Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

Directory of Open Access Journals (Sweden)

Alim Samat

2016-03-01

Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

An adaptation of Krylov subspace methods to path following

Energy Technology Data Exchange (ETDEWEB)

Walker, H.F. [Utah State Univ., Logan, UT (United States)

1996-12-31

Krylov subspace methods at present constitute a very well known and highly developed class of iterative linear algebra methods. These have been effectively applied to nonlinear system solving through Newton-Krylov methods, in which Krylov subspace methods are used to solve the linear systems that characterize steps of Newton`s method (the Newton equations). Here, we will discuss the application of Krylov subspace methods to path following problems, in which the object is to track a solution curve as a parameter varies. Path following methods are typically of predictor-corrector form, in which a point near the solution curve is {open_quotes}predicted{close_quotes} by some easy but relatively inaccurate means, and then a series of Newton-like corrector iterations is used to return approximately to the curve. The analogue of the Newton equation is underdetermined, and an additional linear condition must be specified to determine corrector steps uniquely. This is typically done by requiring that the steps be orthogonal to an approximate tangent direction. Augmenting the under-determined system with this orthogonality condition in a straightforward way typically works well if direct linear algebra methods are used, but Krylov subspace methods are often ineffective with this approach. We will discuss recent work in which this orthogonality condition is imposed directly as a constraint on the corrector steps in a certain way. The means of doing this preserves problem conditioning, allows the use of preconditioners constructed for the fixed-parameter case, and has certain other advantages. Experiments on standard PDE continuation test problems indicate that this approach is effective.

OpenSubspace

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack...... of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets...... these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering....

Greedy subspace clustering.

Science.gov (United States)

2016-09-01

We consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets ...

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

Reduced Wiener Chaos representation of random fields via basis adaptation and projection

Energy Technology Data Exchange (ETDEWEB)

Tsilifis, Panagiotis, E-mail: tsilifis@usc.edu [Department of Mathematics, University of Southern California, Los Angeles, CA 90089 (United States); Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States); Ghanem, Roger G., E-mail: ghanem@usc.edu [Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States)

2017-07-15

A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptation capabilities of these expansions where the core idea is to rotate the basis of the underlying Gaussian Hilbert space, in order to achieve reduced functional representations that concentrate the induced probability measure in a lower dimensional subspace. For a smooth family of rotations along the domain of interest, the uncorrelated Gaussian inputs are transformed into a Gaussian process, thus introducing a mesoscale that captures intermediate characteristics of the quantity of interest.

Subspace-based interference removal methods for a multichannel biomagnetic sensor array

Science.gov (United States)

Sekihara, Kensuke; Nagarajan, Srikantan S.

2017-10-01

Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

Science.gov (United States)

Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

2018-05-08

When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

Scalable Density-Based Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

An efficient preconditioning technique using Krylov subspace methods for 3D characteristics solvers

International Nuclear Information System (INIS)

Dahmani, M.; Le Tellier, R.; Roy, R.; Hebert, A.

2005-01-01

The Generalized Minimal RESidual (GMRES) method, using a Krylov subspace projection, is adapted and implemented to accelerate a 3D iterative transport solver based on the characteristics method. Another acceleration technique called the self-collision rebalancing technique (SCR) can also be used to accelerate the solution or as a left preconditioner for GMRES. The GMRES method is usually used to solve a linear algebraic system (Ax=b). It uses K(r (o) ,A) as projection subspace and AK(r (o) ,A) for the orthogonalization of the residual. This paper compares the performance of these two combined methods on various problems. To implement the GMRES iterative method, the characteristics equations are derived in linear algebra formalism by using the equivalence between the method of characteristics and the method of collision probability to end up with a linear algebraic system involving fluxes and currents. Numerical results show good performance of the GMRES technique especially for the cases presenting large material heterogeneity with a scattering ratio close to 1. Similarly, the SCR preconditioning slightly increases the GMRES efficiency

Different structures on subspaces of OsckM

Directory of Open Access Journals (Sweden)

Čomić Irena

2013-01-01

Full Text Available The geometry of OsckM spaces was introduced by R. Miron and Gh. Atanasiu in [6] and [7]. The theory of these spaces was developed by R. Miron and his cooperators from Romania, Japan and other countries in several books and many papers. Only some of them are mentioned in references. Here we recall the construction of adapted bases in T(OsckM and T*(OsckM, which are comprehensive with the J structure. The theory of two complementary family of subspaces is presented as it was done in [2] and [4]. The operators J,J, θ,θ, p, p* are introduced in the ambient space and subspaces. Some new relations between them are established. The action of these operators on Liouville vector fields are examined.

Subspace K-means clustering.

Science.gov (United States)

Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-12-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

Geometric mean for subspace selection.

Science.gov (United States)

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2009-02-01

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.

INDOOR SUBSPACING TO IMPLEMENT INDOORGML FOR INDOOR NAVIGATION

Directory of Open Access Journals (Sweden)

H. Jung

2015-10-01

Full Text Available According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

Indoor Subspacing to Implement Indoorgml for Indoor Navigation

Science.gov (United States)

Jung, H.; Lee, J.

2015-10-01

According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

Controllable Subspaces of Open Quantum Dynamical Systems

International Nuclear Information System (INIS)

Zhang Ming; Gong Erling; Xie Hongwei; Hu Dewen; Dai Hongyi

2008-01-01

This paper discusses the concept of controllable subspace for open quantum dynamical systems. It is constructively demonstrated that combining structural features of decoherence-free subspaces with the ability to perform open-loop coherent control on open quantum systems will allow decoherence-free subspaces to be controllable. This is in contrast to the observation that open quantum dynamical systems are not open-loop controllable. To a certain extent, this paper gives an alternative control theoretical interpretation on why decoherence-free subspaces can be useful for quantum computation.

Consistency Analysis of Nearest Subspace Classifier

OpenAIRE

Wang, Yi

2015-01-01

The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...

Subspace preservation, subspace locality, and gluing of completely positive maps

International Nuclear Information System (INIS)

Aaberg, Johan

2004-01-01

Three concepts concerning completely positive maps (CPMs) and trace preserving CPMs (channels) are introduced and investigated. These are named subspace preserving (SP) CPMs, subspace local (SL) channels, and gluing of CPMs. SP CPMs has, in the case of trace preserving CPMs, a simple interpretation as those which preserve probability weights on a given orthogonal sum decomposition of the Hilbert space of a quantum system. The proposed definition of subspace locality of quantum channels is an attempt to answer the question of what kind of restriction should be put on a channel, if it is to act 'locally' with respect to two 'locations', when these naturally correspond to a separation of the total Hilbert space in an orthogonal sum of subspaces, rather than a tensor product decomposition. As a description of the concept of gluings of quantum channels, consider a pair of 'evolution machines', each with the ability to evolve the internal state of a 'particle' inserted into its input. Each of these machines is characterized by a channel describing the operation the internal state has experienced when the particle is returned at the output. Suppose a particle is put in a superposition between the input of the first and the second machine. Here it is shown that the total evolution caused by a pair of such devices is not uniquely determined by the channels of the two machines. Such 'global' channels describing the machine pair are examples of gluings of the two single machine channels. Various expressions to generate the set of SP and SL channels, as well as expressions to generate the set of gluings of given channels, are deduced. We discuss conceptual aspects of the nature of these channels and the nature of the non-uniqueness of gluings

Active Subspaces of Airfoil Shape Parameterizations

Science.gov (United States)

Grey, Zachary J.; Constantine, Paul G.

2018-05-01

Design and optimization benefit from understanding the dependence of a quantity of interest (e.g., a design objective or constraint function) on the design variables. A low-dimensional active subspace, when present, identifies important directions in the space of design variables; perturbing a design along the active subspace associated with a particular quantity of interest changes that quantity more, on average, than perturbing the design orthogonally to the active subspace. This low-dimensional structure provides insights that characterize the dependence of quantities of interest on design variables. Airfoil design in a transonic flow field with a parameterized geometry is a popular test problem for design methodologies. We examine two particular airfoil shape parameterizations, PARSEC and CST, and study the active subspaces present in two common design quantities of interest, transonic lift and drag coefficients, under each shape parameterization. We mathematically relate the two parameterizations with a common polynomial series. The active subspaces enable low-dimensional approximations of lift and drag that relate to physical airfoil properties. In particular, we obtain and interpret a two-dimensional approximation of both transonic lift and drag, and we show how these approximation inform a multi-objective design problem.

Unsupervised spike sorting based on discriminative subspace learning.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

External Evaluation Measures for Subspace Clustering

DEFF Research Database (Denmark)

Günnemann, Stephan; Färber, Ines; Müller, Emmanuel

2011-01-01

research area of subspace clustering. We formalize general quality criteria for subspace clustering measures not yet addressed in the literature. We compare the existing external evaluation methods based on these criteria and pinpoint limitations. We propose a novel external evaluation measure which meets...

Subspace learning from image gradient orientations

NARCIS (Netherlands)

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

2012-01-01

We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Directory of Open Access Journals (Sweden)

Yu Ding

2018-01-01

Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

Subspace Based Blind Sparse Channel Estimation

DEFF Research Database (Denmark)

Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

2012-01-01

The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

Subspace exclusion zones for damage localization

DEFF Research Database (Denmark)

Bernal, Dionisio; Ulriksen, Martin Dalgaard

2018-01-01

, this is exploited in the context of structural damage localization to cast the Subspace Exclusion Zone (SEZ) scheme, which locates damage by reconstructing the captured field quantity shifts from analytical subspaces indexed by postulated boundaries, the so-called exclusion zones (EZs), in a model of the structure...

Subspace methods for pattern recognition in intelligent environment

CERN Document Server

Jain, Lakhmi

2014-01-01

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

The effect of self-distancing on adaptive versus maladaptive self-reflection in children.

Science.gov (United States)

Kross, Ethan; Duckworth, Angela; Ayduk, Ozlem; Tsukayama, Eli; Mischel, Walter

2011-10-01

Although children and adolescents vary in their chronic tendencies to adaptively versus maladaptively reflect over negative feelings, the psychological mechanisms underlying these different types of self-reflection among youngsters are unknown. We addressed this issue in the present research by examining the role that self-distancing plays in distinguishing adaptive versus maladaptive self-reflection among an ethnically and socioeconomically diverse sample of fifth-grade public schoolchildren. Children were randomly assigned to analyze their feelings surrounding a recent anger-related interpersonal experience from either a self-immersed or self-distanced perspective. They then rated their negative affect and described in writing the stream of thoughts they experienced when they analyzed their feelings. Children's stream-of-thought essays were content analyzed for the presence of recounting statements, reconstruing statements, and blame attributions. Path analyses indicated that children who analyzed their feelings from a self-distanced perspective focused significantly less on recounting the "hot," emotionally arousing features of their memory (i.e., what happened to me?) and relatively more on reconstruing their experience. This shift in thought content--less recounting and more reconstruing--led children in the self-distanced group to blame the other person involved in their recalled experience significantly less, which in turn led them to display significantly lower levels of emotional reactivity. These findings help delineate the psychological mechanisms that distinguish adaptive versus maladaptive forms of self-reflection over anger experiences in children. Their basic findings and clinical implications are discussed.

On Covering Approximation Subspaces

Directory of Open Access Journals (Sweden)

Xun Ge

2009-06-01

Full Text Available Let (U';C' be a subspace of a covering approximation space (U;C and X⊂U'. In this paper, we show that and B'(X⊂B(X∩U'. Also, iff (U;C has Property Multiplication. Furthermore, some connections between outer (resp. inner definable subsets in (U;C and outer (resp. inner definable subsets in (U';C' are established. These results answer a question on covering approximation subspace posed by J. Li, and are helpful to obtain further applications of Pawlak rough set theory in pattern recognition and artificial intelligence.

Active Subspaces for Wind Plant Surrogate Modeling

Energy Technology Data Exchange (ETDEWEB)

King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Adcock, Christiane [Massachusetts Institute of Technology

2018-01-12

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.

Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

Directory of Open Access Journals (Sweden)

Wang Wei

2016-01-01

Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.

Kernel based subspace projection of hyperspectral images

DEFF Research Database (Denmark)

Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten

In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...

Sinusoidal Order Estimation Using Angles between Subspaces

Directory of Open Access Journals (Sweden)

Søren Holdt Jensen

2009-01-01

Full Text Available We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.

An alternative subspace approach to EEG dipole source localization

Science.gov (United States)

Xu, Xiao-Liang; Xu, Bobby; He, Bin

2004-01-01

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

An alternative subspace approach to EEG dipole source localization

International Nuclear Information System (INIS)

Xu Xiaoliang; Xu, Bobby; He Bin

2004-01-01

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist

Shape analysis with subspace symmetries

KAUST Repository

Berner, Alexander

2011-04-01

We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising. © 2010 The Author(s).

Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation

OpenAIRE

Bi, Qiao; Guo, Liu; Ruda, H. E.

2010-01-01

A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.

Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation

Directory of Open Access Journals (Sweden)

Qiao Bi

2010-01-01

Full Text Available A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.

LBAS: Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems

DEFF Research Database (Denmark)

Hansen, Per Christian; Abe, Kyniyoshi

The regularizing properties of Lanczos bidiagonalization are powerful when the underlying Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by augmenting the Krylov subspace with a low-dimensional subspace tha...

Detecting anomalies in crowded scenes via locality-constrained affine subspace coding

Science.gov (United States)

Fan, Yaxiang; Wen, Gongjian; Qiu, Shaohua; Li, Deren

2017-07-01

Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.

