal'' digital image restoration techniques (the count-dependent Metz filter and the Wiener filter) was made. They were applied both as two-dimensional ''window'' functions for preprocessing SPECT images, and for filtering reconstructed images. Their performance was compared by measuring image contrast and per cent fractional standard deviation (% FSD) in multiple-acquisitions of the Jaszczak SPECT phantom at two different count levels. A statistically significant increase in image contrast and decrease in % FSD was observed with these techniques when compared to the results of reconstruction with a ramp filter. The adaptability of the techniques was manifested in a lesser % reduction in % FSD at the high count level coupled with a greater enhancement in image contrast. Using an array processor, processing time was 0.2 sec per image for the Metz filter and 3 sec for the Wiener filter. It is concluded that two-dimensional digital image restoration with these techniques can produce a significant increase in SPECT image quality

  10. Learning with partially labeled and interdependent data

    CERN Document Server

    Amini, Massih-Reza

    2015-01-01

    This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus d

  11. Extended inflation from higher dimensional theories

    International Nuclear Information System (INIS)

    Holman, R.; Kolb, E.W.; Vadas, S.L.; Wang, Yun.

    1990-04-01

    The possibility is considered that higher dimensional theories may, upon reduction to four dimensions, allow extended inflation to occur. Two separate models are analayzed. One is a very simple toy model consisting of higher dimensional gravity coupled to a scalar field whose potential allows for a first-order phase transition. The other is a more sophisticated model incorporating the effects of non-trivial field configurations (monopole, Casimir, and fermion bilinear condensate effects) that yield a non-trivial potential for the radius of the internal space. It was found that extended inflation does not occur in these models. It was also found that the bubble nucleation rate in these theories is time dependent unlike the case in the original version of extended inflation

  12. Instability in near-horizon geometries of even-dimensional Myers–Perry black holes

    International Nuclear Information System (INIS)

    Tanahashi, Norihiro; Murata, Keiju

    2012-01-01

    We study the gravitational, electromagnetic and scalar field perturbations on the near-horizon geometries of the even-dimensional extremal Myers–Perry black holes. By dimensional reduction, the perturbation equations are reduced to effective equations of motion in AdS 2 . We find that some modes in the gravitational perturbations violate the Breitenlöhner–Freedman bound in AdS 2 . This result suggests that the even-dimensional (near-)extremal Myers–Perry black holes are unstable against gravitational perturbations. We also discuss implications of our results to the Kerr–CFT correspondence. (paper)

  13. Evaluation of Orthopedic Metal Artifact Reduction Application in Three-Dimensional Computed Tomography Reconstruction of Spinal Instrumentation: A Single Saudi Center Experience.

    Science.gov (United States)

    Ali, Amir Monir

    2018-01-01

    The aim of the study was to evaluate the commercially available orthopedic metal artifact reduction (OMAR) technique in postoperative three-dimensional computed tomography (3DCT) reconstruction studies after spinal instrumentation and to investigate its clinical application. One hundred and twenty (120) patients with spinal metallic implants were included in the study. All had 3DCT reconstruction examinations using the OMAR software after obtaining the informed consents and approval of the Institution Ethical Committee. The degree of the artifacts, the related muscular density, the clearness of intermuscular fat planes, and definition of the adjacent vertebrae were qualitatively evaluated. The diagnostic satisfaction and quality of the 3D reconstruction images were thoroughly assessed. The majority (96.7%) of 3DCT reconstruction images performed were considered satisfactory to excellent for diagnosis. Only 3.3% of the reconstructed images had rendered unacceptable diagnostic quality. OMAR can effectively reduce metallic artifacts in patients with spinal instrumentation with highly diagnostic 3DCT reconstruction images.

  14. Fast Multiscale Reservoir Simulations using POD-DEIM Model Reduction

    KAUST Repository

    Ghasemi, Mohammadreza

    2015-02-23

    In this paper, we present a global-local model reduction for fast multiscale reservoir simulations in highly heterogeneous porous media with applications to optimization and history matching. Our proposed approach identifies a low dimensional structure of the solution space. We introduce an auxiliary variable (the velocity field) in our model reduction that allows achieving a high degree of model reduction. The latter is due to the fact that the velocity field is conservative for any low-order reduced model in our framework. Because a typical global model reduction based on POD is a Galerkin finite element method, and thus it can not guarantee local mass conservation. This can be observed in numerical simulations that use finite volume based approaches. Discrete Empirical Interpolation Method (DEIM) is used to approximate the nonlinear functions of fine-grid functions in Newton iterations. This approach allows achieving the computational cost that is independent of the fine grid dimension. POD snapshots are inexpensively computed using local model reduction techniques based on Generalized Multiscale Finite Element Method (GMsFEM) which provides (1) a hierarchical approximation of snapshot vectors (2) adaptive computations by using coarse grids (3) inexpensive global POD operations in a small dimensional spaces on a coarse grid. By balancing the errors of the global and local reduced-order models, our new methodology can provide an error bound in simulations. Our numerical results, utilizing a two-phase immiscible flow, show a substantial speed-up and we compare our results to the standard POD-DEIM in finite volume setup.

  15. Quantum fluctuations and spontaneous compactification of eleven-dimensional gravity

    International Nuclear Information System (INIS)

    Nguen Van Hieu.

    1985-01-01

    The reduction of the eleven-dimensional pure gravity to the field theory in the four-dimensional Minkowski space-time by means of the spontaneous compactification of the extra dimensions is investigated. The contribution of the quantum fluctuations of the eleven-dimen-- sonal second rank symmetric tensor field to the curvatures of the space-time and the compactified space of the extra dimensions are calculated in the one-loop approximation. It is shown that there exist the values of the cosmological constant for which tachions are absent. As a result, self-consistent quantum field theory is obtained in spontaneous compactified Minkowski space M 4 xS 7 ,is where M 4 is Minkowski space-time, and S 7 is seven-dimensional sphere

  16. Isostable reduction with applications to time-dependent partial differential equations.

    Science.gov (United States)

    Wilson, Dan; Moehlis, Jeff

    2016-07-01

    Isostables and isostable reduction, analogous to isochrons and phase reduction for oscillatory systems, are useful in the study of nonlinear equations which asymptotically approach a stationary solution. In this work, we present a general method for isostable reduction of partial differential equations, with the potential power to reduce the dimensionality of a nonlinear system from infinity to 1. We illustrate the utility of this reduction by applying it to two different models with biological relevance. In the first example, isostable reduction of the Fokker-Planck equation provides the necessary framework to design a simple control strategy to desynchronize a population of pathologically synchronized oscillatory neurons, as might be relevant to Parkinson's disease. Another example analyzes a nonlinear reaction-diffusion equation with relevance to action potential propagation in a cardiac system.

  17. Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference [version 1; referees: 1 approved, 2 approved with reservations

    Directory of Open Access Journals (Sweden)

    Fanny Perraudeau

    2017-07-01

    Full Text Available Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1 dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2 cell clustering using resampling-based sequential ensemble clustering; (3 inference of cell lineages and pseudotimes; and (4 differential expression analysis along lineages.

  18. Giant Andreev backscattering and reentrant resistance in a 2- dimensional electron gas coupled to superconductors

    NARCIS (Netherlands)

    den Hartog, Sander; Wees, B.J. van; Nazarov, Yu.V.; Klapwijk, T.M.; Borghs, G.

    1998-01-01

    We have investigated the superconducting-phase modulated reduction in the resistance of a ballistic quantum point contact (QPC) connected via a disordered 2-dimensional electron gas (2DEG) to superconductors. We show that this reduction is caused by coherent Andreev back scattering of holes through

  19. Big Data, Biostatistics and Complexity Reduction

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2018-01-01

    Roč. 14, č. 2 (2018), s. 24-32 ISSN 1801-5603 R&D Projects: GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Biostatistics * Big data * Multivariate statistics * Dimensionality * Variable selection Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) https://www.ejbi.org/scholarly-articles/big-data-biostatistics-and-complexity-reduction.pdf

  20. Five-dimensional Nernst branes from special geometry

    Energy Technology Data Exchange (ETDEWEB)

    Dempster, P.; Errington, D. [Department of Mathematical Sciences, University of LiverpoolPeach Street, Liverpool L69 7ZL (United Kingdom); Gutowski, J. [Department of Mathematics, University of Surrey,Guildford, GU2 7XH (United Kingdom); Mohaupt, T. [Department of Mathematical Sciences, University of LiverpoolPeach Street, Liverpool L69 7ZL (United Kingdom)

    2016-11-21

    We construct Nernst brane solutions, that is black branes with zero entropy density in the extremal limit, of FI-gauged minimal five-dimensional supergravity coupled to an arbitrary number of vector multiplets. While the scalars take specific constant values and dynamically determine the value of the cosmological constant in terms of the FI-parameters, the metric takes the form of a boosted AdS Schwarzschild black brane. This metric can be brought to the Carter-Novotný-Horský form that has previously been observed to occur in certain limits of boosted D3-branes. By dimensional reduction to four dimensions we recover the four-dimensional Nernst branes of arXiv:1501.07863 and show how the five-dimensional lift resolves all their UV singularities. The dynamics of the compactification circle, which expands both in the UV and in the IR, plays a crucial role. At asymptotic infinity, the curvature singularity of the four-dimensional metric and the run-away behaviour of the four-dimensional scalar combine in such a way that the lifted solution becomes asymptotic to AdS{sub 5}. Moreover, the existence of a finite chemical potential in four dimensions is related to fact that the compactification circle has a finite minimal value. While it is not clear immediately how to embed our solutions into string theory, we argue that the same type of dictionary as proposed for boosted D3-branes should apply, although with a lower amount of supersymmetry.

  1. A Robust Supervised Variable Selection for Noisy High-Dimensional Data

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Schlenker, Anna

    2015-01-01

    Roč. 2015, Article 320385 (2015), s. 1-10 ISSN 2314-6133 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : dimensionality reduction * variable selection * robustness Subject RIV: BA - General Mathematics Impact factor: 2.134, year: 2015

  2. Understanding decay resistance, dimensional stability and strength changes in heat treated and acetylated wood

    Science.gov (United States)

    Roger M. Rowell; Rebecca E. Ibach; James McSweeny; Thomas Nilsson

    2009-01-01

    Reductions in hygroscopicity, increased dimensional stability and decay resistance of heat-treated wood depend on decomposition of a large portion of the hemicelluloses in the wood cell wall. In theory, these hemicelluloses are converted to small organic molecules, water and volatile furan-type intermediates that can polymerize in the cell wall. Reductions in...

  3. Space-time least-squares Petrov-Galerkin projection in nonlinear model reduction.

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Youngsoo [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Carlberg, Kevin Thomas [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.

    2017-09-01

    Our work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply Petrov-Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over all space and time in a weighted ℓ2-norm. This norm can be de ned to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.

  4. Reentrant resistance and giant Andreev back scattering in a two-dimensional electron gas coupled to superconductors

    NARCIS (Netherlands)

    den Hartog, Sander; Wees, B.J. van; Nazarov, Yu.V.; Klapwijk, T.M.; Borghs, G.

    1998-01-01

    We first present the bias-voltage dependence of the superconducting phase-dependent reduction in the differential resistance of a disordered T-shaped two-dimensional electron gas (2DEG) coupled to two superconductors. This reduction exhibits a reentrant behavior, since it first increases upon

  5. Symmetry Analysis and Exact Solutions of (2+1)-Dimensional Sawada-Kotera Equation

    International Nuclear Information System (INIS)

    Zhi Hongyan; Zhang Hongqing

    2008-01-01

    Based on the symbolic computation system Maple, the infinite-dimensional symmetry group of the (2+1)-dimensional Sawada-Kotera equation is found by the classical Lie group method and the characterization of the group properties is given. The symmetry groups are used to perform the symmetry reduction. Moreover, with Lou's direct method that is based on Lax pairs, we obtain the symmetry transformations of the Sawada-Kotera and Konopelchenko-Dubrovsky equations, respectively.

  6. Reduction theories elucidate the origins of complex biological rhythms generated by interacting delay-induced oscillations.

    Directory of Open Access Journals (Sweden)

    Ikuhiro Yamaguchi

    Full Text Available Time delay is known to induce sustained oscillations in many biological systems such as electroencephalogram (EEG activities and gene regulations. Furthermore, interactions among delay-induced oscillations can generate complex collective rhythms, which play important functional roles. However, due to their intrinsic infinite dimensionality, theoretical analysis of interacting delay-induced oscillations has been limited. Here, we show that the two primary methods for finite-dimensional limit cycles, namely, the center manifold reduction in the vicinity of the Hopf bifurcation and the phase reduction for weak interactions, can successfully be applied to interacting infinite-dimensional delay-induced oscillations. We systematically derive the complex Ginzburg-Landau equation and the phase equation without delay for general interaction networks. Based on the reduced low-dimensional equations, we demonstrate that diffusive (linearly attractive coupling between a pair of delay-induced oscillations can exhibit nontrivial amplitude death and multimodal phase locking. Our analysis provides unique insights into experimentally observed EEG activities such as sudden transitions among different phase-locked states and occurrence of epileptic seizures.

  7. Reduction of the dimensionality and comparative analysis of multivariate radiological data

    International Nuclear Information System (INIS)

    Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

    2009-01-01

    Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of 238 U, 226 Ra or 232 Th, combined with the concentration of 40 K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

  8. BioXTAS RAW, a software program for high-throughput automated small-angle X-ray scattering data reduction and preliminary analysis

    DEFF Research Database (Denmark)

    Nielsen, S.S.; Toft, K.N.; Snakenborg, Detlef

    2009-01-01

    A fully open source software program for automated two-dimensional and one-dimensional data reduction and preliminary analysis of isotropic small-angle X-ray scattering (SAXS) data is presented. The program is freely distributed, following the open-source philosophy, and does not rely on any...... commercial software packages. BioXTAS RAW is a fully automated program that, via an online feature, reads raw two-dimensional SAXS detector output files and processes and plots data as the data files are created during measurement sessions. The software handles all steps in the data reduction. This includes...... mask creation, radial averaging, error bar calculation, artifact removal, normalization and q calibration. Further data reduction such as background subtraction and absolute intensity scaling is fast and easy via the graphical user interface. BioXTAS RAW also provides preliminary analysis of one...

  9. Dimensional Reduction of N=1, E_8 SYM over SU(3)/U(1) x U(1) x Z_3 and its four-dimensional effective action

    CERN Document Server

    Irges, Nikos; Zoupanos, George

    2011-01-01

    We present an extension of the Standard Model inspired by the E_8 x E_8 Heterotic String. In order that a reasonable effective Lagrangian is presented we neglect everything else other than the ten-dimensional N=1 supersymmetric Yang-Mills sector associated with one of the gauge factors and certain couplings necessary for anomaly cancellation. We consider a compactified space-time M_4 x B_0 / Z_3, where B_0 is the nearly-Kaehler manifold SU(3)/U(1) x U(1) and Z_3 is a freely acting discrete group on B_0. Then we reduce dimensionally the E_8 on this manifold and we employ the Wilson flux mechanism leading in four dimensions to an SU(3)^3 gauge theory with the spectrum of a N=1 supersymmetric theory. We compute the effective four-dimensional Lagrangian and demonstrate that an extension of the Standard Model is obtained with interesting features including a conserved baryon number and fixed tree level Yukawa couplings and scalar potential. The spectrum contains new states such as right handed neutrinos and heavy ...

  10. Symmetry Reduction and Cauchy Problems for a Class of Fourth-Order Evolution Equations

    International Nuclear Information System (INIS)

    Li Jina; Zhang Shunli

    2008-01-01

    We exploit higher-order conditional symmetry to reduce initial-value problems for evolution equations to Cauchy problems for systems of ordinary differential equations (ODEs). We classify a class of fourth-order evolution equations which admit certain higher-order generalized conditional symmetries (GCSs) and give some examples to show the main reduction procedure. These reductions cannot be derived within the framework of the standard Lie approach, which hints that the technique presented here is something essential for the dimensional reduction of evolution equations

  11. A reduction method for phase equilibrium calculations with cubic equations of state

    Directory of Open Access Journals (Sweden)

    D. V. Nichita

    2006-09-01

    Full Text Available In this work we propose a new reduction method for phase equilibrium calculations using a general form of cubic equations of state (CEOS. The energy term in the CEOS is a quadratic form, which is diagonalized by applying a linear transformation. The number of the reduction parameters is related to the rank of the matrix C with elements (1-Cij, where Cij denotes the binary interaction parameters (BIPs. The dimensionality of the problem depends only on the number of reduction parameters, and is independent of the number of components in the mixture.