Breaking of separability condition for dynamical collective subspace; Onset of quantum chaos in large-amplitude collective motion

Energy Technology Data Exchange (ETDEWEB)

Sakata, Fumihiko [Tokyo Univ., Tanashi (Japan). Inst. for Nuclear Study; Yamamoto, Yoshifumi; Marumori, Toshio; Iida, Shinji; Tsukuma, Hidehiko

1989-11-01

It is the purpose of the present paper to study 'global structure' of the state space of an N-body interacting fermion system, which exhibits regular, transient and stochastic phases depending on strength of the interaction. An optimum representation called a dynamical representation plays an essential role in this investigation. The concept of the dynamical representation has been introduced in the quantum theory of dynamical subspace in our previous paper, in order to determine self-consistently an optimum collective subspace as well as an optimum collective Hamiltonian. In the theory, furthermore, dynamical conditions called separability and stability conditions have been provided in order to identify the optimum collective subspace as an approximate invariant subspace of the Hamiltonian. Physical meaning of these conditions are clarified from a viewpoint to relate breaking of them with bifurcation of the collectivity and an onset of quantum chaos from the regular collective motion, by illustrating the general idea with numerical results obtained for a simple soluble model. It turns out that the onset of the stochastic phase is associated with dissolution of the quantum numbers to specify the collective subspace and this dissolution is induced by the breaking of the separability condition in the dynamical representation. (author).

Subspace System Identification of the Kalman Filter

Directory of Open Access Journals (Sweden)

David Di Ruscio

2003-07-01

Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.

Subspace confinement: how good is your qubit?

International Nuclear Information System (INIS)

Devitt, Simon J; Schirmer, Sonia G; Oi, Daniel K L; Cole, Jared H; Hollenberg, Lloyd C L

2007-01-01

The basic operating element of standard quantum computation is the qubit, an isolated two-level system that can be accurately controlled, initialized and measured. However, the majority of proposed physical architectures for quantum computation are built from systems that contain much more complicated Hilbert space structures. Hence, defining a qubit requires the identification of an appropriate controllable two-dimensional sub-system. This prompts the obvious question of how well a qubit, thus defined, is confined to this subspace, and whether we can experimentally quantify the potential leakage into states outside the qubit subspace. We demonstrate how subspace leakage can be characterized using minimal theoretical assumptions by examining the Fourier spectrum of the oscillation experiment

Self-adapted sliding scale spectroscopy ADC

International Nuclear Information System (INIS)

Xu Qichun; Wang Jingjin

1992-01-01

The traditional sliding scale technique causes a disabled range that is equal to the sliding length, thus reduces the analysis range of a MCA. A method for reduce ADC's DNL, which is called self-adapted sliding scale method, has been designed and tested. With this method, the disabled range caused by a traditional sliding scale method can be eliminated by a random trial scale and there is no need of an additional amplitude discriminator with swing threshold. A special trial-and-correct logic is presented. The tested DNL of the spectroscopy ADC described here is less than 0.5%

Two-qubit quantum computing in a projected subspace

International Nuclear Information System (INIS)

Bi Qiao; Ruda, H.E.; Zhan, M.S.

2002-01-01

A formulation for performing quantum computing in a projected subspace is presented, based on the subdynamical kinetic equation (SKE) for an open quantum system. The eigenvectors of the kinetic equation are shown to remain invariant before and after interaction with the environment. However, the eigenvalues in the projected subspace exhibit a type of phase shift to the evolutionary states. This phase shift does not destroy the decoherence-free (DF) property of the subspace because the associated fidelity is 1. This permits a universal formalism to be presented--the eigenprojectors of the free part of the Hamiltonian for the system and bath may be used to construct a DF projected subspace based on the SKE. To eliminate possible phase or unitary errors induced by the change in the eigenvalues, a cancellation technique is proposed, using the adjustment of the coupling time, and applied to a two-qubit computing system. A general criteria for constructing a DF-projected subspace from the SKE is discussed. Finally, a proposal for using triangulation to realize a decoherence-free subsystem based on SKE is presented. The concrete formulation for a two-qubit model is given exactly. Our approach is general and appears to be applicable to any type of decoherence

Spontaneous Self-Distancing and Adaptive Self-Reflection Across Adolescence.

Science.gov (United States)

White, Rachel E; Kross, Ethan; Duckworth, Angela L

2015-07-01

Experiments performed primarily with adults show that self-distancing facilitates adaptive self-reflection. However, no research has investigated whether adolescents spontaneously engage in this process or whether doing so is linked to adaptive outcomes. In this study, 226 African American adolescents, aged 11-20, reflected on an anger-related interpersonal experience. As expected, spontaneous self-distancing during reflection predicted lower levels of emotional reactivity by leading adolescents to reconstrue (rather than recount) their experience and blame their partner less. Moreover, the inverse relation between self-distancing and emotional reactivity strengthened with age. These findings highlight the role that self-distancing plays in fostering adaptive self-reflection in adolescence, and begin to elucidate the role that development plays in enhancing the benefits of engaging in this process. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

[Self-acceptance as adaptively resigning the self to low self-evaluation].

Science.gov (United States)

Ueda, T

1996-10-01

In past studies, the concept of self-acceptance has often been confused with self-evaluation or self-esteem. The purpose of this study was to distinguish these concepts, and operationally define self-acceptance as Carl Rogers proposed: feeling all right toward the self when self-evaluation was low. Self-acceptance as adaptive resignation, a moderating variable, therefore should raise self-esteem of only those people with low self-evaluation. Self-acceptance was measured in the study as affirmative evaluation of own self-evaluation. Two hundred and forty college students, 120 each for men and women, completed a questionnaire of self-evaluative consciousness and self-esteem scales. Results of statistical analyses showed that among subjects with low self-evaluation, the higher self-acceptance, the higher the person's self-esteem. The same relation was not observed among those with high self-evaluation. Thus, it may be concluded that self-acceptance was adaptive resignation, and therefore meaningful to only those with low self-evaluation.

Beamforming using subspace estimation from a diagonally averaged sample covariance.

Science.gov (United States)

Quijano, Jorge E; Zurk, Lisa M

2017-08-01

The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment.

Monomial codes seen as invariant subspaces

Directory of Open Access Journals (Sweden)

García-Planas María Isabel

2017-08-01

Full Text Available It is well known that cyclic codes are very useful because of their applications, since they are not computationally expensive and encoding can be easily implemented. The relationship between cyclic codes and invariant subspaces is also well known. In this paper a generalization of this relationship is presented between monomial codes over a finite field and hyperinvariant subspaces of n under an appropriate linear transformation. Using techniques of Linear Algebra it is possible to deduce certain properties for this particular type of codes, generalizing known results on cyclic codes.

Adiabatic evolution of decoherence-free subspaces and its shortcuts

Science.gov (United States)

Wu, S. L.; Huang, X. L.; Li, H.; Yi, X. X.

2017-10-01

The adiabatic theorem and shortcuts to adiabaticity for time-dependent open quantum systems are explored in this paper. Starting from the definition of dynamical stable decoherence-free subspace, we show that, under a compact adiabatic condition, the quantum state remains in the time-dependent decoherence-free subspace with an extremely high purity, even though the dynamics of the open quantum system may not be adiabatic. The adiabatic condition mentioned here in the adiabatic theorem for open systems is very similar to that for closed quantum systems, except that the operators required to change slowly are the Lindblad operators. We also show that the adiabatic evolution of decoherence-free subspaces depends on the existence of instantaneous decoherence-free subspaces, which requires that the Hamiltonian of open quantum systems be engineered according to the incoherent control protocol. In addition, shortcuts to adiabaticity for adiabatic decoherence-free subspaces are also presented based on the transitionless quantum driving method. Finally, we provide an example that consists of a two-level system coupled to a broadband squeezed vacuum field to show our theory. Our approach employs Markovian master equations and the theory can apply to finite-dimensional quantum open systems.

Subspace-Based Holistic Registration for Low-Resolution Facial Images

Directory of Open Access Journals (Sweden)

Boom BJ

2010-01-01

Full Text Available Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration.

On the maximal dimension of a completely entangled subspace for ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

dim S = d1d2 ...dk − (d1 +···+ dk) + k − 1, where E is the collection of all completely entangled subspaces. When H1 = H2 and k = 2 an explicit orthonormal basis of a maximal completely entangled subspace of H1 ⊗ H2 is given. We also introduce a more delicate notion of a perfectly entangled subspace for a multipartite ...

Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

Science.gov (United States)

Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey

2007-12-01

In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).

Seismic noise attenuation using an online subspace tracking algorithm

NARCIS (Netherlands)

Zhou, Yatong; Li, Shuhua; Zhang, D.; Chen, Yangkang

2018-01-01

We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient

A New Inexact Inverse Subspace Iteration for Generalized Eigenvalue Problems

Directory of Open Access Journals (Sweden)

Fatemeh Mohammad

2014-05-01

Full Text Available In this paper‎, ‎we represent an inexact inverse‎ ‎subspace iteration method for computing a few eigenpairs of the‎ ‎generalized eigenvalue problem $Ax = \\lambda Bx$[Q.~Ye and P.~Zhang‎, ‎Inexact inverse subspace iteration for generalized eigenvalue‎ ‎problems‎, ‎Linear Algebra and its Application‎, ‎434 (2011 1697-1715‎‎]‎. ‎In particular‎, ‎the linear convergence property of the inverse‎ ‎subspace iteration is preserved‎.

Concept of a collective subspace associated with the invariance principle of the Schroedinger equation

International Nuclear Information System (INIS)

Marumori, Toshio; Hayashi, Akihisa; Tomoda, Toshiaki; Kuriyama, Atsushi; Maskawa, Toshihide

1980-01-01

The aim of this series of papers is to propose a microscopic theory to go beyond the situations where collective motions are described by the random phase approximation, i.e., by small amplitude harmonic oscillations about equilibrium. The theory is thus appropriate for the microscopic description of the large amplitude collective motion of soft nuclei. The essential idea is to develop a method to determine the collective subspace (or submanifold) in the many-particle Hilbert space in an optimal way, on the basis of a fundamental principle called the invariance principle of the Schroedinger equation. By using the principle within the framework of the Hartree-Fock theory, it is shown that the theory can clarify the structure of the so-called ''phonon-bands'' by self-consistently deriving the collective Hamiltonian where the number of the ''physical phonon'' is conserved. The purpose of this paper is not to go into detailed quantitative discussion, but rather to develop the basic idea. (author)

Semitransitive subspaces of operators

Czech Academy of Sciences Publication Activity Database

Bernik, J.; Drnovšek, R.; Hadwin, D.; Jafarian, A.; Bukovšek, D.K.; Košir, T.; Fijavž, M.K.; Laffey, T.; Livshits, L.; Mastnak, M.; Meshulam, R.; Müller, Vladimír; Nordgren, E.; Okniński, J.; Omladič, M.; Radjavi, H.; Sourour, A.; Timoney, R.

2006-01-01

Roč. 15, č. 1 (2006), s. 225-238 E-ISSN 1081-3810 Institutional research plan: CEZ:AV0Z10190503 Keywords : semitransitive subspaces Subject RIV: BA - General Mathematics Impact factor: 0.322, year: 2006 http://www.math.technion.ac.il/iic/ ela

Self-rumination, self-reflection, and depression: self-rumination counteracts the adaptive effect of self-reflection.

Science.gov (United States)

Takano, Keisuke; Tanno, Yoshihiko

2009-03-01

Self-focused attention has adaptive and maladaptive aspects: self-reflection and self-rumination [Trapnell, P. D., & Campbell, J. D. (1999). Private self-consciousness and the Five-Factor Model of personality: distinguishing rumination from reflection. Journal of Personality and Social Psychology, 76, 284-304]. Although reflection is thought to be associated with problem solving and the promotion of mental health, previous researches have shown that reflection does not always have an adaptive effect on depression. Authors have examined the causes behind this inconsistency by modeling the relationships among self-reflection, self-rumination, and depression. One hundred and eleven undergraduates (91 men and 20 women) participated in a two-time point assessment with a 3-week interval. Statistical analysis with structural equation modeling showed that self-reflection significantly predicted self-rumination, whereas self-rumination did not predict self-reflection. With regard to depression, self-reflection was associated with a lower level of depression; self-rumination, with a higher level of depression. The total effect of self-reflection on depression was almost zero. This result indicates that self-reflection per se has an adaptive effect, which is canceled out by the maladaptive effect of self-rumination, because reflectors are likely to ruminate and reflect simultaneously.

Subspace orthogonalization for substructuring preconditioners for nonsymmetric systems of linear equations

Energy Technology Data Exchange (ETDEWEB)

Starke, G. [Universitaet Karlsruhe (Germany)

1994-12-31

For nonselfadjoint elliptic boundary value problems which are preconditioned by a substructuring method, i.e., nonoverlapping domain decomposition, the author introduces and studies the concept of subspace orthogonalization. In subspace orthogonalization variants of Krylov methods the computation of inner products and vector updates, and the storage of basis elements is restricted to a (presumably small) subspace, in this case the edge and vertex unknowns with respect to the partitioning into subdomains. The author investigates subspace orthogonalization for two specific iterative algorithms, GMRES and the full orthogonalization method (FOM). This is intended to eliminate certain drawbacks of the Arnoldi-based Krylov subspace methods mentioned above. Above all, the length of the Arnoldi recurrences grows linearly with the iteration index which is therefore restricted to the number of basis elements that can be held in memory. Restarts become necessary and this often results in much slower convergence. The subspace orthogonalization methods, in contrast, require the storage of only the edge and vertex unknowns of each basis element which means that one can iterate much longer before restarts become necessary. Moreover, the computation of inner products is also restricted to the edge and vertex points which avoids the disturbance of the computational flow associated with the solution of subdomain problems. The author views subspace orthogonalization as an alternative to restarting or truncating Krylov subspace methods for nonsymmetric linear systems of equations. Instead of shortening the recurrences, one restricts them to a subset of the unknowns which has to be carefully chosen in order to be able to extend this partial solution to the entire space. The author discusses the convergence properties of these iteration schemes and its advantages compared to restarted or truncated versions of Krylov methods applied to the full preconditioned system.