  12. Investigating reduction of dimensionality during single-joint elbow movements: a case study on muscle synergies

    Directory of Open Access Journals (Sweden)

    Enrico eChiovetto

    2013-02-01

    Full Text Available A long standing hypothesis in the neuroscience community is that the CNS generates the muscle activities to accomplish movements by combining a relatively small number of stereotyped patterns of muscle activations, often referred to as muscle synergies. Different definitions of synergies have been given in the literature. The most well-known are those of synchronous, time-varying and temporal muscle synergies. Each one of them is based on a different mathematical model used to factor some EMG array recordings collected during the execution of variety of motor tasks into a well-determined spatial, temporal or spatio-temporal organization. This plurality of definitions and their separate application to complex tasks have so far complicated the comparison and interpretation of the results obtained across studies, and it has always remained unclear why and when one synergistic decomposition should be preferred to another one. By using well-understood motor tasks such as elbow flexions and extensions, we aimed in this study to clarify better what are the motor features characterized by each kind of decomposition and to assess whether, when and why one of them should be preferred to the others. We found that three temporal synergies, each one of them accounting for specific temporal phases of the movements could account for the majority of the data variation. Similar performances could be achieved by two synchronous synergies, encoding the agonist-antagonist nature of the two muscles considered, and by two time-varying muscle synergies, encoding each one a task-related feature of the elbow movements, specifically their direction. Our findings support the notion that each EMG decomposition provides a set of well-interpretable muscle synergies, identifying reduction of dimensionality in different aspects of the movements. Taken together, our findings suggest that all decompositions are not equivalent and may imply different neurophysiological substrates

  13. Effective Hamiltonian for 2-dimensional arbitrary spin Ising model

    International Nuclear Information System (INIS)

    Sznajd, J.; Polska Akademia Nauk, Wroclaw. Inst. Niskich Temperatur i Badan Strukturalnych)

    1983-08-01

    The method of the reduction of the generalized arbitrary-spin 2-dimensional Ising model to spin-half Ising model is presented. The method is demonstrated in detail by calculating the effective interaction constants to the third order in cumulant expansion for the triangular spin-1 Ising model (the Blume-Emery-Griffiths model). (author)

  14. Model reduction for circuit simulation

    CERN Document Server

    Hinze, Michael; Maten, E Jan W Ter

    2011-01-01

    Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies require the use of refined mathematical models, and to take into account secondary, parasitic effects. This leads to very high dimensional problems which nowadays require simulation times too large for the short time-to-market demands in industry. Modern Model Order Reduction (MOR) techniques present a way out of this dilemma in providing surrogate models which keep the main characteristics of the devi

  15. How to Reduce Dimensionality of Data: Robustness Point of View

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Rensová, D.

    2015-01-01

    Roč. 10, č. 1 (2015), s. 131-140 ISSN 1452-4864 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : data analysis * dimensionality reduction * robust statistics * principal component analysis * robust classification analysis Subject RIV: BB - Applied Statistics, Operational Research

  16. An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

    Directory of Open Access Journals (Sweden)

    Jun Huang

    2014-01-01

    Full Text Available We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA and linear discriminant analysis (LDA. Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while the K nearest neighbor (KNN is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

  17. Coset space dimension reduction of gauge theories

    International Nuclear Information System (INIS)

    Farakos, K.; Kapetanakis, D.; Koutsoumbas, G.; Zoupanos, G.

    1989-01-01

    A very interesting approach in the attempts to unify all the interactions is to consider that a unification takes place in higher than four dimensions. The most ambitious program based on the old Kaluza-Klein idea is not able to reproduce the low energy chiral nature of the weak interactions. A suggested way out was the introduction of Yang-Mills fields in the higher dimensional theory. From the particle physics point of view the most important question is how such a theory behaves in four dimensions and in particular in low energies. Therefore most of our efforts concern studies of the properties of an attractive scheme, the Coset-Space-Dimensional-Reduction (C.S.D.R.) scheme, which permits the study of the effective four dimensional theory coming from a gauge theory defined in higher dimensions. Here we summarize the C.S.D.R. procedure the main the rems which are obeyed and to present a realistic model which is the result of the model building efforts that take into account all the C.S.D.R. properties. (orig./HSI)

  18. Kinetics of hydrogen reduction of titanium-doped molybdenum dioxide

    International Nuclear Information System (INIS)

    He, Qian; Marin-Flores, Oscar; Hu, Shuozhen; Scudiero, Louis; Ha, Su; Norton, M. Grant

    2015-01-01

    Ti-doped MoO 2 was synthesized to broaden the oxygen-to-carbon ratio operating range of MoO 2 for partial oxidation of long-chain hydrocarbons by increasing the redox stability. The structure modification causes the hydrogen reduction mechanism to change from three-dimensional nuclei growth with an activation energy of 61.3 kJ mol −1 to a three-dimensional hydrogen diffusion limited model with an activation energy of 317.9 kJ mol −1 . Because of the enhanced redox stability, Ti-doped MoO 2 has potential as an alternative anode in direct liquid-fed solid oxide fuel cells

  19. Chemical Reduction Synthesis of Iron Aluminum Powders

    Science.gov (United States)

    Zurita-Méndez, N. N.; la Torre, G. Carbajal-De; Ballesteros-Almanza, L.; Villagómez-Galindo, M.; Sánchez-Castillo, A.; Espinosa-Medina, M. A.

    In this study, a chemical reduction synthesis method of iron aluminum (FeAl) nano-dimensional intermetallic powders is described. The process has two stages: a salt reduction and solvent evaporation by a heat treatment at 1100°C. The precursors of the synthesis are ferric chloride, aluminum foil chips, a mix of Toluene/THF in a 75/25 volume relationship, and concentrated hydrochloric acid as initiator of the reaction. The reaction time was 20 days, the product obtained was dried at 60 °C for 2 h and calcined at 400, 800, and 1100 °C for 4 h each. To characterize and confirm the obtained synthesis products, X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) techniques were used. The results of morphology and chemical characterization of nano-dimensional powders obtained showed a formation of agglomerated particles of a size range of approximately 150 nm to 1.0 μm. Composition of powders was identified as corundum (Al2O3), iron aluminide (FeAl3), and iron-aluminum oxides (Fe0. 53Al0. 47)2O3 phases. The oxide phases formation were associated with the reaction of atmospheric concentration-free oxygen during synthesis and sintering steps, reducing the concentration of the iron aluminum phase.

  20. Discovering Hidden Controlling Parameters using Data Analytics and Dimensional Analysis

    Science.gov (United States)

    Del Rosario, Zachary; Lee, Minyong; Iaccarino, Gianluca

    2017-11-01

    Dimensional Analysis is a powerful tool, one which takes a priori information and produces important simplifications. However, if this a priori information - the list of relevant parameters - is missing a relevant quantity, then the conclusions from Dimensional Analysis will be incorrect. In this work, we present novel conclusions in Dimensional Analysis, which provide a means to detect this failure mode of missing or hidden parameters. These results are based on a restated form of the Buckingham Pi theorem that reveals a ridge function structure underlying all dimensionless physical laws. We leverage this structure by constructing a hypothesis test based on sufficient dimension reduction, allowing for an experimental data-driven detection of hidden parameters. Both theory and examples will be presented, using classical turbulent pipe flow as the working example. Keywords: experimental techniques, dimensional analysis, lurking variables, hidden parameters, buckingham pi, data analysis. First author supported by the NSF GRFP under Grant Number DGE-114747.

  1. Tumor Volume Changes Assessed by Three-Dimensional Magnetic Resonance Volumetry in Rectal Cancer Patients After Preoperative Chemoradiation: The Impact of the Volume Reduction Ratio on the Prediction of Pathologic Complete Response

    International Nuclear Information System (INIS)

    Kang, Jeong Hyun; Kim, Young Chul; Kim, Hyunki; Kim, Young Wan; Hur, Hyuk; Kim, Jin Soo; Min, Byung Soh; Kim, Hogeun; Lim, Joon Seok; Seong, Jinsil; Keum, Ki Chang; Kim, Nam Kyu

    2010-01-01

    Purpose: The aim of this study was to determine the correlation between tumor volume changes assessed by three-dimensional (3D) magnetic resonance (MR) volumetry and the histopathologic tumor response in rectal cancer patients undergoing preoperative chemoradiation therapy (CRT). Methods and Materials: A total of 84 patients who underwent preoperative CRT followed by radical surgery were prospectively enrolled in the study. The post-treatment tumor volume and tumor volume reduction ratio (% decrease ratio), as shown by 3D MR volumetry, were compared with the histopathologic response, as shown by T and N downstaging and the tumor regression grade (TRG). Results: There were no significant differences in the post-treatment tumor volume and the volume reduction ratio shown by 3D MR volumetry with respect to T and N downstaging and the tumor regression grade. In a multivariate analysis, the tumor volume reduction ratio was not significantly associated with T and N downstaging. The volume reduction ratio (>75%, p = 0.01) and the pretreatment carcinoembryonic antigen level (≤3 ng/ml, p = 0.01), but not the post-treatment volume shown by 3D MR (≤ 5ml), were, however, significantly associated with an increased pathologic complete response rate. Conclusion: More than 75% of the tumor volume reduction ratios were significantly associated with a high pathologic complete response rate. Therefore, limited treatment options such as local excision or simple observation might be considered after preoperative CRT in this patient population.

  2. Low-Dimensional Feature Representation for Instrument Identification

    Science.gov (United States)

    Ihara, Mizuki; Maeda, Shin-Ichi; Ikeda, Kazushi; Ishii, Shin

    For monophonic music instrument identification, various feature extraction and selection methods have been proposed. One of the issues toward instrument identification is that the same spectrum is not always observed even in the same instrument due to the difference of the recording condition. Therefore, it is important to find non-redundant instrument-specific features that maintain information essential for high-quality instrument identification to apply them to various instrumental music analyses. For such a dimensionality reduction method, the authors propose the utilization of linear projection methods: local Fisher discriminant analysis (LFDA) and LFDA combined with principal component analysis (PCA). After experimentally clarifying that raw power spectra are actually good for instrument classification, the authors reduced the feature dimensionality by LFDA or by PCA followed by LFDA (PCA-LFDA). The reduced features achieved reasonably high identification performance that was comparable or higher than those by the power spectra and those achieved by other existing studies. These results demonstrated that our LFDA and PCA-LFDA can successfully extract low-dimensional instrument features that maintain the characteristic information of the instruments.

  3. Global sensitivity analysis by polynomial dimensional decomposition

    Energy Technology Data Exchange (ETDEWEB)

    Rahman, Sharif, E-mail: rahman@engineering.uiowa.ed [College of Engineering, The University of Iowa, Iowa City, IA 52242 (United States)

    2011-07-15

    This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol's method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent.

  4. Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

    Directory of Open Access Journals (Sweden)

    M. P. Jarabo-Amores

    2010-01-01

    Full Text Available The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs. In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

  5. Hypergraph-based anomaly detection of high-dimensional co-occurrences.

    Science.gov (United States)

    Silva, Jorge; Willett, Rebecca

    2009-03-01

    This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.

  6. Geometric subspace updates with applications to online adaptive nonlinear model reduction

    DEFF Research Database (Denmark)

    Zimmermann, Ralf; Peherstorfer, Benjamin; Willcox, Karen

    2018-01-01

    In many scientific applications, including model reduction and image processing, subspaces are used as ansatz spaces for the low-dimensional approximation and reconstruction of the state vectors of interest. We introduce a procedure for adapting an existing subspace based on information from...... Estimation (GROUSE). We establish for GROUSE a closed-form expression for the residual function along the geodesic descent direction. Specific applications of subspace adaptation are discussed in the context of image processing and model reduction of nonlinear partial differential equation systems....

  7. Two dimensional generalizations of the Newcomb equation

    International Nuclear Information System (INIS)

    Dewar, R.L.; Pletzer, A.

    1989-11-01

    The Bineau reduction to scalar form of the equation governing ideal, zero frequency linearized displacements from a hydromagnetic equilibrium possessing a continuous symmetry is performed in 'universal coordinates', applicable to both the toroidal and helical cases. The resulting generalized Newcomb equation (GNE) has in general a more complicated form than the corresponding one dimensional equation obtained by Newcomb in the case of circular cylindrical symmetry, but in this cylindrical case , the equation can be transformed to that of Newcomb. In the two dimensional case there is a transformation which leaves the form of the GNE invariant and simplifies the Frobenius expansion about a rational surface, especially in the limit of zero pressure gradient. The Frobenius expansions about a mode rational surface is developed and the connection with Hamiltonian transformation theory is shown. 17 refs

  8. Controllable one-pot synthesis of various one-dimensional Bi2S3 nanostructures and their enhanced visible-light-driven photocatalytic reduction of Cr(VI)

    International Nuclear Information System (INIS)

    Hu, Enlai; Gao, Xuehui; Etogo, Atangana; Xie, Yunlong; Zhong, Yijun; Hu, Yong

    2014-01-01

    Highlights: • 1D Bi 2 S 3 nanostructures were prepared by a facile ethanol-assisted one-pot reaction. • The size and morphology of the products can be conveniently varied. • The sulfur source plays a crucial role in determining the morphologies of products. • 1D Bi 2 S 3 nanostructures exhibit enhanced photocatalytic reduction of Cr(VI). • Bi 2 S 3 nanowires exhibit the highest photoreduction activity among three samples. - Abstract: One-dimensional (1D) Bi 2 S 3 nanostructures with various morphologies, including nanowires, nanorods, and nanotubes, have been successfully synthesized through a facile ethanol-assisted one-pot reaction. It is found that the size, morphology and structure of the products can be conveniently varied or controlled by simply adjusting the volume ratio of ethanol and water in the reaction system. Further experimental results indicate that sulfur source also plays the other crucial role in determining the product morphology. The synthetic strategy developed in this work is highly efficient in producing 1D Bi 2 S 3 nanostructures with high quality and large quantity. Photocatalysis experiments show the as-prepared 1D Bi 2 S 3 nanostructures possess significantly enhanced photocatalytic reduction of Cr(VI) when exposed to visible light irradiation. Especially, Bi 2 S 3 nanowires exhibit the highest photocatalytic activity and can be used repeatedly after washed with dilute HCl

  9. N = 2 local and N = 4 non-local reductions of supersymmetric KP hierarchy in N = 2 superspace

    International Nuclear Information System (INIS)

    Delduc, F.; Gallot, L.; Sorin, A.

    1999-01-01

    An N = 4 supersymmetric matrix KP hierarchy is proposed and a wide class of its reductions which are characterized by a finite number of fields are described. This class includes the one-dimensional reduction of the two-dimensional N = (2,2) superconformal Toda lattice hierarchy possessing the N = 4 supersymmetry -- the N = 4 Toda chain hierarchy - which may be relevant in the construction of supersymmetric matrix models. The Lax-pair representations of the bosonic and fermionic flows, corresponding local and non-local Hamiltonians, finite and infinite discrete symmetries, the first two Hamiltonian structures and the recursion operator connecting all evolution equations and the Hamiltonian structures of the N = 4 Toda chain hierarchy are constructed in explicit form. Is secondary reduction to the N 4 supersymmetric α = - 2 KdV hierarchy is

  10. An unsupervised text mining method for relation extraction from biomedical literature.

    Directory of Open Access Journals (Sweden)

    Changqin Quan

    Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

  11. Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

    Science.gov (United States)

    Aytaç Korkmaz, Sevcan; Binol, Hamidullah

    2018-03-01

    Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

  12. Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

    Science.gov (United States)

    Becus, Georges A.; Chan, Alistair K.

    1993-01-01

    Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

  13. Two-dimensional mapping of three-dimensional SPECT data: a preliminary step to the quantitation of thallium myocardial perfusion single photon emission tomography

    International Nuclear Information System (INIS)

    Goris, M.L.; Boudier, S.; Briandet, P.A.

    1987-01-01

    A method is presented by which tomographic myocardial perfusion data are prepared for quantitative analysis. The method is characterized by an interrogation of the original data, which results in a size and shape normalization. The method is analogous to the circumferential profile methods used in planar scintigraphy but requires a polar-to-cartesian transformation from three to two dimensions. As was the case in the planar situation, centering and reorientation are explicit. The degree of data reduction is evaluated by reconstructing idealized three-dimensional data from the two-dimensional sampling vectors. The method differs from previously described approaches by the absence in the resulting vector of a coordinate reflecting cartesian coordinate in the original data (slice number)

  14. Cohomological reduction of sigma models

    Energy Technology Data Exchange (ETDEWEB)

    Candu, Constantin; Mitev, Vladimir; Schomerus, Volker [DESY, Hamburg (Germany). Theory Group; Creutzig, Thomas [North Carolina Univ., Chapel Hill, NC (United States). Dept. of Physics and Astronomy

    2010-01-15

    This article studies some features of quantum field theories with internal supersymmetry, focusing mainly on 2-dimensional non-linear sigma models which take values in a coset superspace. It is discussed how BRST operators from the target space super- symmetry algebra can be used to identify subsectors which are often simpler than the original model and may allow for an explicit computation of correlation functions. After an extensive discussion of the general reduction scheme, we present a number of interesting examples, including symmetric superspaces G/G{sup Z{sub 2}} and coset superspaces of the form G/G{sup Z{sub 4}}. (orig.)

  15. A general reduction method for one-loop N-point integrals

    International Nuclear Information System (INIS)

    Heinrich, G.; Binoth, T.

    2000-01-01

    In order to calculate cross sections with a large number of particles/jets in the final state at next-to-leading order, one has to reduce the occurring scalar and tensor one-loop integrals to a small set of known integrals. In massless theories, this reduction procedure is complicated by the presence of infrared divergences. Working in n = 4 - 2ε dimensions, it will be outlined how to achieve such a reduction for diagrams with an arbitrary number of external legs. As a result, any integral with more than four propagators and generic 4-dimensional external momenta can be reduced to box integrals

  16. Parallel accelerated cyclic reduction preconditioner for three-dimensional elliptic PDEs with variable coefficients

    KAUST Repository

    Chavez Chavez, Gustavo Ivan; Turkiyyah, George; Zampini, Stefano; Keyes, David E.

    2017-01-01

    and the cyclic reduction method. The setup and application phases of the preconditioner achieve log-linear complexity in memory footprint and number of operations, and numerical experiments exhibit good weak and strong scalability at large processor counts in a

  17. Face recognition based on two-dimensional discriminant sparse preserving projection

    Science.gov (United States)

    Zhang, Dawei; Zhu, Shanan

    2018-04-01

    In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

  18. Feature Space Dimensionality Reduction for Real-Time Vision-Based Food Inspection

    Directory of Open Access Journals (Sweden)

    Mai Moussa CHETIMA

    2009-03-01

    Full Text Available Machine vision solutions are becoming a standard for quality inspection in several manufacturing industries. In the processed-food industry where the appearance attributes of the product are essential to customer’s satisfaction, visual inspection can be reliably achieved with machine vision. But such systems often involve the extraction of a larger number of features than those actually needed to ensure proper quality control, making the process less efficient and difficult to tune. This work experiments with several feature selection techniques in order to reduce the number of attributes analyzed by a real-time vision-based food inspection system. Identifying and removing as much irrelevant and redundant information as possible reduces the dimensionality of the data and allows classification algorithms to operate faster. In some cases, accuracy on classification can even be improved. Filter-based and wrapper-based feature selectors are experimentally evaluated on different bakery products to identify the best performing approaches.