Subspace K-means clustering

NARCIS (Netherlands)

Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-01-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the

Free probability and random matrices

CERN Document Server

Mingo, James A

2017-01-01

This volume opens the world of free probability to a wide variety of readers. From its roots in the theory of operator algebras, free probability has intertwined with non-crossing partitions, random matrices, applications in wireless communications, representation theory of large groups, quantum groups, the invariant subspace problem, large deviations, subfactors, and beyond. This book puts a special emphasis on the relation of free probability to random matrices, but also touches upon the operator algebraic, combinatorial, and analytic aspects of the theory. The book serves as a combination textbook/research monograph, with self-contained chapters, exercises scattered throughout the text, and coverage of important ongoing progress of the theory. It will appeal to graduate students and all mathematicians interested in random matrices and free probability from the point of view of operator algebras, combinatorics, analytic functions, or applications in engineering and statistical physics.

Conjunctive patches subspace learning with side information for collaborative image retrieval.

Science.gov (United States)

Zhang, Lining; Wang, Lipo; Lin, Weisi

2012-08-01

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

The Role of Item Feedback in Self-Adapted Testing.

Science.gov (United States)

Roos, Linda L.; And Others

1997-01-01

The importance of item feedback in self-adapted testing was studied by comparing feedback and no feedback conditions for computerized adaptive tests and self-adapted tests taken by 363 college students. Results indicate that item feedback is not necessary to realize score differences between self-adapted and computerized adaptive testing. (SLD)

Gamow state vectors as functionals over subspaces of the nuclear space

International Nuclear Information System (INIS)

Bohm, A.

1979-12-01

Exponentially decaying Gamow state vectors are obtained from S-matrix poles in the lower half of the second sheet, and are defined as functionals over a subspace of the nuclear space, PHI. Exponentially growing Gamow state vectors are obtained from S-matrix poles in the upper half of the second sheet, and are defined as functionals over another subspace of PHI. On functionals over these two subspaces the dynamical group of time development splits into two semigroups

Matrix Krylov subspace methods for image restoration

Directory of Open Access Journals (Sweden)

khalide jbilou

2015-09-01

Full Text Available In the present paper, we consider some matrix Krylov subspace methods for solving ill-posed linear matrix equations and in those problems coming from the restoration of blurred and noisy images. Applying the well known Tikhonov regularization procedure leads to a Sylvester matrix equation depending the Tikhonov regularized parameter. We apply the matrix versions of the well known Krylov subspace methods, namely the Least Squared (LSQR and the conjugate gradient (CG methods to get approximate solutions representing the restored images. Some numerical tests are presented to show the effectiveness of the proposed methods.

On the numerical stability analysis of pipelined Krylov subspace methods

Czech Academy of Sciences Publication Activity Database

Carson, E.T.; Rozložník, Miroslav; Strakoš, Z.; Tichý, P.; Tůma, M.

submitted 2017 (2018) R&D Projects: GA ČR GA13-06684S Grant - others:GA MŠk(CZ) LL1202 Institutional support: RVO:67985807 Keywords : Krylov subspace methods * the conjugate gradient method * numerical stability * inexact computations * delay of convergence * maximal attainable accuracy * pipelined Krylov subspace methods * exascale computations

Microscopic theory of dynamical subspace for large amplitude collective motion

International Nuclear Information System (INIS)

Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.

1986-01-01

A full quantum theory appropriate for describing large amplitude collective motion is proposed by exploiting the basic idea of the semi-classical theory so far developed within the time-depedent Hartree-Fock theory. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation specific for the collective subspace where the large amplitude collective motion is replicated as precisely as possible. As an extension of the semi-classical theory where the concept of an approximate integral surface played an important role, the collective subspace is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)

Adapting hypertension self-management interventions to enhance their sustained effectiveness among urban African Americans.

Science.gov (United States)

Ameling, Jessica M; Ephraim, Patti L; Bone, Lee R; Levine, David M; Roter, Debra L; Wolff, Jennifer L; Hill-Briggs, Felicia; Fitzpatrick, Stephanie L; Noronha, Gary J; Fagan, Peter J; Lewis-Boyer, LaPricia; Hickman, Debra; Simmons, Michelle; Purnell, Leon; Fisher, Annette; Cooper, Lisa A; Aboumatar, Hanan J; Albert, Michael C; Flynn, Sarah J; Boulware, L Ebony

2014-01-01

African Americans suffer disproportionately poor hypertension control despite the availability of efficacious interventions. Using principles of community-based participatory research and implementation science, we adapted established hypertension self-management interventions to enhance interventions' cultural relevance and potential for sustained effectiveness among urban African Americans. We obtained input from patients and their family members, their health care providers, and community members. The process required substantial time and resources, and the adapted interventions will be tested in a randomized controlled trial.

Self-Adaptive Systems for Machine Intelligence

CERN Document Server

He, Haibo

2011-01-01

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application

Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Directory of Open Access Journals (Sweden)

Hong-bin Tu

2013-01-01

Full Text Available Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA. Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

Independence and totalness of subspaces in phase space methods

Science.gov (United States)

Vourdas, A.

2018-04-01

The concepts of independence and totalness of subspaces are introduced in the context of quasi-probability distributions in phase space, for quantum systems with finite-dimensional Hilbert space. It is shown that due to the non-distributivity of the lattice of subspaces, there are various levels of independence, from pairwise independence up to (full) independence. Pairwise totalness, totalness and other intermediate concepts are also introduced, which roughly express that the subspaces overlap strongly among themselves, and they cover the full Hilbert space. A duality between independence and totalness, that involves orthocomplementation (logical NOT operation), is discussed. Another approach to independence is also studied, using Rota's formalism on independent partitions of the Hilbert space. This is used to define informational independence, which is proved to be equivalent to independence. As an application, the pentagram (used in discussions on contextuality) is analysed using these concepts.

Visual characterization and diversity quantification of chemical libraries: 2. Analysis and selection of size-independent, subspace-specific diversity indices.

Science.gov (United States)

Colliandre, Lionel; Le Guilloux, Vincent; Bourg, Stephane; Morin-Allory, Luc

2012-02-27

High Throughput Screening (HTS) is a standard technique widely used to find hit compounds in drug discovery projects. The high costs associated with such experiments have highlighted the need to carefully design screening libraries in order to avoid wasting resources. Molecular diversity is an established concept that has been used to this end for many years. In this article, a new approach to quantify the molecular diversity of screening libraries is presented. The approach is based on the Delimited Reference Chemical Subspace (DRCS) methodology, a new method that can be used to delimit the densest subspace spanned by a reference library in a reduced 2D continuous space. A total of 22 diversity indices were implemented or adapted to this methodology, which is used here to remove outliers and obtain a relevant cell-based partition of the subspace. The behavior of these indices was assessed and compared in various extreme situations and with respect to a set of theoretical rules that a diversity function should satisfy when libraries of different sizes have to be compared. Some gold standard indices are found inappropriate in such a context, while none of the tested indices behave perfectly in all cases. Five DRCS-based indices accounting for different aspects of diversity were finally selected, and a simple framework is proposed to use them effectively. Various libraries have been profiled with respect to more specific subspaces, which further illustrate the interest of the method.

Adapting Hypertension Self-Management Interventions to Enhance their Sustained Effectiveness among Urban African Americans

Science.gov (United States)

Ameling, Jessica M.; Ephraim, Patti L.; Bone, Lee R.; Levine, David M.; Roter, Debra L.; Wolff, Jennifer L.; Hill-Briggs, Felicia; Fitzpatrick, Stephanie L.; Noronha, Gary J.; Fagan, Peter J.; Lewis-Boyer, LaPricia; Hickman, Debra; Simmons, Michelle; Purnell, Leon; Fisher, Annette; Cooper, Lisa A.; Aboumatar, Hanan J.; Albert, Michael C.; Flynn, Sarah J.; Boulware, L. Ebony

2014-01-01

African Americans suffer disproportionately poor hypertension control despite the availability of efficacious interventions. Using principles of community-based participatory research and implementation science, we adapted established hypertension self-management interventions to enhance interventions’ cultural relevance and potential for sustained effectiveness among urban African Americans. We obtained input from patients and their family members, their health care providers, and community members. The process required substantial time and resources, and the adapted interventions will be tested in a randomized controlled trial. PMID:24569158

An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

Science.gov (United States)

Abba, Sani; Lee, Jeong-A

2015-08-18

We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

On spectral subspaces and their applications to automorphism groups

International Nuclear Information System (INIS)

Olesen, Dorte

1974-03-01

An attempt is made to give a survey of the theory of spectra and spectral subspaces of group representations in an abstract Banach space setting. The theory is applied to the groups of automorphisms of operator algebras (mostly C*-algebras) and some important results of interest for mathematical physicists are proved (restrictions of the bitransposed action, spectral subspaces for the transposed action on a C*-algebra, and positive states and representations of Rsup(n)) [fr

Physiological Self-Regulation and Adaptive Automation

Science.gov (United States)

Prinzell, Lawrence J.; Pope, Alan T.; Freeman, Frederick G.

2007-01-01

Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.

Adaptive Algebraic Multigrid for Finite Element Elliptic Equations with Random Coefficients

Energy Technology Data Exchange (ETDEWEB)

Kalchev, D

2012-04-02

This thesis presents a two-grid algorithm based on Smoothed Aggregation Spectral Element Agglomeration Algebraic Multigrid (SA-{rho}AMGe) combined with adaptation. The aim is to build an efficient solver for the linear systems arising from discretization of second-order elliptic partial differential equations (PDEs) with stochastic coefficients. Examples include PDEs that model subsurface flow with random permeability field. During a Markov Chain Monte Carlo (MCMC) simulation process, that draws PDE coefficient samples from a certain distribution, the PDE coefficients change, hence the resulting linear systems to be solved change. At every such step the system (discretized PDE) needs to be solved and the computed solution used to evaluate some functional(s) of interest that then determine if the coefficient sample is acceptable or not. The MCMC process is hence computationally intensive and requires the solvers used to be efficient and fast. This fact that at every step of MCMC the resulting linear system changes, makes an already existing solver built for the old problem perhaps not as efficient for the problem corresponding to the new sampled coefficient. This motivates the main goal of our study, namely, to adapt an already existing solver to handle the problem (with changed coefficient) with the objective to achieve this goal to be faster and more efficient than building a completely new solver from scratch. Our approach utilizes the local element matrices (for the problem with changed coefficients) to build local problems associated with constructed by the method agglomerated elements (a set of subdomains that cover the given computational domain). We solve a generalized eigenproblem for each set in a subspace spanned by the previous local coarse space (used for the old solver) and a vector, component of the error, that the old solver cannot handle. A portion of the spectrum of these local eigen-problems (corresponding to eigenvalues close to zero) form the

Extending the subspace hybrid method for eigenvalue problems in reactor physics calculation

International Nuclear Information System (INIS)

Zhang, Q.; Abdel-Khalik, H. S.

2013-01-01

This paper presents an innovative hybrid Monte-Carlo-Deterministic method denoted by the SUBSPACE method designed for improving the efficiency of hybrid methods for reactor analysis applications. The SUBSPACE method achieves its high computational efficiency by taking advantage of the existing correlations between desired responses. Recently, significant gains in computational efficiency have been demonstrated using this method for source driven problems. Within this work the mathematical theory behind the SUBSPACE method is introduced and extended to address core wide level k-eigenvalue problems. The method's efficiency is demonstrated based on a three-dimensional quarter-core problem, where responses are sought on the pin cell level. The SUBSPACE method is compared to the FW-CADIS method and is found to be more efficient for the utilized test problem because of the reason that the FW-CADIS method solves a forward eigenvalue problem and an adjoint fixed-source problem while the SUBSPACE method only solves an adjoint fixed-source problem. Based on the favorable results obtained here, we are confident that the applicability of Monte Carlo for large scale reactor analysis could be realized in the near future. (authors)

Estimating absolute configurational entropies of macromolecules: the minimally coupled subspace approach.

Directory of Open Access Journals (Sweden)

Ulf Hensen

Full Text Available We develop a general minimally coupled subspace approach (MCSA to compute absolute entropies of macromolecules, such as proteins, from computer generated canonical ensembles. Our approach overcomes limitations of current estimates such as the quasi-harmonic approximation which neglects non-linear and higher-order correlations as well as multi-minima characteristics of protein energy landscapes. Here, Full Correlation Analysis, adaptive kernel density estimation, and mutual information expansions are combined and high accuracy is demonstrated for a number of test systems ranging from alkanes to a 14 residue peptide. We further computed the configurational entropy for the full 67-residue cofactor of the TATA box binding protein illustrating that MCSA yields improved results also for large macromolecular systems.

A generalized Schwinger boson mapping with a physical subspace

International Nuclear Information System (INIS)

Scholtz, F.G.; Geyer, H.B.

1988-01-01

We investigate the existence of a physical subspace for generalized Schwinger boson mappings of SO(2n+1) contains SO(2n) in view of previous observations by Marshalek and the recent construction of such a mapping and subspace for SO(8) by Kaup. It is shown that Kaup's construction can be attributed to the existence of a unique SO(8) automorphism. We proceed to construct a generalized Schwinger-type mapping for SO(2n+1) contains SO(2n) which, in contrast to a similar attempt by Yamamura and Nishiyama, indeed has a corresponding physical subspace. This new mapping includes in the special case of SO(8) the mapping by Kaup which is equivalent to the one given by Yamamura and Nishiyama for n=4. Nevertheless, we indicate the limitations of the generalized Schwinger mapping regarding its applicability to situations where one seeks to establish a direct link between phenomenological boson models and an underlying fermion microscopy. (orig.)

An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks

Science.gov (United States)

Abba, Sani; Lee, Jeong-A

2015-01-01

We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network. PMID:26295236

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Directory of Open Access Journals (Sweden)

Debesh Jha

2017-01-01

Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.

Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization

KAUST Repository

Fornasier, Massimo

2009-01-01

This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via oblique thresholding, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for degenerate elliptic PDEs arising in total variation minimization and in accelerated sparse recovery algorithms based on 1-minimization. We include numerical examples which show e.cient solutions to classical problems in signal and image processing. © 2009 Society for Industrial and Applied Physics.