  19. Universal contributions to scalar masses from five dimensional supergravity

    CERN Document Server

    Dudas, Emilian

    2012-01-01

    We compute the effective Kahler potential for matter fields in warped compactifications, starting from five dimensional gauged supergravity, as a function of the matter fields localization. We show that truncation to zero modes is inconsistent and the tree-level exchange of the massive gravitational multiplet is needed for consistency of the four-dimensional theory. In addition to the standard Kahler coming from dimensional reduction, we find the quartic correction coming from integrating out the gravity multiplet. We apply our result to the computation of scalar masses, by assuming that the SUSY breaking field is a bulk hypermultiplet. In the limit of extreme opposite localization of the matter and the spurion fields, we find zero scalar masses, consistent with sequestering arguments. Surprisingly enough, for all the other cases the scalar masses are tachyonic. This suggests the holographic interpretation that a CFT sector always generates operators contributing in a tachyonic way to scalar masses. Viability...

  20. Improving feature selection process resistance to failures caused by curse-of-dimensionality effects

    Czech Academy of Sciences Publication Activity Database

    Somol, Petr; Grim, Jiří; Novovičová, Jana; Pudil, P.

    2011-01-01

    Roč. 47, č. 3 (2011), s. 401-425 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection * curse of dimensionality * over-fitting * stability * machine learning * dimensionality reduction Subject RIV: IN - Informatics, Computer Science Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/somol-0368741.pdf

  1. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    Energy Technology Data Exchange (ETDEWEB)

    Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca [Department of Radiology, Stanford University, Stanford, California 94305 (United States); Department of Radiology, Stanford University, Stanford, California 94305 (United States) and Center for Medical Image Science and Visualization, Linkoeping University, Linkoeping (Sweden); Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen (Germany); Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Siemens AG Healthcare, Forchheim 91301 (Germany); Department of Radiology, Stanford University, Stanford, California 94305 (United States)

    2011-11-15

    Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8

  2. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    International Nuclear Information System (INIS)

    Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca

    2011-01-01

    Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold

  3. Three-dimensional versus two-dimensional vision in laparoscopy

    DEFF Research Database (Denmark)

    Sørensen, Stine D; Savran, Mona Meral; Konge, Lars

    2016-01-01

    were cohort size and characteristics, skill trained or operation performed, instrument used, outcome measures, and conclusions. Two independent authors performed the search and data extraction. RESULTS: Three hundred and forty articles were screened for eligibility, and 31 RCTs were included...... through a two-dimensional (2D) projection on a monitor, which results in loss of depth perception. To counter this problem, 3D imaging for laparoscopy was developed. A systematic review of the literature was performed to assess the effect of 3D laparoscopy. METHODS: A systematic search of the literature...... in the review. Three trials were carried out in a clinical setting, and 28 trials used a simulated setting. Time was used as an outcome measure in all of the trials, and number of errors was used in 19 out of 31 trials. Twenty-two out of 31 trials (71 %) showed a reduction in performance time, and 12 out of 19...

  4. Incomplete Dirac reduction of constrained Hamiltonian systems

    Energy Technology Data Exchange (ETDEWEB)

    Chandre, C., E-mail: chandre@cpt.univ-mrs.fr

    2015-10-15

    First-class constraints constitute a potential obstacle to the computation of a Poisson bracket in Dirac’s theory of constrained Hamiltonian systems. Using the pseudoinverse instead of the inverse of the matrix defined by the Poisson brackets between the constraints, we show that a Dirac–Poisson bracket can be constructed, even if it corresponds to an incomplete reduction of the original Hamiltonian system. The uniqueness of Dirac brackets is discussed. The relevance of this procedure for infinite dimensional Hamiltonian systems is exemplified.

  5. Reduction of respiratory ghosting motion artifacts in conventional two-dimensional multi-slice Cartesian turbo spin-echo: which k-space filling order is the best?

    Science.gov (United States)

    Inoue, Yuuji; Yoneyama, Masami; Nakamura, Masanobu; Takemura, Atsushi

    2018-06-01

    The two-dimensional Cartesian turbo spin-echo (TSE) sequence is widely used in routine clinical studies, but it is sensitive to respiratory motion. We investigated the k-space orders in Cartesian TSE that can effectively reduce motion artifacts. The purpose of this study was to demonstrate the relationship between k-space order and degree of motion artifacts using a moving phantom. We compared the degree of motion artifacts between linear and asymmetric k-space orders. The actual spacing of ghost artifacts in the asymmetric order was doubled compared with that in the linear order in the free-breathing situation. The asymmetric order clearly showed less sensitivity to incomplete breath-hold at the latter half of the imaging period. Because of the actual number of partitions of the k-space and the temporal filling order, the asymmetric k-space order of Cartesian TSE was superior to the linear k-space order for reduction of ghosting motion artifacts.

  6. Similarity measurement method of high-dimensional data based on normalized net lattice subspace

    Institute of Scientific and Technical Information of China (English)

    Li Wenfa; Wang Gongming; Li Ke; Huang Su

    2017-01-01

    The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity, leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals, and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this meth-od, three data types are used, and seven common similarity measurement methods are compared. The experimental result indicates that the relative difference of the method is increasing with the di-mensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition, the similarity range of this method in different dimensions is [0, 1], which is fit for similarity analysis after dimensionality reduction.

  7. Canonical reduction of self-dual Yang-Mills equations to Fitzhugh-Nagumo equation and exact solutions

    International Nuclear Information System (INIS)

    Sayed, S.M.; Gharib, G.M.

    2009-01-01

    The (constrained) canonical reduction of four-dimensional self-dual Yang-Mills theory to two-dimensional Fitzhugh-Nagumo and the real Newell-Whitehead equations are considered. On the other hand, other methods and transformations are developed to obtain exact solutions for the original two-dimensional Fitzhugh-Nagumo and Newell-Whitehead equations. The corresponding gauge potential A μ and the gauge field strengths F μν are also obtained. New explicit and exact traveling wave and solitary solutions (for Fitzhugh-Nagumo and Newell-Whitehead equations) are obtained by using an improved sine-cosine method and the Wu's elimination method with the aid of Mathematica.

  8. Complete algebraic reduction of one-loop tensor Feynman integrals

    International Nuclear Information System (INIS)

    Fleischer, J.; Riemann, T.

    2011-01-01

    We set up a new, flexible approach for the tensor reduction of one-loop Feynman integrals. The 5-point tensor integrals up to rank R=5 are expressed by 4-point tensor integrals of rank R-1, such that the appearance of the inverse 5-point Gram determinant is avoided. The 4-point tensor coefficients are represented in terms of 4-point integrals, defined in d dimensions, 4-2ε≤d≤4-2ε+2(R-1), with higher powers of the propagators. They can be further reduced to expressions which stay free of the inverse 4-point Gram determinants but contain higher-dimensional 4-point integrals with only the first power of scalar propagators, plus 3-point tensor coefficients. A direct evaluation of the higher-dimensional 4-point functions would avoid the appearance of inverse powers of the Gram determinants completely. The simplest approach, however, is to apply here dimensional recurrence relations in order to reduce them to the familiar 2- to 4-point functions in generic dimension d=4-2ε, introducing thereby coefficients with inverse 4-point Gram determinants up to power R for tensors of rank R. For small or vanishing Gram determinants--where this reduction is not applicable--we use analytic expansions in positive powers of the Gram determinants. Improving the convergence of the expansions substantially with Pade approximants we close up to the evaluation of the 4-point tensor coefficients for larger Gram determinants. Finally, some relations are discussed which may be useful for analytic simplifications of Feynman diagrams.