A subspace preconditioning algorithm for eigenvector/eigenvalue computation

Energy Technology Data Exchange (ETDEWEB)

Bramble, J.H.; Knyazev, A.V.; Pasciak, J.E.

1996-12-31

We consider the problem of computing a modest number of the smallest eigenvalues along with orthogonal bases for the corresponding eigen-spaces of a symmetric positive definite matrix. In our applications, the dimension of a matrix is large and the cost of its inverting is prohibitive. In this paper, we shall develop an effective parallelizable technique for computing these eigenvalues and eigenvectors utilizing subspace iteration and preconditioning. Estimates will be provided which show that the preconditioned method converges linearly and uniformly in the matrix dimension when used with a uniform preconditioner under the assumption that the approximating subspace is close enough to the span of desired eigenvectors.

The influence of different PAST-based subspace trackers on DaPT parameter estimation

Science.gov (United States)

Lechtenberg, M.; Götze, J.

2012-09-01

In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers. One class of these is based on the PAST algorithm. Our non-linear parameter estimation algorithm DaPT builds on-top of the ESPRIT algorithm. Evaluation of the different variants of the PAST algorithm shows which variant of the PAST algorithm is worthwhile in the context of frequency estimation.

Catalytic micromotor generating self-propelled regular motion through random fluctuation

Science.gov (United States)

Yamamoto, Daigo; Mukai, Atsushi; Okita, Naoaki; Yoshikawa, Kenichi; Shioi, Akihisa

2013-07-01

Most of the current studies on nano/microscale motors to generate regular motion have adapted the strategy to fabricate a composite with different materials. In this paper, we report that a simple object solely made of platinum generates regular motion driven by a catalytic chemical reaction with hydrogen peroxide. Depending on the morphological symmetry of the catalytic particles, a rich variety of random and regular motions are observed. The experimental trend is well reproduced by a simple theoretical model by taking into account of the anisotropic viscous effect on the self-propelled active Brownian fluctuation.

Two-Level Chebyshev Filter Based Complementary Subspace Method: Pushing the Envelope of Large-Scale Electronic Structure Calculations.

Science.gov (United States)

Banerjee, Amartya S; Lin, Lin; Suryanarayana, Phanish; Yang, Chao; Pask, John E

2018-06-12

We describe a novel iterative strategy for Kohn-Sham density functional theory calculations aimed at large systems (>1,000 electrons), applicable to metals and insulators alike. In lieu of explicit diagonalization of the Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ a two-level Chebyshev polynomial filter based complementary subspace strategy to (1) compute a set of vectors that span the occupied subspace of the Hamiltonian; (2) reduce subspace diagonalization to just partially occupied states; and (3) obtain those states in an efficient, scalable manner via an inner Chebyshev filter iteration. By reducing the necessary computation to just partially occupied states and obtaining these through an inner Chebyshev iteration, our approach reduces the cost of large metallic calculations significantly, while eliminating subspace diagonalization for insulating systems altogether. We describe the implementation of the method within the framework of the discontinuous Galerkin (DG) electronic structure method and show that this results in a computational scheme that can effectively tackle bulk and nano systems containing tens of thousands of electrons, with chemical accuracy, within a few minutes or less of wall clock time per SCF iteration on large-scale computing platforms. We anticipate that our method will be instrumental in pushing the envelope of large-scale ab initio molecular dynamics. As a demonstration of this, we simulate a bulk silicon system containing 8,000 atoms at finite temperature, and obtain an average SCF step wall time of 51 s on 34,560 processors; thus allowing us to carry out 1.0 ps of ab initio molecular dynamics in approximately 28 h (of wall time).

[Behavioral types in relation to burnout, mobbing, personality, and adaptation of self-conduct in health care workers].

Science.gov (United States)

Domínguez Fernández, Julián Manuel; Padilla Segura, Inés; Domínguez Fernández, Javier; Domínguez Padilla, María

2013-04-01

To define the different patterns of behavior among workers in health care in Ceuta. Cross-sectional and descriptive. SITES AND PARTICIPANTS: 200 randomly selected workers in the Ceuta Health Care Area using a stratified sampling of workplace, job and sex. The instruments used were the MBI, the LIPT by Leymann, a reduced version of the Pinillos CEP, Musitu self concept and adaptation behavior, all adapted in the context of occupational health examinations. Principal components analysis allowed us to define 5 components, one strictly related to the scale of mobbing with 85% of weight; another for burnout with 70% weight; a third to adaptation and family satisfaction with a weight of 64%; a fourth with adaptation, control, emotional self, professional achievement and occupational self-weight of 52%; and a fifth component defined by social evaluations in the levels of extraversion and social adjustment with 73%. Highlights five different behavioral characteristics peculiar interest for clinical work are highlighted: burnout, mobbing, family work satisfaction; individual occupational and sociable satisfaction. Copyright © 2012 Elsevier España, S.L. All rights reserved.

Excluding Noise from Short Krylov Subspace Approximations to the Truncated Singular Value Decomposition (SVD)

Science.gov (United States)

2017-09-27

100 times larger for the minimal Krylov subspace. 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjĜ ¡ 1 jj F SVD...approximation Kn (G;u(0) ) 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjx jj fo r m in x jjĜ x ¡ bjj SVD approximation Kn (G;u(0

DETECTION OF CHANGES OF THE SYSTEM TECHNICAL STATE USING STOCHASTIC SUBSPACE OBSERVATION METHOD

Directory of Open Access Journals (Sweden)

Andrzej Puchalski

2014-03-01

Full Text Available System diagnostics based on vibroacoustics signals, carried out by means of stochastic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diagnostic methods according to data, leading to the generation of residuals allowing failure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine.

Closed and Open Loop Subspace System Identification of the Kalman Filter

Directory of Open Access Journals (Sweden)

David Di Ruscio

2009-04-01

Full Text Available Some methods for consistent closed loop subspace system identification presented in the literature are analyzed and compared to a recently published subspace algorithm for both open as well as for closed loop data, the DSR_e algorithm. Some new variants of this algorithm are presented and discussed. Simulation experiments are included in order to illustrate if the algorithms are variance efficient or not.

Subspace Arrangement Codes and Cryptosystems

Science.gov (United States)

2011-05-09

Signature Date Acceptance for the Trident Scholar Committee Professor Carl E. Wick Associate Director of Midshipmen Research Signature Date SUBSPACE...Professor William Traves. I also thank Professor Carl Wick and the Trident Scholar Committee for providing me with the opportunity to conduct this... Sagan . Why the characteristic polynomial factors. Bulletin of the American Mathematical Society, 36(2):113–133, February 1999. [16] Karen E. Smith

Krylov subspace methods for solving large unsymmetric linear systems

International Nuclear Information System (INIS)

Saad, Y.

1981-01-01

Some algorithms based upon a projection process onto the Krylov subspace K/sub m/ = Span(r 0 , Ar 0 ,...,A/sup m/-1r 0 ) are developed, generalizing the method of conjugate gradients to unsymmetric systems. These methods are extensions of Arnoldi's algorithm for solving eigenvalue problems. The convergence is analyzed in terms of the distance of the solution to the subspace K/sub m/ and some error bounds are established showing, in particular, a similarity with the conjugate gradient method (for symmetric matrices) when the eigenvalues are real. Several numerical experiments are described and discussed

Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning

Science.gov (United States)

2015-03-01

ARL-SR-0318 ● MAR 2015 US Army Research Laboratory Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated...Adaptive Intelligent Tutoring Systems for Self-Regulated Learning by Robert A Sottilare Human Research and Engineering Directorate, ARL...TITLE AND SUBTITLE Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

Krylov Subspace Methods for Complex Non-Hermitian Linear Systems. Thesis

Science.gov (United States)

Freund, Roland W.

1991-01-01

We consider Krylov subspace methods for the solution of large sparse linear systems Ax = b with complex non-Hermitian coefficient matrices. Such linear systems arise in important applications, such as inverse scattering, numerical solution of time-dependent Schrodinger equations, underwater acoustics, eddy current computations, numerical computations in quantum chromodynamics, and numerical conformal mapping. Typically, the resulting coefficient matrices A exhibit special structures, such as complex symmetry, or they are shifted Hermitian matrices. In this paper, we first describe a Krylov subspace approach with iterates defined by a quasi-minimal residual property, the QMR method, for solving general complex non-Hermitian linear systems. Then, we study special Krylov subspace methods designed for the two families of complex symmetric respectively shifted Hermitian linear systems. We also include some results concerning the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported.

Krylov subspace method with communication avoiding technique for linear system obtained from electromagnetic analysis

International Nuclear Information System (INIS)

Ikuno, Soichiro; Chen, Gong; Yamamoto, Susumu; Itoh, Taku; Abe, Kuniyoshi; Nakamura, Hiroaki

2016-01-01

Krylov subspace method and the variable preconditioned Krylov subspace method with communication avoiding technique for a linear system obtained from electromagnetic analysis are numerically investigated. In the k−skip Krylov method, the inner product calculations are expanded by Krylov basis, and the inner product calculations are transformed to the scholar operations. k−skip CG method is applied for the inner-loop solver of Variable Preconditioned Krylov subspace methods, and the converged solution of electromagnetic problem is obtained using the method. (author)

A hydraulic hybrid propulsion method for automobiles with self-adaptive system

International Nuclear Information System (INIS)

Wu, Wei; Hu, Jibin; Yuan, Shihua; Di, Chongfeng

2016-01-01

A hydraulic hybrid vehicle with the self-adaptive system is proposed. The mode-switching between the driving mode and the hydraulic regenerative braking mode is realised by the pressure cross-feedback control. Extensive simulated and tested results are presented. The control parameters are reduced and the energy efficiency can be increased by the self-adaptive system. The mode-switching response is fast. The response time can be adjusted by changing the controlling spool diameter of the hydraulic operated check valve in the self-adaptive system. The closing of the valve becomes faster with a smaller controlling spool diameter. The hydraulic regenerative braking mode can be achieved by changing the hydraulic transformer controlled angle. Compared with the convention electric-hydraulic system, the self-adaptive system for the hydraulic hybrid vehicle mode-switching has a higher reliability and a lower cost. The efficiency of the hydraulic regenerative braking is also increased. - Highlights: • A new hybrid system with a self-adaptive system for automobiles is presented. • The mode-switching is realised by the pressure cross-feedback control. • The energy efficiency can be increased with the self-adaptive system. • The control parameters are reduced with the self-adaptive system.

Robust subspace estimation using low-rank optimization theory and applications

CERN Document Server

Oreifej, Omar

2014-01-01

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

Science.gov (United States)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

Lyapunov vectors and assimilation in the unstable subspace: theory and applications

International Nuclear Information System (INIS)

Palatella, Luigi; Carrassi, Alberto; Trevisan, Anna

2013-01-01

Based on a limited number of noisy observations, estimation algorithms provide a complete description of the state of a system at current time. Estimation algorithms that go under the name of assimilation in the unstable subspace (AUS) exploit the nonlinear stability properties of the forecasting model in their formulation. Errors that grow due to sensitivity to initial conditions are efficiently removed by confining the analysis solution in the unstable and neutral subspace of the system, the subspace spanned by Lyapunov vectors with positive and zero exponents, while the observational noise does not disturb the system along the stable directions. The formulation of the AUS approach in the context of four-dimensional variational assimilation (4DVar-AUS) and the extended Kalman filter (EKF-AUS) and its application to chaotic models is reviewed. In both instances, the AUS algorithms are at least as efficient but simpler to implement and computationally less demanding than their original counterparts. As predicted by the theory when error dynamics is linear, the optimal subspace dimension for 4DVar-AUS is given by the number of positive and null Lyapunov exponents, while the EKF-AUS algorithm, using the same unstable and neutral subspace, recovers the solution of the full EKF algorithm, but dealing with error covariance matrices of a much smaller dimension and significantly reducing the computational burden. Examples of the application to a simplified model of the atmospheric circulation and to the optimal velocity model for traffic dynamics are given. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (paper)

Time-domain simulations for metallic nano-structures - a Krylov-subspace approach beyond the limitations of FDTD

Energy Technology Data Exchange (ETDEWEB)

Koenig, Michael [Institut fuer Theoretische Festkoerperphysik, Universitaet Karlsruhe (Germany); Karlsruhe School of Optics and Photonics (KSOP), Universitaet Karlsruhe (Germany); Niegemann, Jens; Tkeshelashvili, Lasha; Busch, Kurt [Institut fuer Theoretische Festkoerperphysik, Universitaet Karlsruhe (Germany); DFG Forschungszentrum Center for Functional Nanostructures (CFN), Universitaet Karlsruhe (Germany); Karlsruhe School of Optics and Photonics (KSOP), Universitaet Karlsruhe (Germany)

2008-07-01

Numerical simulations of metallic nano-structures are crucial for the efficient design of plasmonic devices. Conventional time-domain solvers such as FDTD introduce large numerical errors especially at metallic surfaces. Our approach combines a discontinuous Galerkin method on an adaptive mesh for the spatial discretisation with a Krylov-subspace technique for the time-stepping procedure. Thus, the higher-order accuracy in both time and space is supported by unconditional stability. As illustrative examples, we compare numerical results obtained with our method against analytical reference solutions and results from FDTD calculations.

Adaptive random walks on the class of Web graphs

Science.gov (United States)

Tadić, B.

2001-09-01

We study random walk with adaptive move strategies on a class of directed graphs with variable wiring diagram. The graphs are grown from the evolution rules compatible with the dynamics of the world-wide Web [B. Tadić, Physica A 293, 273 (2001)], and are characterized by a pair of power-law distributions of out- and in-degree for each value of the parameter β, which measures the degree of rewiring in the graph. The walker adapts its move strategy according to locally available information both on out-degree of the visited node and in-degree of target node. A standard random walk, on the other hand, uses the out-degree only. We compute the distribution of connected subgraphs visited by an ensemble of walkers, the average access time and survival probability of the walks. We discuss these properties of the walk dynamics relative to the changes in the global graph structure when the control parameter β is varied. For β≥ 3, corresponding to the world-wide Web, the access time of the walk to a given level of hierarchy on the graph is much shorter compared to the standard random walk on the same graph. By reducing the amount of rewiring towards rigidity limit β↦βc≲ 0.1, corresponding to the range of naturally occurring biochemical networks, the survival probability of adaptive and standard random walk become increasingly similar. The adaptive random walk can be used as an efficient message-passing algorithm on this class of graphs for large degree of rewiring.

Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

Energy Technology Data Exchange (ETDEWEB)

Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

2015-01-15

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

Counting Subspaces of a Finite Vector Space

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 15; Issue 11. Counting Subspaces of a Finite Vector Space – 1. Amritanshu Prasad. General Article Volume 15 Issue 11 November 2010 pp 977-987. Fulltext. Click here to view fulltext PDF. Permanent link:

Roller Bearing Monitoring by New Subspace-Based Damage Indicator

Directory of Open Access Journals (Sweden)

G. Gautier

2015-01-01

Full Text Available A frequency-band subspace-based damage identification method for fault diagnosis in roller bearings is presented. Subspace-based damage indicators are obtained by filtering the vibration data in the frequency range where damage is likely to occur, that is, around the bearing characteristic frequencies. The proposed method is validated by considering simulated data of a damaged bearing. Also, an experimental case is considered which focuses on collecting the vibration data issued from a run-to-failure test. It is shown that the proposed method can detect bearing defects and, as such, it appears to be an efficient tool for diagnosis purpose.

From a distance: implications of spontaneous self-distancing for adaptive self-reflection.

Science.gov (United States)

Ayduk, Ozlem; Kross, Ethan

2010-05-01

Although recent experimental work indicates that self-distancing facilitates adaptive self-reflection, it remains unclear (a) whether spontaneous self-distancing leads to similar adaptive outcomes, (b) how spontaneous self-distancing relates to avoidance, and (c) how this strategy impacts interpersonal behavior. Three studies examined these issues demonstrating that the more participants spontaneously self-distanced while reflecting on negative memories, the less emotional (Studies 1-3) and cardiovascular (Study 2) reactivity they displayed in the short term. Spontaneous self-distancing was also associated with lower emotional reactivity and intrusive ideation over time (Study 1). The negative association between spontaneous self-distancing and emotional reactivity was mediated by how participants construed their experience (i.e., less recounting relative to reconstruing) rather than avoidance (Studies 1-2). In addition, spontaneous self-distancing was associated with more problem-solving behavior and less reciprocation of negativity during conflicts among couples in ongoing relationships (Study 3). Although spontaneous self-distancing was empirically related to trait rumination, it explained unique variance in predicting key outcomes. 2010 APA, all rights reserved

EVD Dualdating Based Online Subspace Learning

Directory of Open Access Journals (Sweden)

Bo Jin

2014-01-01

Full Text Available Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

Science.gov (United States)

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Integrated Phoneme Subspace Method for Speech Feature Extraction

Directory of Open Access Journals (Sweden)

Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering

Directory of Open Access Journals (Sweden)

Yubao Sun

2015-01-01

Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.

Time stepping free numerical solution of linear differential equations: Krylov subspace versus waveform relaxation

NARCIS (Netherlands)

Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.

2013-01-01

The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation method based on block Krylov subspaces. Second, we compare this new implementation against Krylov subspace methods combined with the shift and invert technique.

s-Step Krylov Subspace Methods as Bottom Solvers for Geometric Multigrid

Energy Technology Data Exchange (ETDEWEB)

Williams, Samuel [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lijewski, Mike [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Almgren, Ann [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Straalen, Brian Van [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Carson, Erin [Univ. of California, Berkeley, CA (United States); Knight, Nicholas [Univ. of California, Berkeley, CA (United States); Demmel, James [Univ. of California, Berkeley, CA (United States)

2014-08-14

Geometric multigrid solvers within adaptive mesh refinement (AMR) applications often reach a point where further coarsening of the grid becomes impractical as individual sub domain sizes approach unity. At this point the most common solution is to use a bottom solver, such as BiCGStab, to reduce the residual by a fixed factor at the coarsest level. Each iteration of BiCGStab requires multiple global reductions (MPI collectives). As the number of BiCGStab iterations required for convergence grows with problem size, and the time for each collective operation increases with machine scale, bottom solves in large-scale applications can constitute a significant fraction of the overall multigrid solve time. In this paper, we implement, evaluate, and optimize a communication-avoiding s-step formulation of BiCGStab (CABiCGStab for short) as a high-performance, distributed-memory bottom solver for geometric multigrid solvers. This is the first time s-step Krylov subspace methods have been leveraged to improve multigrid bottom solver performance. We use a synthetic benchmark for detailed analysis and integrate the best implementation into BoxLib in order to evaluate the benefit of a s-step Krylov subspace method on the multigrid solves found in the applications LMC and Nyx on up to 32,768 cores on the Cray XE6 at NERSC. Overall, we see bottom solver improvements of up to 4.2x on synthetic problems and up to 2.7x in real applications. This results in as much as a 1.5x improvement in solver performance in real applications.

Smart Electrochemical Energy Storage Devices with Self-Protection and Self-Adaptation Abilities.

Science.gov (United States)

Yang, Yun; Yu, Dandan; Wang, Hua; Guo, Lin

2017-12-01

Currently, with booming development and worldwide usage of rechargeable electrochemical energy storage devices, their safety issues, operation stability, service life, and user experience are garnering special attention. Smart and intelligent energy storage devices with self-protection and self-adaptation abilities aiming to address these challenges are being developed with great urgency. In this Progress Report, we highlight recent achievements in the field of smart energy storage systems that could early-detect incoming internal short circuits and self-protect against thermal runaway. Moreover, intelligent devices that are able to take actions and self-adapt in response to external mechanical disruption or deformation, i.e., exhibiting self-healing or shape-memory behaviors, are discussed. Finally, insights into the future development of smart rechargeable energy storage devices are provided. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-adaptive phosphor coating technology for wafer-level scale chip packaging

International Nuclear Information System (INIS)

Zhou Linsong; Rao Haibo; Wang Wei; Wan Xianlong; Liao Junyuan; Wang Xuemei; Zhou Da; Lei Qiaolin

2013-01-01

A new self-adaptive phosphor coating technology has been successfully developed, which adopted a slurry method combined with a self-exposure process. A phosphor suspension in the water-soluble photoresist was applied and exposed to LED blue light itself and developed to form a conformal phosphor coating with self-adaptability to the angular distribution of intensity of blue light and better-performing spatial color uniformity. The self-adaptive phosphor coating technology had been successfully adopted in the wafer surface to realize a wafer-level scale phosphor conformal coating. The first-stage experiments show satisfying results and give an adequate demonstration of the flexibility of self-adaptive coating technology on application of WLSCP. (semiconductor devices)

Outlier Ranking via Subspace Analysis in Multiple Views of the Data

DEFF Research Database (Denmark)

Muller, Emmanuel; Assent, Ira; Iglesias, Patricia

2012-01-01

, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...... principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures....

Krylov subspace methods for the solution of large systems of ODE's

DEFF Research Database (Denmark)

Thomsen, Per Grove; Bjurstrøm, Nils Henrik

1998-01-01

In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified.......In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified....

Quantum Zeno subspaces induced by temperature

Energy Technology Data Exchange (ETDEWEB)

Militello, B.; Scala, M.; Messina, A. [Dipartimento di Fisica dell' Universita di Palermo, Via Archirafi 36, I-90123 Palermo (Italy)

2011-08-15

We discuss the partitioning of the Hilbert space of a quantum system induced by the interaction with another system at thermal equilibrium, showing that the higher the temperature the more effective is the formation of Zeno subspaces. We show that our analysis keeps its validity even in the case of interaction with a bosonic reservoir, provided appropriate limitations of the relevant bandwidth.

Improved Stochastic Subspace System Identification for Structural Health Monitoring

Science.gov (United States)

Chang, Chia-Ming; Loh, Chin-Hsiung

2015-07-01

Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.

DySOA : Making service systems self-adaptive

NARCIS (Netherlands)

Siljee, J; Bosloper, [No Value; Nijhuis, J; Hammer, D; Benatallah, B; Casati, F; Traverso, P

2005-01-01

Service-centric systems exist in a very dynamic environment. This requires these systems to adapt at runtime in order to keep fulfilling their QoS. In order to create self-adaptive service systems, developers should not only design the service architecture, but also need to design the

A subspace approach to high-resolution spectroscopic imaging.

Science.gov (United States)

Lam, Fan; Liang, Zhi-Pei

2014-04-01

To accelerate spectroscopic imaging using sparse sampling of (k,t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio. The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high-dimensional spectroscopic signals reside in a very low-dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high-resolution spatiospectral distributions with good signal-to-noise ratio. More specifically, a hybrid chemical shift imaging/echo-planar spectroscopic imaging pulse sequence is proposed for sparse sampling of (k,t)-space, and a low-rank model-based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction. The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two-dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal-to-noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal-to-noise ratio compared to the accelerated echo-planar spectroscopic imaging experiments. The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible. Copyright © 2014 Wiley Periodicals, Inc.

mHealth intervention to support asthma self-management in adolescents: the ADAPT study.

Science.gov (United States)

Kosse, Richelle C; Bouvy, Marcel L; de Vries, Tjalling W; Kaptein, Ad A; Geers, Harm Cj; van Dijk, Liset; Koster, Ellen S

2017-01-01

Poor medication adherence in adolescents with asthma results in poorly controlled disease and increased morbidity. The aim of the ADolescent Adherence Patient Tool (ADAPT) study is to develop an mHealth intervention to support self-management and to evaluate the effectiveness in improving medication adherence and asthma control. The ADAPT intervention consists of an interactive smartphone application (app) connected to a desktop application for health care providers, in this study, the community pharmacist. The app contains several functions to improve adherence as follows: 1) a questionnaire function to rate asthma symptoms and monitor these over time; 2) short movie clips with medication and disease information; 3) a medication reminder; 4) a chat function with peers; and 5) a chat function with the pharmacist. The pharmacist receives data from the patient's app through the desktop application, which enables the pharmacist to send information and feedback to the patient. The ADAPT intervention is tested in a community pharmacy-based cluster randomized controlled trial in the Netherlands, aiming to include 352 adolescents with asthma. The main outcome is adherence, measured by patient's self-report and refill adherence calculated from pharmacy dispensing records. In addition, asthma control, illness perceptions, medication beliefs, and asthma-related quality of life are measured. This study will provide in-depth knowledge on the effectiveness of an mHealth intervention to support asthma self-management in adolescents. These insights will also be useful for adolescents with other chronic diseases.

Subspace in Linear Algebra: Investigating Students' Concept Images and Interactions with the Formal Definition

Science.gov (United States)

Wawro, Megan; Sweeney, George F.; Rabin, Jeffrey M.

2011-01-01

This paper reports on a study investigating students' ways of conceptualizing key ideas in linear algebra, with the particular results presented here focusing on student interactions with the notion of subspace. In interviews conducted with eight undergraduates, we found students' initial descriptions of subspace often varied substantially from…

Boundary regularity of Nevanlinna domains and univalent functions in model subspaces

International Nuclear Information System (INIS)

Baranov, Anton D; Fedorovskiy, Konstantin Yu

2011-01-01

In the paper we study boundary regularity of Nevanlinna domains, which have appeared in problems of uniform approximation by polyanalytic polynomials. A new method for constructing Nevanlinna domains with essentially irregular nonanalytic boundaries is suggested; this method is based on finding appropriate univalent functions in model subspaces, that is, in subspaces of the form K Θ =H 2 ominus ΘH 2 , where Θ is an inner function. To describe the irregularity of the boundaries of the domains obtained, recent results by Dolzhenko about boundary regularity of conformal mappings are used. Bibliography: 18 titles.

Extended Krylov subspaces approximations of matrix functions. Application to computational electromagnetics

Energy Technology Data Exchange (ETDEWEB)

Druskin, V.; Lee, Ping [Schlumberger-Doll Research, Ridgefield, CT (United States); Knizhnerman, L. [Central Geophysical Expedition, Moscow (Russian Federation)

1996-12-31

There is now a growing interest in the area of using Krylov subspace approximations to compute the actions of matrix functions. The main application of this approach is the solution of ODE systems, obtained after discretization of partial differential equations by method of lines. In the event that the cost of computing the matrix inverse is relatively inexpensive, it is sometimes attractive to solve the ODE using the extended Krylov subspaces, originated by actions of both positive and negative matrix powers. Examples of such problems can be found frequently in computational electromagnetics.

Sparse subspace clustering for data with missing entries and high-rank matrix completion.

Science.gov (United States)

Fan, Jicong; Chow, Tommy W S

2017-09-01

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Subspace identification of distributed clusters of homogeneous systems

NARCIS (Netherlands)

Yu, C.; Verhaegen, M.H.G.

2017-01-01

This note studies the identification of a network comprised of interconnected clusters of LTI systems. Each cluster consists of homogeneous dynamical systems, and its interconnections with the rest of the network are unmeasurable. A subspace identification method is proposed for identifying a single

Parallel Monitors for Self-adaptive Sessions

Directory of Open Access Journals (Sweden)

Mario Coppo

2016-06-01

Full Text Available The paper presents a data-driven model of self-adaptivity for multiparty sessions. System choreography is prescribed by a global type. Participants are incarnated by processes associated with monitors, which control their behaviour. Each participant can access and modify a set of global data, which are able to trigger adaptations in the presence of critical changes of values. The use of the parallel composition for building global types, monitors and processes enables a significant degree of flexibility: an adaptation step can dynamically reconfigure a set of participants only, without altering the remaining participants, even if the two groups communicate.

Adaptation in the fuzzy self-organising controller

DEFF Research Database (Denmark)

Jantzen, Jan; Poulsen, Niels Kjølstad

2003-01-01

This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....

Reverse time migration by Krylov subspace reduced order modeling

Science.gov (United States)

Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali

2018-04-01

Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.