  9. Twisted spin Sutherland models from quantum Hamiltonian reduction

    International Nuclear Information System (INIS)

    Feher, L; Pusztai, B G

    2008-01-01

    Recent general results on Hamiltonian reductions under polar group actions are applied to study some reductions of the free particle governed by the Laplace-Beltrami operator of a compact, connected, simple Lie group. The reduced systems associated with arbitrary finite-dimensional irreducible representations of the group by using the symmetry induced by twisted conjugations are described in detail. These systems generically yield integrable Sutherland-type many-body models with spin, which are called twisted spin Sutherland models if the underlying twisted conjugations are built on non-trivial Dynkin diagram automorphisms. The spectra of these models can be calculated, in principle, by solving certain Clebsch-Gordan problems, and the result is presented for the models associated with the symmetric tensorial powers of the defining representation of SU(N)

  10. Three-dimensional quantum electrodynamics as an effective interaction

    International Nuclear Information System (INIS)

    Abdalla, E.; Carvalho Filho, F.M. de

    1995-10-01

    We obtain a Quantum Electrodynamics in 2 + 1 dimensions by applying a Kaluza-Klein type method of dimensional reduction to Quantum Electrodynamics in 3 + 1 dimensions rendering the model more realistic to application in solid-state systems, invariant under translations in one direction. We show that the model obtained leads to an effective action exhibiting an interesting phase structure and that the generated Chern-Simons term survives only in the broken phase. (author). 20 refs

  11. Equivariant Reduction of Gauge Theories over Fuzzy Extra Dimensions

    International Nuclear Information System (INIS)

    Kürkçüoglu, Seçkin

    2012-01-01

    In SU(N) Yang-Mills theories on a manifold M, which are suitably coupled to a set of scalars, fuzzy spheres may be generated as extra dimensions by spontaneous symmetry breaking. This process results in gauge theories over the product space of the manifold M and the fuzzy spheres with smaller gauge groups. Here we present the SU(2)– and SU(2) × SU(2)-equivariant parametrization of U(2) and U(4) gauge fields on S 2 F and S 2 F × S 2 F respectively and outline the dimensional reduction of these theories over the fuzzy extra dimensions. The emerging dimensionally reduced theories are Higgs type models. Some vortex type solutions of these theories are briefly discussed.

  12. Dimensional reduction of a Lorentz and CPT-violating Maxwell-Chern-Simons model

    International Nuclear Information System (INIS)

    Belich, H. Jr.; Helayel Neto, J.A.; Ferreira, M.M. Jr.; Maranhao Univ., Sao Luiz, MA; Orlando, M.T.D.; Espirito Santo Univ., Vitoria, ES

    2003-01-01

    Taking as starting point a Lorentz and CPT non-invariant Chern-Simons-like model defined in 1+3 dimensions, we proceed realizing its dimensional to D = 1+2. One then obtains a new planar model, composed by the Maxwell-Chern-Simons (MCS) sector, a Klein-Gordon massless scalar field, and a coupling term that mixes the gauge field to the external vector, ν μ . In spite of breaking Lorentz invariance in the particle frame, this model may preserve the CPT symmetry for a single particular choice of ν μ . Analyzing the dispersion relations, one verifies that the reduced model exhibits stability, but the causality can be jeopardized by some modes. The unitary of the gauge sector is assured without any restriction , while the scalar sector is unitary only in the space-like case. (author)

  13. Open reduction and internal fixation aided by intraoperative 3-dimensional imaging improved the articular reduction in 72 displaced acetabular fractures

    DEFF Research Database (Denmark)

    Eckardt, Henrik; Lind, Dennis; Toendevold, Erik

    2015-01-01

    was evaluated on reconstructed coronal and sagittal images of the acetabulum. Results - The fracture severity and patient characteristics were similar in the 2 groups. In the 3D group, 46 of 72 patients (0.6) had a perfect result after open reduction and internal fixation, and in the control group, 17 of 42 (0...

  14. Solid-state Water-mediated Transport Reduction of Nanostructured Iron Oxides

    International Nuclear Information System (INIS)

    Smirnov, Vladimir M.; Povarov, Vladimir G.; Voronkov, Gennadii P.; Semenov, Valentin G.; Murin, Igor' V.; Gittsovich, Viktor N.; Sinel'nikov, Boris M.

    2001-01-01

    The Fe 2+ /Fe 3+ ratio in two-dimensional iron oxide nanosructures (nanolayers with a thickness of 0.3-1.5 nm on silica surface) may be precisely controlled using the transport reduction (TR) technique. The species ≡-O-Fe(OH) 2 and (≡Si-O-) 2 -FeOH forming the surface monolayer are not reduced at 400-600 deg. C because of their covalent bonding to the silica surface, as demonstrated by Moessbauer spectroscopy. Iron oxide microparticles (microstructures) obtained by the impregnation technique, being chemically unbound to silica, are subjected to reduction at T ≥ 500 deg. C with formation of metallic iron in the form of α-Fe. Transport reduction of supported nanostructures (consisting of 1 or 4 monolayers) at T ≥ 600 deg. C produces bulk iron(II) silicate and metallic iron phases. The structural-chemical transformations occurring in transport reduction of supported iron oxide nanolayers are proved to be governed by specific phase processes in the nanostructures themselves

  15. Low-resistance gateless high electron mobility transistors using three-dimensional inverted pyramidal AlGaN/GaN surfaces

    International Nuclear Information System (INIS)

    So, Hongyun; Senesky, Debbie G.

    2016-01-01

    In this letter, three-dimensional gateless AlGaN/GaN high electron mobility transistors (HEMTs) were demonstrated with 54% reduction in electrical resistance and 73% increase in surface area compared with conventional gateless HEMTs on planar substrates. Inverted pyramidal AlGaN/GaN surfaces were microfabricated using potassium hydroxide etched silicon with exposed (111) surfaces and metal-organic chemical vapor deposition of coherent AlGaN/GaN thin films. In addition, electrical characterization of the devices showed that a combination of series and parallel connections of the highly conductive two-dimensional electron gas along the pyramidal geometry resulted in a significant reduction in electrical resistance at both room and high temperatures (up to 300 °C). This three-dimensional HEMT architecture can be leveraged to realize low-power and reliable power electronics, as well as harsh environment sensors with increased surface area

  16. Low-resistance gateless high electron mobility transistors using three-dimensional inverted pyramidal AlGaN/GaN surfaces

    Energy Technology Data Exchange (ETDEWEB)

    So, Hongyun, E-mail: hyso@stanford.edu [Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94305 (United States); Senesky, Debbie G. [Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94305 (United States); Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States)

    2016-01-04

    In this letter, three-dimensional gateless AlGaN/GaN high electron mobility transistors (HEMTs) were demonstrated with 54% reduction in electrical resistance and 73% increase in surface area compared with conventional gateless HEMTs on planar substrates. Inverted pyramidal AlGaN/GaN surfaces were microfabricated using potassium hydroxide etched silicon with exposed (111) surfaces and metal-organic chemical vapor deposition of coherent AlGaN/GaN thin films. In addition, electrical characterization of the devices showed that a combination of series and parallel connections of the highly conductive two-dimensional electron gas along the pyramidal geometry resulted in a significant reduction in electrical resistance at both room and high temperatures (up to 300 °C). This three-dimensional HEMT architecture can be leveraged to realize low-power and reliable power electronics, as well as harsh environment sensors with increased surface area.

  17. Dimensional reduction of the Standard Model coupled to a new singlet scalar field

    Energy Technology Data Exchange (ETDEWEB)

    Brauner, Tomáš [Faculty of Science and Technology, University of Stavanger,N-4036 Stavanger (Norway); Tenkanen, Tuomas V.I. [Department of Physics and Helsinki Institute of Physics,P.O. Box 64, FI-00014 University of Helsinki (Finland); Tranberg, Anders [Faculty of Science and Technology, University of Stavanger,N-4036 Stavanger (Norway); Vuorinen, Aleksi [Department of Physics and Helsinki Institute of Physics,P.O. Box 64, FI-00014 University of Helsinki (Finland); Weir, David J. [Faculty of Science and Technology, University of Stavanger,N-4036 Stavanger (Norway); Department of Physics and Helsinki Institute of Physics,P.O. Box 64, FI-00014 University of Helsinki (Finland)

    2017-03-01

    We derive an effective dimensionally reduced theory for the Standard Model augmented by a real singlet scalar. We treat the singlet as a superheavy field and integrate it out, leaving an effective theory involving only the Higgs and SU(2){sub L}×U(1){sub Y} gauge fields, identical to the one studied previously for the Standard Model. This opens up the possibility of efficiently computing the order and strength of the electroweak phase transition, numerically and nonperturbatively, in this extension of the Standard Model. Understanding the phase diagram is crucial for models of electroweak baryogenesis and for studying the production of gravitational waves at thermal phase transitions.

  18. Single-time reduction of bethe-salpeter formalism for two-fermion system

    International Nuclear Information System (INIS)

    Arkhipov, A.A.

    1988-01-01

    The single-time reduction method proposed in other refs. for the system of two scalar particles is generalized for the case of two-fermion system. A self-consistent procedure of single-time reduction has been constructed both in terms of the Bethe-Salpeter wave function and in terms of the Green's function of two-fermion system. Three-dimensional dynamic equations have been obtained for single-time wave functions and two-time Green's functions of a two-fermion system and the Schroedinger structure of the equations obtained is shown to be a consequence of the causality structure of the local QFT. 32 refs

  19. Low Overpotential and High Current CO2 Reduction with Surface Reconstructed Cu Foam Electrodess

    KAUST Repository

    Min, Shixiong; Yang, Xiulin; Lu, Ang-Yu; Tseng, Chien-Chih; Hedhili, Mohamed N.; Li, Lain-Jong; Huang, Kuo-Wei

    2016-01-01

    for large-scale fuel synthesis. Here we report an extremely high current density for CO2 reduction at low overpotential using a Cu foam electrode prepared by air-oxidation and subsequent electroreduction. Apart from possessing three-dimensional (3D) open

  20. Relativistic formulations with Blankenbecler-Sugar reduction technique for the three-particle system

    International Nuclear Information System (INIS)

    Morioka, S.; Afnan, I.R.