Experimental fault-tolerant quantum cryptography in a decoherence-free subspace

International Nuclear Information System (INIS)

Zhang Qiang; Pan Jianwei; Yin Juan; Chen Tengyun; Lu Shan; Zhang Jun; Li Xiaoqiang; Yang Tao; Wang Xiangbin

2006-01-01

We experimentally implement a fault-tolerant quantum key distribution protocol with two photons in a decoherence-free subspace [Phys. Rev. A 72, 050304(R) (2005)]. It is demonstrated that our protocol can yield a good key rate even with a large bit-flip error rate caused by collective rotation, while the usual realization of the Bennett-Brassard 1984 protocol cannot produce any secure final key given the same channel. Since the experiment is performed in polarization space and does not need the calibration of a reference frame, important applications in free-space quantum communication are expected. Moreover, our method can also be used to robustly transmit an arbitrary two-level quantum state in a type of decoherence-free subspace

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

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....

Enhancing Low-Rank Subspace Clustering by Manifold Regularization.

Science.gov (United States)

Liu, Junmin; Chen, Yijun; Zhang, JiangShe; Xu, Zongben

2014-07-25

Recently, low-rank representation (LRR) method has achieved great success in subspace clustering (SC), which aims to cluster the data points that lie in a union of low-dimensional subspace. Given a set of data points, LRR seeks the lowest rank representation among the many possible linear combinations of the bases in a given dictionary or in terms of the data itself. However, LRR only considers the global Euclidean structure, while the local manifold structure, which is often important for many real applications, is ignored. In this paper, to exploit the local manifold structure of the data, a manifold regularization characterized by a Laplacian graph has been incorporated into LRR, leading to our proposed Laplacian regularized LRR (LapLRR). An efficient optimization procedure, which is based on alternating direction method of multipliers (ADMM), is developed for LapLRR. Experimental results on synthetic and real data sets are presented to demonstrate that the performance of LRR has been enhanced by using the manifold regularization.

Body Image Distortion and Exposure to Extreme Body Types: Contingent Adaptation and Cross Adaptation for Self and Other.

Science.gov (United States)

Brooks, Kevin R; Mond, Jonathan M; Stevenson, Richard J; Stephen, Ian D

2016-01-01

Body size misperception is common amongst the general public and is a core component of eating disorders and related conditions. While perennial media exposure to the "thin ideal" has been blamed for this misperception, relatively little research has examined visual adaptation as a potential mechanism. We examined the extent to which the bodies of "self" and "other" are processed by common or separate mechanisms in young women. Using a contingent adaptation paradigm, experiment 1 gave participants prolonged exposure to images both of the self and of another female that had been distorted in opposite directions (e.g., expanded other/contracted self), and assessed the aftereffects using test images both of the self and other. The directions of the resulting perceptual biases were contingent on the test stimulus, establishing at least some separation between the mechanisms encoding these body types. Experiment 2 used a cross adaptation paradigm to further investigate the extent to which these mechanisms are independent. Participants were adapted either to expanded or to contracted images of their own body or that of another female. While adaptation effects were largest when adapting and testing with the same body type, confirming the separation of mechanisms reported in experiment 1, substantial misperceptions were also demonstrated for cross adaptation conditions, demonstrating a degree of overlap in the encoding of self and other. In addition, the evidence of misperception of one's own body following exposure to "thin" and to "fat" others demonstrates the viability of visual adaptation as a model of body image disturbance both for those who underestimate and those who overestimate their own size.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

Science.gov (United States)

Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kutz, J Nathan

2016-01-01

In this wIn this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.ork, we explore finite

Subspace-based analysis of the ERT inverse problem

Science.gov (United States)

Ben Hadj Miled, Mohamed Khames; Miller, Eric L.

2004-05-01

In a previous work, we proposed a source-type formulation to the electrical resistance tomography (ERT) problem. Specifically, we showed that inhomogeneities in the medium can be viewed as secondary sources embedded in the homogeneous background medium and located at positions associated with variation in electrical conductivity. Assuming a piecewise constant conductivity distribution, the support of equivalent sources is equal to the boundary of the inhomogeneity. The estimation of the anomaly shape takes the form of an inverse source-type problem. In this paper, we explore the use of subspace methods to localize the secondary equivalent sources associated with discontinuities in the conductivity distribution. Our first alternative is the multiple signal classification (MUSIC) algorithm which is commonly used in the localization of multiple sources. The idea is to project a finite collection of plausible pole (or dipole) sources onto an estimated signal subspace and select those with largest correlations. In ERT, secondary sources are excited simultaneously but in different ways, i.e. with distinct amplitude patterns, depending on the locations and amplitudes of primary sources. If the number of receivers is "large enough", different source configurations can lead to a set of observation vectors that span the data subspace. However, since sources that are spatially close to each other have highly correlated signatures, seperation of such signals becomes very difficult in the presence of noise. To overcome this problem we consider iterative MUSIC algorithms like R-MUSIC and RAP-MUSIC. These recursive algorithms pose a computational burden as they require multiple large combinatorial searches. Results obtained with these algorithms using simulated data of different conductivity patterns are presented.

Beyond Reactive Planning: Self Adaptive Software and Self Modeling Software in Predictive Deliberation Management

National Research Council Canada - National Science Library

Lenahan, Jack; Nash, Michael P; Charles, Phil

2008-01-01

.... We present the following hypothesis: predictive deliberation management using self-adapting and self-modeling software will be required to provide mission planning adjustments after the start of a mission...

Evaluating Clustering in Subspace Projections of High Dimensional Data

DEFF Research Database (Denmark)

Müller, Emmanuel; Günnemann, Stephan; Assent, Ira

2009-01-01

Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...... of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering...

Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality

Directory of Open Access Journals (Sweden)

Carlos A. L. Pires

2017-01-01

Full Text Available We propose an expansion of multivariate time-series data into maximally independent source subspaces. The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources. We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis. By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity. Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors. The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies. Sources result essentially from the coherency of extremes of the data components. The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies. The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans. Benefits and usefulness of independent subspaces are then discussed.

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.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

Science.gov (United States)

Brunton, Steven L.; Brunton, Bingni W.; Proctor, Joshua L.; Kutz, J. Nathan

2016-01-01

In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control. PMID:26919740

Random Number Simulations Reveal How Random Noise Affects the Measurements and Graphical Portrayals of Self-Assessed Competency

Directory of Open Access Journals (Sweden)

Edward Nuhfer

2016-01-01

Full Text Available Self-assessment measures of competency are blends of an authentic self-assessment signal that researchers seek to measure and random disorder or "noise" that accompanies that signal. In this study, we use random number simulations to explore how random noise affects critical aspects of self-assessment investigations: reliability, correlation, critical sample size, and the graphical representations of self-assessment data. We show that graphical conventions common in the self-assessment literature introduce artifacts that invite misinterpretation. Troublesome conventions include: (y minus x vs. (x scatterplots; (y minus x vs. (x column graphs aggregated as quantiles; line charts that display data aggregated as quantiles; and some histograms. Graphical conventions that generate minimal artifacts include scatterplots with a best-fit line that depict (y vs. (x measures (self-assessed competence vs. measured competence plotted by individual participant scores, and (y vs. (x scatterplots of collective average measures of all participants plotted item-by-item. This last graphic convention attenuates noise and improves the definition of the signal. To provide relevant comparisons across varied graphical conventions, we use a single dataset derived from paired measures of 1154 participants' self-assessed competence and demonstrated competence in science literacy. Our results show that different numerical approaches employed in investigating and describing self-assessment accuracy are not equally valid. By modeling this dataset with random numbers, we show how recognizing the varied expressions of randomness in self-assessment data can improve the validity of numeracy-based descriptions of self-assessment.

The Detection of Subsynchronous Oscillation in HVDC Based on the Stochastic Subspace Identification Method

Directory of Open Access Journals (Sweden)

Chen Shi

2014-01-01

Full Text Available Subsynchronous oscillation (SSO usually caused by series compensation, power system stabilizer (PSS, high voltage direct current transmission (HVDC and other power electronic equipment, which will affect the safe operation of generator shafting even the system. It is very important to identify the modal parameters of SSO to take effective control strategies as well. Since the identification accuracy of traditional methods are not high enough, the stochastic subspace identification (SSI method is proposed to improve the identification accuracy of subsynchronous oscillation modal. The stochastic subspace identification method was compared with the other two methods on subsynchronous oscillation IEEE benchmark model and Xiang-Shang HVDC system model, the simulation results show that the stochastic subspace identification method has the advantages of high identification precision, high operation efficiency and strong ability of anti-noise.

Body Image Distortion and Exposure to Extreme Body Types: Contingent Adaptation and Cross Adaptation for Self and Other

Directory of Open Access Journals (Sweden)

Kevin R. Brooks

2016-07-01

Full Text Available Body size misperception is common amongst the general public and is a core component of eating disorders and related conditions. While perennial media exposure to the thin ideal has been blamed for this misperception, relatively little research has examined visual adaptation as a potential mechanism. We examined the extent to which the bodies of self and other are processed by common or separate mechanisms in young women. Using a contingent adaptation paradigm, experiment 1 gave participants prolonged exposure to images both of the self and of another female that had been distorted in opposite directions (e.g. expanded other/contracted self, and assessed the aftereffects using test images both of the self and other. The directions of the resulting perceptual biases were contingent on the test stimulus, establishing at least some separation between the mechanisms encoding these body types. Experiment 2 used a cross adaptation paradigm to further investigate the extent to which these mechanisms are independent. Participants were adapted either to expanded or to contracted images of their own body or that of another female. While adaptation effects were largest when adapting and testing with the same body type, confirming the separation of mechanisms reported in experiment 1, substantial misperceptions were also demonstrated for cross adaptation conditions, demonstrating a degree of overlap in the encoding of self and other. In addition, the evidence of misperception of one’s own body following exposure to thin and to fat others demonstrates the viability of visual adaptation as a model of body image disturbance both for those who underestimate and those who overestimate their own size.

Adaptive disengagement buffers self-esteem from negative social feedback.

Science.gov (United States)

Leitner, Jordan B; Hehman, Eric; Deegan, Matthew P; Jones, James M

2014-11-01

The degree to which self-esteem hinges on feedback in a domain is known as a contingency of self-worth, or engagement. Although previous research has conceptualized engagement as stable, it would be advantageous for individuals to dynamically regulate engagement. The current research examined whether the tendency to disengage from negative feedback accounts for variability in self-esteem. We created the Adaptive Disengagement Scale (ADS) to capture individual differences in the tendency to disengage self-esteem from negative outcomes. Results demonstrated that the ADS is reliable and valid (Studies 1 and 2). Furthermore, in response to negative social feedback, higher scores on the ADS predicted greater state self-esteem (Study 3), and this relationship was mediated by disengagement (Study 4). These findings demonstrate that adaptive disengagement protects self-esteem from negative outcomes and that the ADS is a valid measure of individual differences in the implementation of this process. © 2014 by the Society for Personality and Social Psychology, Inc.

Active Subspace Methods for Data-Intensive Inverse Problems

Energy Technology Data Exchange (ETDEWEB)

Wang, Qiqi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

2017-04-27

The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.

Comparison Study of Subspace Identification Methods Applied to Flexible Structures

Science.gov (United States)

Abdelghani, M.; Verhaegen, M.; Van Overschee, P.; De Moor, B.

1998-09-01

In the past few years, various time domain methods for identifying dynamic models of mechanical structures from modal experimental data have appeared. Much attention has been given recently to so-called subspace methods for identifying state space models. This paper presents a detailed comparison study of these subspace identification methods: the eigensystem realisation algorithm with observer/Kalman filter Markov parameters computed from input/output data (ERA/OM), the robust version of the numerical algorithm for subspace system identification (N4SID), and a refined version of the past outputs scheme of the multiple-output error state space (MOESP) family of algorithms. The comparison is performed by simulating experimental data using the five mode reduced model of the NASA Mini-Mast structure. The general conclusion is that for the case of white noise excitations as well as coloured noise excitations, the N4SID/MOESP algorithms perform equally well but give better results (improved transfer function estimates, improved estimates of the output) compared to the ERA/OM algorithm. The key computational step in the three algorithms is the approximation of the extended observability matrix of the system to be identified, for N4SID/MOESP, or of the observer for the system to be identified, for the ERA/OM. Furthermore, the three algorithms only require the specification of one dimensioning parameter.

Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

Directory of Open Access Journals (Sweden)

Ronald Phlypo

2007-01-01

Full Text Available To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power. It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS. Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.

Graduate employability capacities, self-esteem and career adaptability among South African young adults

Directory of Open Access Journals (Sweden)

Sadika Ismail

2017-08-01

Full Text Available Orientation: Employers expect young graduates to have a well-rounded sense of self, to display a range of graduate employability capacities and to adapt to constant changes they are faced with in order to obtain and maintain employment. Research purpose: The goals of this study are (1 to investigate whether a significant relationship exists between graduate employability capacities, self-esteem and career adaptability, (2 to ascertain if a set of graduate employability capacities, when combined with self-esteem, has a significant relationship with a set of career adaptability capacities and (3 to identify the major variables that contribute to this relationship. Motivation for the study: The potential for career adaptability, graduate employability capacities and self-esteem of young adults promotes employability among graduates, thereby addressing and possibly reducing youth unemployment in South Africa. Research approach, design and method: A quantitative, cross-sectional research design approach was utilised in which descriptive statistics, Pearson product-moment correlations and canonical correlation analysis were employed to accomplish the objectives of this study. Respondents (N = 332 were enrolled at further education and training (FET colleges and were predominantly black (98.5% and female (62% students between the ages of 18 and 29. Main findings: The results displayed positive multivariate relationships between the variables and furthermore showed that graduate employability capacities contributed the most in terms of clarifying the respondents’ career adaptability as compared to their self-esteem. Practical and managerial implications: This study proposes that young adults’ career adaptability can be enhanced through the development of their self-esteem and particularly their graduate employability capacities, thus making them more employable. Contributions: Theoretically, this study proves useful because of the significant

Estimation of direction of arrival of a moving target using subspace based approaches

Science.gov (United States)

Ghosh, Ripul; Das, Utpal; Akula, Aparna; Kumar, Satish; Sardana, H. K.

2016-05-01

In this work, array processing techniques based on subspace decomposition of signal have been evaluated for estimation of direction of arrival of moving targets using acoustic signatures. Three subspace based approaches - Incoherent Wideband Multiple Signal Classification (IWM), Least Square-Estimation of Signal Parameters via Rotation Invariance Techniques (LS-ESPRIT) and Total Least Square- ESPIRIT (TLS-ESPRIT) are considered. Their performance is compared with conventional time delay estimation (TDE) approaches such as Generalized Cross Correlation (GCC) and Average Square Difference Function (ASDF). Performance evaluation has been conducted on experimentally generated data consisting of acoustic signatures of four different types of civilian vehicles moving in defined geometrical trajectories. Mean absolute error and standard deviation of the DOA estimates w.r.t. ground truth are used as performance evaluation metrics. Lower statistical values of mean error confirm the superiority of subspace based approaches over TDE based techniques. Amongst the compared methods, LS-ESPRIT indicated better performance.

Experimental Comparison of Signal Subspace Based Noise Reduction Methods

DEFF Research Database (Denmark)

Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus

1999-01-01

The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators...

Large Neighborhood Search and Adaptive Randomized Decompositions for Flexible Jobshop Scheduling

DEFF Research Database (Denmark)

Pacino, Dario; Van Hentenryck, Pascal

2011-01-01

This paper considers a constraint-based scheduling approach to the flexible jobshop, a generalization of the traditional jobshop scheduling where activities have a choice of machines. It studies both large neighborhood (LNS) and adaptive randomized de- composition (ARD) schemes, using random...

Quantum theory of dynamical collective subspace for large-amplitude collective motion

International Nuclear Information System (INIS)

Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.

1986-03-01

By placing emphasis on conceptual correspondence to the ''classical'' theory which has been developed within the framework of the time-dependent Hartree-Fock theory, a full quantum theory appropriate for describing large-amplitude collective motion is proposed. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation; the representation is specific for the collective subspace where the large-amplitude collective motion is replicated as satisfactorily as possible. As an extension of the classical theory where the concept of an approximate integral surface plays an important role, the dynamical representation is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)

Fast image interpolation via random forests.

Science.gov (United States)

Huang, Jun-Jie; Siu, Wan-Chi; Liu, Tian-Rui

2015-10-01

This paper proposes a two-stage framework for fast image interpolation via random forests (FIRF). The proposed FIRF method gives high accuracy, as well as requires low computation. The underlying idea of this proposed work is to apply random forests to classify the natural image patch space into numerous subspaces and learn a linear regression model for each subspace to map the low-resolution image patch to high-resolution image patch. The FIRF framework consists of two stages. Stage 1 of the framework removes most of the ringing and aliasing artifacts in the initial bicubic interpolated image, while Stage 2 further refines the Stage 1 interpolated image. By varying the number of decision trees in the random forests and the number of stages applied, the proposed FIRF method can realize computationally scalable image interpolation. Extensive experimental results show that the proposed FIRF(3, 2) method achieves more than 0.3 dB improvement in peak signal-to-noise ratio over the state-of-the-art nonlocal autoregressive modeling (NARM) method. Moreover, the proposed FIRF(1, 1) obtains similar or better results as NARM while only takes its 0.3% computational time.

A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

Science.gov (United States)

Jalaleddini, Kian; Tehrani, Ehsan Sobhani; Kearney, Robert E

2017-06-01

The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.

The impact of culture on adaptive versus maladaptive self-reflection.

Science.gov (United States)

Grossmann, Igor; Kross, Ethan

2010-08-01

Although recent findings indicate that people can reflect either adaptively or maladaptively over negative experiences, extant research has not examined how culture influences this process. We compared the self-reflective practices of Russians (members of an interdependent culture characterized by a tendency to brood) and Americans (members of an independent culture in which self-reflection has been studied extensively). We predicted that self-reflection would be associated with less-detrimental outcomes among Russians because they self-distance more when analyzing their feelings than Americans do. Findings from two studies supported these predictions. In Study 1, self-reflection was associated with fewer depressive symptoms among Russians than among Americans. In Study 2, Russians displayed less distress and a more adaptive pattern of construals than Americans after reflecting over a recent negative event. In addition, they self-distanced more than Americans while analyzing their feelings, and self-distancing mediated the cultural differences in self-reflection. These findings demonstrate how culture shapes the way people reflect over negative experiences.

Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization

KAUST Repository

Fornasier, Massimo; Schö nlieb, Carola-Bibiane

2009-01-01

This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a

LogDet Rank Minimization with Application to Subspace Clustering

Directory of Open Access Journals (Sweden)

Zhao Kang

2015-01-01

Full Text Available Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

DEFF Research Database (Denmark)

Hansen, Per Christian; Jensen, Søren Holdt

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Science.gov (United States)

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Directory of Open Access Journals (Sweden)

Guihua Wen

2017-01-01

Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

A Comparative Study for Orthogonal Subspace Projection and Constrained Energy Minimization

National Research Council Canada - National Science Library

Du, Qian; Ren, Hsuan; Chang, Chein-I

2003-01-01

...: orthogonal subspace projection (OSP) and constrained energy minimization (CEM). It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR...

Using CUDA Technology for Defining the Stiffness Matrix in the Subspace of Eigenvectors

Directory of Open Access Journals (Sweden)

Yu. V. Berchun

2015-01-01

Full Text Available The aim is to improve the performance of solving a problem of deformable solid mechanics through the use of GPGPU. The paper describes technologies for computing systems using both a central and a graphics processor and provides motivation for using CUDA technology as the efficient one.The paper also analyses methods to solve the problem of defining natural frequencies and design waveforms, i.e. an iteration method in the subspace. The method includes several stages. The paper considers the most resource-hungry stage, which defines the stiffness matrix in the subspace of eigenforms and gives the mathematical interpretation of this stage.The GPU choice as a computing device is justified. The paper presents an algorithm for calculating the stiffness matrix in the subspace of eigenforms taking into consideration the features of input data. The global stiffness matrix is very sparse, and its size can reach tens of millions. Therefore, it is represented as a set of the stiffness matrices of the single elements of a model. The paper analyses methods of data representation in the software and selects the best practices for GPU computing.It describes the software implementation using CUDA technology to calculate the stiffness matrix in the subspace of eigenforms. Due to the input data nature, it is impossible to use the universal libraries of matrix computations (cuSPARSE and cuBLAS for loading the GPU. For efficient use of GPU resources in the software implementation, the stiffness matrices of elements are built in the block matrices of a special form. The advantages of using shared memory in GPU calculations are described.The transfer to the GPU computations allowed a twentyfold increase in performance (as compared to the multithreaded CPU-implementation on the model of middle dimensions (degrees of freedom about 2 million. Such an acceleration of one stage speeds up defining the natural frequencies and waveforms by the iteration method in a subspace

N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.

Science.gov (United States)

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.

Biclustering of gene expression data using reactive greedy randomized adaptive search procedure.

Science.gov (United States)

Dharan, Smitha; Nair, Achuthsankar S

2009-01-30

Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously. We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach. The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

Science.gov (United States)

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

Fast regularizing sequential subspace optimization in Banach spaces

International Nuclear Information System (INIS)

Schöpfer, F; Schuster, T

2009-01-01

We are concerned with fast computations of regularized solutions of linear operator equations in Banach spaces in case only noisy data are available. To this end we modify recently developed sequential subspace optimization methods in such a way that the therein employed Bregman projections onto hyperplanes are replaced by Bregman projections onto stripes whose width is in the order of the noise level

Lie n-derivations on 7 -subspace lattice algebras

Indian Academy of Sciences (India)

all x ∈ K and all A ∈ Alg L. Based on this result, a complete characterization of linear n-Lie derivations on Alg L is obtained. Keywords. J -subspace lattice algebras; Lie derivations; Lie n-derivations; derivations. 2010 Mathematics Subject Classification. 47B47, 47L35. 1. Introduction. Let A be an algebra. Recall that a linear ...

Towards Self-adaptation for Dependable Service-Oriented Systems

Science.gov (United States)

Cardellini, Valeria; Casalicchio, Emiliano; Grassi, Vincenzo; Lo Presti, Francesco; Mirandola, Raffaela

Increasingly complex information systems operating in dynamic environments ask for management policies able to deal intelligently and autonomously with problems and tasks. An attempt to deal with these aspects can be found in the Service-Oriented Architecture (SOA) paradigm that foresees the creation of business applications from independently developed services, where services and applications build up complex dependencies. Therefore the dependability of SOA systems strongly depends on their ability to self-manage and adapt themselves to cope with changes in the operating conditions and to meet the required dependability with a minimum of resources. In this paper we propose a model-based approach to the realization of self-adaptable SOA systems, aimed at the fulfillment of dependability requirements. Specifically, we provide a methodology driving the system adaptation and we discuss the architectural issues related to its implementation. To bring this approach to fruition, we developed a prototype tool and we show the results that can be achieved with a simple example.

Adaptive control for a PWR using a self-tuning reference model concept

International Nuclear Information System (INIS)

Miley, G.H.; Park, G.T.; Kim, B.S.

1992-01-01

Possible applications of an adaptive control method to a pressurized-water reactor nuclear power plant are investigated. The self-tuning technique with a reference model concept is employed. This control algorithm is developed by combining the self-tuning controller with the model reference adaptive control. This approach overcomes the difficulties in choosing the appropriate weighting polynomials in the cost function of the self-tuning control

Beamspace Adaptive Beamforming for Hydrodynamic Towed Array Self-Noise Cancellation

National Research Council Canada - National Science Library

Premus, Vincent

2001-01-01

... against signal self-nulling associated with steering vector mismatch. Particular attention is paid to the definition of white noise gain as the metric that reflects the level of mainlobe adaptive nulling for an adaptive beamformer...

Beamspace Adaptive Beamforming for Hydrodynamic Towed Array Self-Noise Cancellation

National Research Council Canada - National Science Library

Premus, Vincent

2000-01-01

... against signal self-nulling associated with steering vector mismatch. Particular attention is paid to the definition of white noise gain as the metric that reflects the level of mainlobe adaptive nulling for an adaptive beamformer...

Self-adapted thermocouple-diagnostic complex

International Nuclear Information System (INIS)

Alekseev, S.V.; Grankovskij, K.Eh.; Olejnikov, P.P.; Prijmak, S.V.; Shikalov, V.F.

2003-01-01

A self-adapted thermocouple-diagnostic complex (STDC) for obtaining the reliable data on the coolant temperature in the reactors of NPP is described. The STDC in based on the thermal pulse monitoring of a thermocouple in the measuring channel of a reactor. Measurement method and STDC composition are substantiated. It is shown that introduction of the developed STDC ensures realization of precise and reliable temperature monitoring in the reactors of all types [ru

An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

International Nuclear Information System (INIS)

Zhang, Liangwei; Lin, Jing; Karim, Ramin

2015-01-01

The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces. - Highlights: • An anomaly detection approach for high-dimensional reliability data is proposed. • The approach selects relevant subspaces by assessing vectorial angles. • The novel ABSAD approach displays superior accuracy over other alternatives. • Numerical illustration approves its efficacy in fault detection applications

Recursive subspace identification for in flight modal analysis of airplanes

OpenAIRE

De Cock , Katrien; Mercère , Guillaume; De Moor , Bart

2006-01-01

International audience; In this paper recursive subspace identification algorithms are applied to track the modal parameters of airplanes on-line during test flights. The ability to track changes in the damping ratios and the influence of the forgetting factor are studied through simulations.

Correlation between self-differentiation and professional adaptability among undergraduate nursing students in China

Directory of Open Access Journals (Sweden)

Si-wei Liu

2016-12-01

Conclusion: The level of self-differentiation of undergraduate nursing studentsaffects their professional adaptability. Nursing educators should consider the characteristics of self-differentiation of undergraduate nursing students in developing measures to improve their professional adaptability.

Does self-employment really raise job satisfaction? Adaptation and anticipation effects on self-employment and general job changes

OpenAIRE

Hanglberger, Dominik; Merz, Joachim

2015-01-01

Empirical analyses using cross-sectional and panel data found significantly higher levels of job satisfaction for the self-employed than for employees. We argue that by neglecting anticipation and adaptation effects estimates in previous studies might be misleading. To test this, we specify models accounting for anticipation and adaptation to self-employment and general job changes. In contrast to recent literature we find no specific long-term effect of self-employment on job satisfaction. A...

A Novel Self-Adaptive Harmony Search Algorithm

Directory of Open Access Journals (Sweden)

Kaiping Luo

2013-01-01

Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self-adaptive variants for large-scale optimization problems and constrained problems.

Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption

Directory of Open Access Journals (Sweden)

Zheping Yan

2014-01-01

Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

DEFF Research Database (Denmark)

Chakraborty, Rudrasis; Hauberg, Søren; Vemuri, Baba C.

2017-01-01

Principal Component Analysis (PCA) is a fundamental method for estimating a linear subspace approximation to high-dimensional data. Many algorithms exist in literature to achieve a statistically robust version of PCA called RPCA. In this paper, we present a geometric framework for computing the p...

Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

Directory of Open Access Journals (Sweden)

Xu Wang

2013-01-01

Full Text Available A differential evolution (DE algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

Relationship between adaptation and self-esteem in addicted female prisoners in the south east of Iran.