    1980-05-01

    A critical comparison for two-types of three-dimensional covariant equations for the three-particle system obtained by the Blankenbecler-Sugar reduction technique with the Whitghtman-Garding momenta and the usual Jacobi variables is presented. The relations between the relativistic and non-relativistic equations in the low energy limit are discussed

  1. Canonical reduction of self-dual Yang-Mills equations to Fitzhugh-Nagumo equation and exact solutions

    Energy Technology Data Exchange (ETDEWEB)

    Sayed, S.M. [Mathematics Department, Faculty of Science, Beni-Suef University, Beni-Suef (Egypt); Mathematics Department, P.O. Box 1144, Tabouk Teacher College, Ministry of Education (Saudi Arabia)], E-mail: eaashour@lycos.com; Gharib, G.M. [Mathematics Department, P.O. Box 1144, Tabouk Teacher College, Ministry of Education (Saudi Arabia)

    2009-01-30

    The (constrained) canonical reduction of four-dimensional self-dual Yang-Mills theory to two-dimensional Fitzhugh-Nagumo and the real Newell-Whitehead equations are considered. On the other hand, other methods and transformations are developed to obtain exact solutions for the original two-dimensional Fitzhugh-Nagumo and Newell-Whitehead equations. The corresponding gauge potential A{sub {mu}} and the gauge field strengths F{sub {mu}}{sub {nu}} are also obtained. New explicit and exact traveling wave and solitary solutions (for Fitzhugh-Nagumo and Newell-Whitehead equations) are obtained by using an improved sine-cosine method and the Wu's elimination method with the aid of Mathematica.

  2. Dimensional reduction of a Lorentz and CPT-violating Maxwell-Chern-Simons model

    Energy Technology Data Exchange (ETDEWEB)

    Belich, H. Jr.; Helayel Neto, J.A. [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil). Coordenacao de Teoria de Campos e Particulas; Grupo de Fisica Teorica Jose Leite Lopes, Petropolis, RJ (Brazil); E-mails: belich@cbpf.br; helayel@cbpf.br; Ferreira, M.M. Jr. [Grupo de Fisica Teorica Jose Leite Lopes, Petropolis, RJ (Brazil); Maranhao Univ., Sao Luiz, MA (Brazil). Dept. de Fisica]. E-mail: manojr@cbpf.br; Orlando, M.T.D. [Grupo de Fisica Teorica Jose Leite Lopes, Petropolis, RJ (Brazil); Espirito Santo Univ., Vitoria, ES (Brazil). Dept. de Fisica e Quimica; E-mail: orlando@cce.ufes.br

    2003-01-01

    Taking as starting point a Lorentz and CPT non-invariant Chern-Simons-like model defined in 1+3 dimensions, we proceed realizing its dimensional to D = 1+2. One then obtains a new planar model, composed by the Maxwell-Chern-Simons (MCS) sector, a Klein-Gordon massless scalar field, and a coupling term that mixes the gauge field to the external vector, {nu}{sup {mu}}. In spite of breaking Lorentz invariance in the particle frame, this model may preserve the CPT symmetry for a single particular choice of {nu}{sup {mu}} . Analyzing the dispersion relations, one verifies that the reduced model exhibits stability, but the causality can be jeopardized by some modes. The unitary of the gauge sector is assured without any restriction , while the scalar sector is unitary only in the space-like case. (author)

  3. Efficient two-dimensional compressive sensing in MIMO radar

    Science.gov (United States)

    Shahbazi, Nafiseh; Abbasfar, Aliazam; Jabbarian-Jahromi, Mohammad

    2017-12-01

    Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed received signals and then we propose a new measurement matrix design for our 2D compressive sensing model that is based on minimizing the coherence of sensing matrix using gradient descent algorithm. The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

  4. High index surfaces of Au-nanocrystals supported on one-dimensional MoO3-nanorod as a bi-functional electrocatalyst for ethanol oxidation and oxygen reduction

    International Nuclear Information System (INIS)

    Karuppasamy, L.; Chen, C.Y.; Anandan, S.; Wu, J.J.

    2017-01-01

    Highlights: •Synthesis of a new class of Au nanocrystals enclosed with high index surface supported on MoO 3 nanorods by ultrasonic probe method. •The role of supporting materials reduces the loading of Au and acts as a co-catalyst. •The as prepared electrocatalyst exhibits enhanced catalytic activity and stability towards both EOR and ORR. -- Abstract: The design of highly active electrocatalyst for ethanol electrooxidation (EOR) and oxygen reduction reaction (ORR) is of great significance for the improvement of efficient direct ethanol fuel cells (DEFCs). Therefore, creating high index facets nanocrystals with abundant catalytic active sites of stepped atoms is an effective way to enhance the electrocatalytic performance. In this article, we prepared the high index surface structures of Au nanocrystals supported on one-dimensional (1-D) MoO 3 nanorods by using two steps ultrasonic probe irradiation method. The size and physical properties of as electrocatalysts were studied by using field emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), and x-ray diffraction (XRD) instrumentation. Besides, the catalytic activity of as Au-MoO 3 electrocatalyst was determined by using cyclic voltammetry (CV), chronoamperometric (CA), electrochemical impedance spectroscopy (EIS), CO-stripping, and linear sweep voltammetry-rotating disk electrode (LSV-RDE). As a consequence, the Au-MoO 3 nanocomposites has been considered as an effective electrocatalyst for both ethanol oxidation and oxygen reduction reaction.

  5. Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning

    OpenAIRE

    Lai, Rongjie; Li, Jia

    2017-01-01

    Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regu...

  6. Evaluation and Development of Chemical Kinetic Mechanism Reduction Scheme for Biodiesel and Diesel Fuel Surrogates

    DEFF Research Database (Denmark)

    Poon, Hiew Mun; Ng, Hoon Kiat; Gan, Suyin

    2013-01-01

    The aim of this study is to evaluate the existing chemical kinetic mechanism reduction techniques. From here, an appropriate reduction scheme was developed to create compact yet comprehensive surrogate models for both diesel and biodiesel fuels for diesel engine applications. The reduction...... techniques applied here were Directed Relation Graph (DRG), DRG with Error Propagation, DRG-aided Sensitivity Analysis, and DRG with Error Propagation and Sensitivity Analysis. Nonetheless, the reduced mechanisms generated via these techniques were not sufficiently small for application in multi......-dimensional computational fluid dynamics (CFD) study. A new reduction scheme was therefore formulated. A 68-species mechanism for biodiesel surrogate and a 49-species mechanism for diesel surrogate were successfully derived from the respective detailed mechanisms. An overall 97% reduction in species number...

  7. Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

    Directory of Open Access Journals (Sweden)

    Yingchun Chen

    2018-04-01

    Full Text Available With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency.

  8. Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

    Science.gov (United States)

    Cui, Tiangang; Marzouk, Youssef; Willcox, Karen

    2016-06-01

    Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.

  9. Green reduction of graphene oxide by ascorbic acid

    Science.gov (United States)

    Khosroshahi, Zahra; Kharaziha, Mahshid; Karimzadeh, Fathallah; Allafchian, Alireza

    2018-01-01

    Graphene, a single layer of sp2-hybridized carbon atoms in a hexagonal (two-dimensional honey-comb) lattice, has attracted strong scientific and technological interest due to its novel and excellent optical, chemical, electrical, mechanical and thermal properties. The solution-processable chemical reduction of Graphene oxide (GO is considered as the most favorable method regarding mass production of graphene. Generally, the reduction of GO is carried out by chemical approaches using different reductants such as hydrazine and sodium borohydride. These components are corrosive, combustible and highly toxic which may be dangerous for personnel health and the environment. Hence, these reducing agents are not promising choice for reducing of graphene oxide (GO). As a consequence, it is necessary for further development and optimization of eco-friendly, natural reducing agent for clean and effective reduction of GO. Ascorbic acid, an eco-friendly and natural reducing agents, having a mild reductive ability and nontoxic property. So, the aim of this research was to green synthesis of GO with ascorbic acid. For this purpose, the required amount of NaOH and ascorbic acid were added to GO solution (0.5 mg/ml) and were heated at 95 °C for 1 hour. According to the X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and electrochemical results, GO were reduced with ascorbic acid like hydrazine with better electrochemical properties and ascorbic acid is an ideal substitute for hydrazine in the reduction of graphene oxide process.

  10. Reduction of the Glauber amplitude for electron impact rotational excitation of quadrupolar molecular ions

    International Nuclear Information System (INIS)

    Mathur, K.C.; Gupta, G.P.; Pundir, R.S.

    1981-06-01

    A reduction of the Glauber amplitude for the rotational excitation of pure quadrupolar molecular ions by electron impact is presented in a form suitable for numerical evaluation. The differential cross-section is expressed in terms of one dimensional integrals over impact parameter. (author)

  11. The multicomponent (2+1)-dimensional Glachette–Johnson (GJ) equation hierarchy and its super-integrable coupling system

    International Nuclear Information System (INIS)

    Yu Fajun; Zhang Hongqing

    2008-01-01

    This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra à M . By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which possesses the Hamiltonian structure. As its reduction cases, the multicomponent (2+1)-dimensional Glachette–Johnson (GJ) hierarchy is given. Finally, the super-integrable coupling system of multicomponent (2+1)-dimensional GJ hierarchy is established through enlarging the spectral problem

  12. Renormalization Group Reduction of Non Integrable Hamiltonian Systems

    International Nuclear Information System (INIS)

    Tzenov, Stephan I.

    2002-01-01

    Based on Renormalization Group method, a reduction of non integratable multi-dimensional Hamiltonian systems has been performed. The evolution equations for the slowly varying part of the angle-averaged phase space density and for the amplitudes of the angular modes have been derived. It has been shown that these equations are precisely the Renormalization Group equations. As an application of the approach developed, the modulational diffusion in one-and-a-half degrees of freedom dynamical system has been studied in detail

  13. One-loop infinities in dimensionally reduced supergravities

    International Nuclear Information System (INIS)

    Fradkin, E.S.; Tseytlin, A.A.

    1983-11-01

    It is proved explicitly in the paper that d=4 theory following via reduction from N=1, d=10 supergravities is not finite at one loop while the version of N=8 supergravity directly following from N=1, d=11 theory is one-loop finite. The method used is based on quantization of initial higher dimensional theory as a first step. The results also suggest possible existence of non-standard (higher N) d>4 supergravities which reduce to d=4 theories with finite N=8 supergravity sector. (author)

  14. Three-dimensional laparoscopy vs 2-dimensional laparoscopy with high-definition technology for abdominal surgery: a systematic review.

    Science.gov (United States)

    Fergo, Charlotte; Burcharth, Jakob; Pommergaard, Hans-Christian; Kildebro, Niels; Rosenberg, Jacob

    2017-01-01

    This systematic review investigates newer generation 3-dimensional (3D) laparoscopy vs 2-dimensional (2D) laparoscopy in terms of error rating, performance time, and subjective assessment as early comparisons have shown contradictory results due to technological shortcomings. This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Randomized controlled trials (RCTs) comparing newer generation 3D-laparoscopy with 2D-laparoscopy were included through searches in Pubmed, EMBASE, and Cochrane Central Register of Controlled Trials database. Of 643 articles, 13 RCTs were included, of which 2 were clinical trials. Nine of 13 trials (69%) and 10 of 13 trials (77%) found a significant reduction in performance time and error, respectively, with the use of 3D-laparoscopy. Overall, 3D-laparoscopy was found to be superior or equal to 2D-laparoscopy. All trials featuring subjective evaluation found a superiority of 3D-laparoscopy. More clinical RCTs are still awaited for the convincing results to be reproduced. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. An analytical approach for a nodal scheme of two-dimensional neutron transport problems

    International Nuclear Information System (INIS)

    Barichello, L.B.; Cabrera, L.C.; Prolo Filho, J.F.

    2011-01-01

    Research highlights: → Nodal equations for a two-dimensional neutron transport problem. → Analytical Discrete Ordinates Method. → Numerical results compared with the literature. - Abstract: In this work, a solution for a two-dimensional neutron transport problem, in cartesian geometry, is proposed, on the basis of nodal schemes. In this context, one-dimensional equations are generated by an integration process of the multidimensional problem. Here, the integration is performed for the whole domain such that no iterative procedure between nodes is needed. The ADO method is used to develop analytical discrete ordinates solution for the one-dimensional integrated equations, such that final solutions are analytical in terms of the spatial variables. The ADO approach along with a level symmetric quadrature scheme, lead to a significant order reduction of the associated eigenvalues problems. Relations between the averaged fluxes and the unknown fluxes at the boundary are introduced as the usually needed, in nodal schemes, auxiliary equations. Numerical results are presented and compared with test problems.

  16. On a class of reductions of the Manakov-Santini hierarchy connected with the interpolating system

    International Nuclear Information System (INIS)

    Bogdanov, L V

    2010-01-01

    Using the Lax-Sato formulation of the Manakov-Santini hierarchy, we introduce a class of reductions such that the zero-order reduction of this class corresponds to the dKP hierarchy, and the first-order reduction gives the hierarchy associated with the interpolating system introduced by Dunajski. We present the Lax-Sato form of a reduced hierarchy for the interpolating system and also for the reduction of arbitrary order. Similar to the dKP hierarchy, the Lax-Sato equations for L (the Lax function) split from the Lax-Sato equations for M (the Orlov function) due to the reduction, and the reduced hierarchy for an arbitrary order of reduction is defined by Lax-Sato equations for L only. A characterization of the class of reductions in terms of the dressing data is given. We also consider a waterbag reduction of the interpolating system hierarchy, which defines (1+1)-dimensional systems of hydrodynamic type.

  17. An adaptive Kalman filter for speckle reductions in ultrasound images

    International Nuclear Information System (INIS)

    Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.

    1988-01-01

    Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction

  18. Large Reduction of Hot Spot Temperature in Graphene Electronic Devices with Heat-Spreading Hexagonal Boron Nitride.

    Science.gov (United States)

    Choi, David; Poudel, Nirakar; Park, Saungeun; Akinwande, Deji; Cronin, Stephen B; Watanabe, Kenji; Taniguchi, Takashi; Yao, Zhen; Shi, Li

    2018-04-04

    Scanning thermal microscopy measurements reveal a significant thermal benefit of including a high thermal conductivity hexagonal boron nitride (h-BN) heat-spreading layer between graphene and either a SiO 2 /Si substrate or a 100 μm thick Corning flexible Willow glass (WG) substrate. At the same power density, an 80 nm thick h-BN layer on the silicon substrate can yield a factor of 2.2 reduction of the hot spot temperature, whereas a 35 nm thick h-BN layer on the WG substrate is sufficient to obtain a factor of 4.1 reduction. The larger effect of the h-BN heat spreader on WG than on SiO 2 /Si is attributed to a smaller effective heat transfer coefficient per unit area for three-dimensional heat conduction into the thick, low-thermal conductivity WG substrate than for one-dimensional heat conduction through the thin oxide layer on silicon. Consequently, the h-BN lateral heat-spreading length is much larger on WG than on SiO 2 /Si, resulting in a larger degree of temperature reduction.

  19. Rational solutions to two- and one-dimensional multicomponent Yajima–Oikawa systems

    International Nuclear Information System (INIS)

    Chen, Junchao; Chen, Yong; Feng, Bao-Feng; Maruno, Ken-ichi

    2015-01-01

    Exact explicit rational solutions of two- and one-dimensional multicomponent Yajima–Oikawa (YO) systems, which contain multi-short-wave components and single long-wave one, are presented by using the bilinear method. For two-dimensional system, the fundamental rational solution first describes the localized lumps, which have three different patterns: bright, intermediate and dark states. Then, rogue waves can be obtained under certain parameter conditions and their behaviors are also classified to above three patterns with different definition. It is shown that the simplest (fundamental) rogue waves are line localized waves which arise from the constant background with a line profile and then disappear into the constant background again. In particular, two-dimensional intermediate and dark counterparts of rogue wave are found with the different parameter requirements. We demonstrate that multirogue waves describe the interaction of several fundamental rogue waves, in which interesting curvy wave patterns appear in the intermediate times. Different curvy wave patterns form in the interaction of different types fundamental rogue waves. Higher-order rogue waves exhibit the dynamic behaviors that the wave structures start from lump and then retreat back to it, and this transient wave possesses the patterns such as parabolas. Furthermore, different states of higher-order rogue wave result in completely distinguishing lumps and parabolas. Moreover, one-dimensional rogue wave solutions with three states are constructed through the further reduction. Specifically, higher-order rogue wave in one-dimensional case is derived under the parameter constraints. - Highlights: • Exact explicit rational solutions of two-and one-dimensional multicomponent Yajima–Oikawa systems. • Two-dimensional rogue wave contains three different patterns: bright, intermediate and dark states. • Multi- and higher-order rogue waves exhibit distinct dynamic behaviors in two-dimensional case

  20. ONE-DIMENSIONAL AND TWO-DIMENSIONAL LEADERSHIP STYLES

    Directory of Open Access Journals (Sweden)

    Nikola Stefanović

    2007-06-01

    Full Text Available In order to motivate their group members to perform certain tasks, leaders use different leadership styles. These styles are based on leaders' backgrounds, knowledge, values, experiences, and expectations. The one-dimensional styles, used by many world leaders, are autocratic and democratic styles. These styles lie on the two opposite sides of the leadership spectrum. In order to precisely define the leadership styles on the spectrum between the autocratic leadership style and the democratic leadership style, leadership theory researchers use two dimensional matrices. The two-dimensional matrices define leadership styles on the basis of different parameters. By using these parameters, one can identify two-dimensional styles.