Science.gov (United States)

Torkaman, Mahya; Miri, Sakineh; Farokhzadian, Jamileh

2018-02-12

Background Reduction of the adaptation and self-esteem can be the consequence of opium addiction and imprisonment. Drug use causes inappropriate behaviors in women, which are quite different from those in men. Social deviations, prostitution, high-risk sexual behaviors, abortion, divorce and imprisonment followed by loss of self-esteem are the consequences of women's addiction. The present study was conducted to assess the relationship between adaptation and self-esteem in addicted female prisoners. Methods In this descriptive analytical study, 130 addicted female prisoners were selected from a prison in the south east of Iran using census sampling. The data were collected by a demographic questionnaire, the Rosenberg's self-esteem scale and the bell adjustment inventory (BAI). Results According to the results, women's adaptation fell into the 'very unsatisfactory' range. The highest mean was related to the emotional dimension, while the lowest mean was in terms of the health dimension. In total, 96.4% of the participating women had low adaptation. The mean total self-esteem fell into the low range; in fact, 84.6% of the women had a low self-esteem. The results showed no significant relationships between adaptation and self-esteem in these women; however, self-esteem was significantly and inversely related to health and emotional adaptation. Conclusion The findings showed that the majority of the women had unsatisfactory adaptation as well as poor self-esteem. No significant relationships were observed between adaptation and self-esteem in the addicted female prisoners.

Random ensemble learning for EEG classification.

Science.gov (United States)

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

2018-01-01

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

Quantum cloning of mixed states in symmetric subspaces

International Nuclear Information System (INIS)

Fan Heng

2003-01-01

Quantum-cloning machine for arbitrary mixed states in symmetric subspaces is proposed. This quantum-cloning machine can be used to copy part of the output state of another quantum-cloning machine and is useful in quantum computation and quantum information. The shrinking factor of this quantum cloning achieves the well-known upper bound. When the input is identical pure states, two different fidelities of this cloning machine are optimal

The role of disability self-concept in adaptation to congenital or acquired disability.

Science.gov (United States)

Bogart, Kathleen R

2014-02-01

Current theories of adaptation to disability do not address differences in adaptation to congenital or acquired disability. Although people with congenital disabilities are generally assumed to be better adapted than people with acquired disabilities, few studies have tested this, and even fewer have attempted to explain the mechanisms behind these differences. This study tested the proposition that whether a disability is congenital or acquired plays an important role in the development of the disability self-concept (consisting of disability identity and disability self-efficacy), which in turn, affects satisfaction with life. It was predicted that disability self-concept would be better developed among people with congenital, compared with acquired disabilities, predicting greater satisfaction with life in those with acquired conditions. 226 participants with congenital and acquired mobility disabilities completed a cross-sectional online questionnaire measuring satisfaction with life, self-esteem, disability identity, disability self-efficacy, and demographic information. Self-esteem, disability identity, disability self-efficacy, and income were significant predictors of satisfaction with life. Congenital onset predicted higher satisfaction with life; disability identity and disability self-efficacy, but not self-esteem, partially mediated the relationship. Findings highlight the distinction between adaptation to congenital versus acquired disability and the importance of disability self-concept, which are underresearched constructs. Results suggest that rather than attempting to "normalize" individuals with disabilities, health care professionals should foster their disability self-concept. Possible ways to improve disability self-concept are discussed, such as involvement in the disability community and disability pride. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Bayesian analysis for exponential random graph models using the adaptive exchange sampler

KAUST Repository

Jin, Ick Hoon

2013-01-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the existence of intractable normalizing constants. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the issue of intractable normalizing constants encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

The impact of leadership programme on self-esteem and self-efficacy in school: a randomized controlled trial.

Directory of Open Access Journals (Sweden)

Martin C S Wong

Full Text Available BACKGROUND: Leadership training programs by experiential learning among adolescents are very popular worldwide and in particular developed countries, but there exists few studies which formally assessed their impact on the psychological well-being of program participants. This study evaluated the effectiveness of leadership training programs on self-esteem and self-efficacy among adolescents. METHODOLOGY/PRINCIPAL FINDINGS: a total of 180 students of the same grade of one secondary school were randomized into an intervention (n = 50 and a control group (n = 130. The students in the intervention group participated in a 6-month program of leadership training and service learning, while the control group did not participate in any training. Their self-esteem and self-efficacy were assessed by Rosenberg Self-Esteem questionnaire and Chinese Adaptation of the General Self-Efficacy Scale, respectively, before and after the program. Both scales have been recognized internationally as valid and reliable survey instruments to measure these psychological attributes. The scores were compared by Student's tests according to gender. A total of 180 students were enrolled during the study period October, 2009 to May, 2010. Their mean age was 15.18 years (0.62 and 56.7% were male. Students allocated to the intervention and control group had statistically similar demographic characteristics except gender (male 36.0% vs. 64.6%, p = 0.001. Overall, the self-esteem scores increased by 1.28 and decreased by 0.30 (p = 0.161 while the self-efficacy scores increased by 0.26 and decreased by 0.76 (p = 0.429 in the intervention and control group, respectively. Among female students, the intervention group showed significant improvements in both self-esteem (2.38 vs. -0.24, p<0.001 and self-efficacy (1.32 vs. -0.04, p = 0.043. CONCLUSIONS/SIGNIFICANCE: Leadership training program were not found to be effective to enhance self-esteem and self

Von Neumann algebras as complemented subspaces of B(H)

DEFF Research Database (Denmark)

Christensen, Erik; Wang, Liguang

2014-01-01

Let M be a von Neumann algebra of type II1 which is also a complemented subspace of B( H). We establish an algebraic criterion, which ensures that M is an injective von Neumann algebra. As a corollary we show that if M is a complemented factor of type II1 on a Hilbert space H, then M is injective...

Embeddings of model subspaces of the Hardy space: compactness and Schatten-von Neumann ideals

International Nuclear Information System (INIS)

Baranov, Anton D

2009-01-01

We study properties of the embedding operators of model subspaces K p Θ (defined by inner functions) in the Hardy space H p (coinvariant subspaces of the shift operator). We find a criterion for the embedding of K p Θ in L p (μ) to be compact similar to the Volberg-Treil theorem on bounded embeddings, and give a positive answer to a question of Cima and Matheson. The proof is based on Bernstein-type inequalities for functions in K p Θ . We investigate measures μ such that the embedding operator belongs to some Schatten-von Neumann ideal.

The impact of leadership programme on self-esteem and self-efficacy in school: a randomized controlled trial.

Science.gov (United States)

Wong, Martin C S; Lau, Tony C M; Lee, Albert

2012-01-01

Leadership training programs by experiential learning among adolescents are very popular worldwide and in particular developed countries, but there exists few studies which formally assessed their impact on the psychological well-being of program participants. This study evaluated the effectiveness of leadership training programs on self-esteem and self-efficacy among adolescents. a total of 180 students of the same grade of one secondary school were randomized into an intervention (n = 50) and a control group (n = 130). The students in the intervention group participated in a 6-month program of leadership training and service learning, while the control group did not participate in any training. Their self-esteem and self-efficacy were assessed by Rosenberg Self-Esteem questionnaire and Chinese Adaptation of the General Self-Efficacy Scale, respectively, before and after the program. Both scales have been recognized internationally as valid and reliable survey instruments to measure these psychological attributes. The scores were compared by Student's tests according to gender. A total of 180 students were enrolled during the study period October, 2009 to May, 2010. Their mean age was 15.18 years (0.62) and 56.7% were male. Students allocated to the intervention and control group had statistically similar demographic characteristics except gender (male 36.0% vs. 64.6%, p = 0.001). Overall, the self-esteem scores increased by 1.28 and decreased by 0.30 (p = 0.161) while the self-efficacy scores increased by 0.26 and decreased by 0.76 (p = 0.429) in the intervention and control group, respectively. Among female students, the intervention group showed significant improvements in both self-esteem (2.38 vs. -0.24, pself-efficacy (1.32 vs. -0.04, p = 0.043). Leadership training program were not found to be effective to enhance self-esteem and self-efficacy in adolescents, except girls who showed modest increase in these outcomes. Future research

Self-Esteem and Social Adaptation Development in Children

Directory of Open Access Journals (Sweden)

Alicia Lamia

2011-12-01

Full Text Available Self-esteem is the self-evaluation each individual makes from the representations it has of itself and from the representations constructed by the others. The sense of personal worth appears in a process of identity construction. This is associated with the assessment that people make about the social adaptation of the child. The present study concerns the development of self-image and self-esteem of children in school age. The sample consisted of 180 children. The results demonstrated a difference in the responses of children in relation to age and gender. The boys were evaluated more positively than girls. There has been the same results in younger children compared to the older ones.

Application of Earthquake Subspace Detectors at Kilauea and Mauna Loa Volcanoes, Hawai`i

Science.gov (United States)

Okubo, P.; Benz, H.; Yeck, W.

2016-12-01

Recent studies have demonstrated the capabilities of earthquake subspace detectors for detailed cataloging and tracking of seismicity in a number of regions and settings. We are exploring the application of subspace detectors at the United States Geological Survey's Hawaiian Volcano Observatory (HVO) to analyze seismicity at Kilauea and Mauna Loa volcanoes. Elevated levels of microseismicity and occasional swarms of earthquakes associated with active volcanism here present cataloging challenges due the sheer numbers of earthquakes and an intrinsically low signal-to-noise environment featuring oceanic microseism and volcanic tremor in the ambient seismic background. With high-quality continuous recording of seismic data at HVO, we apply subspace detectors (Harris and Dodge, 2011, Bull. Seismol. Soc. Am., doi: 10.1785/0120100103) during intervals of noteworthy seismicity. Waveform templates are drawn from Magnitude 2 and larger earthquakes within clusters of earthquakes cataloged in the HVO seismic database. At Kilauea, we focus on seismic swarms in the summit caldera region where, despite continuing eruptions from vents in the summit region and in the east rift zone, geodetic measurements reflect a relatively inflated volcanic state. We also focus on seismicity beneath and adjacent to Mauna Loa's summit caldera that appears to be associated with geodetic expressions of gradual volcanic inflation, and where precursory seismicity clustered prior to both Mauna Loa's most recent eruptions in 1975 and 1984. We recover several times more earthquakes with the subspace detectors - down to roughly 2 magnitude units below the templates, based on relative amplitudes - compared to the numbers of cataloged earthquakes. The increased numbers of detected earthquakes in these clusters, and the ability to associate and locate them, allow us to infer details of the spatial and temporal distributions and possible variations in stresses within these key regions of the volcanoes.

Discrete-State Stochastic Models of Calcium-Regulated Calcium Influx and Subspace Dynamics Are Not Well-Approximated by ODEs That Neglect Concentration Fluctuations

Science.gov (United States)

Weinberg, Seth H.; Smith, Gregory D.

2012-01-01

Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs) and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters) that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume) with the corresponding deterministic model (an approximation that assumes large system size). When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers. PMID:23509597

mHealth intervention to support asthma self-management in adolescents: the ADAPT study

Directory of Open Access Journals (Sweden)

Kosse RC

2017-03-01

Full Text Available Richelle C Kosse,1 Marcel L Bouvy,1 Tjalling W de Vries,2 Ad A Kaptein,3 Harm CJ Geers,1 Liset van Dijk,4 Ellen S Koster1 1Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, 2Department of Paediatrics, Medical Center Leeuwarden, Leeuwarden, 3Medical Psychology, Leiden University Medical Center, Leiden, 4NIVEL, the Netherlands Institute for Health Services Research, Utrecht, the Netherlands Purpose: Poor medication adherence in adolescents with asthma results in poorly controlled disease and increased morbidity. The aim of the ADolescent Adherence Patient Tool (ADAPT study is to develop an mHealth intervention to support self-management and to evaluate the effectiveness in improving medication adherence and asthma control. Intervention: The ADAPT intervention consists of an interactive smartphone application (app connected to a desktop application for health care providers, in this study, the community pharmacist. The app contains several functions to improve adherence as follows: 1 a questionnaire function to rate asthma symptoms and monitor these over time; 2 short movie clips with medication and disease information; 3 a medication reminder; 4 a chat function with peers; and 5 a chat function with the pharmacist. The pharmacist receives data from the patient’s app through the desktop application, which enables the pharmacist to send information and feedback to the patient. Study design: The ADAPT intervention is tested in a community pharmacy-based cluster randomized controlled trial in the Netherlands, aiming to include 352 adolescents with asthma. The main outcome is adherence, measured by patient’s self-report and refill adherence calculated from pharmacy dispensing records. In addition, asthma control, illness perceptions, medication beliefs, and asthma-related quality of life are measured. Conclusion: This study will provide in

Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System

Directory of Open Access Journals (Sweden)

Minghong She

2018-01-01

Full Text Available For the identification problem of closed-loop subspace model, we propose a zero space projection method based on the estimation of correlation function to fill the block Hankel matrix of identification model by combining the linear algebra with geometry. By using the same projection of related data in time offset set and LQ decomposition, the multiplication operation of projection is achieved and dynamics estimation of the unknown equipment system model is obtained. Consequently, we have solved the problem of biased estimation caused when the open-loop subspace identification algorithm is applied to the closed-loop identification. A simulation example is given to show the effectiveness of the proposed approach. In final, the practicability of the identification algorithm is verified by hardware test of UAV servo system in real environment.

Nanostructural self-organization and dynamic adaptation of metal-polymer tribosystems

Science.gov (United States)

Mashkov, Yu. K.

2017-02-01

The results of investigating the effect of nanosize modifiers of a polymer matrix on the nanostructural self-organization of polymer composites and dynamic adaptation of metal-polymer tribosystems, which considerably affect the wear resistance of polymer composite materials, have been analyzed. It has been shown that the physicochemical nanostructural self-organization processes are developed in metal-polymer tribosystems with the formation of thermotropic liquid-crystal structures of the polymer matrix, followed by the transition of the system to the stationary state with a negative feedback that ensures dynamic adaptation of the tribosystem to given operating conditions.

Index Formulae for Subspaces of Kreĭn Spaces

NARCIS (Netherlands)

Dijksma, Aad; Gheondea, Aurelian

1996-01-01

For a subspace S of a Kreĭn space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula κ-(S) + dim(S⊥ ∩ K+) = κ+(S⊥) + dim(S ∩ K-), where κ±(S) stands for the positive/negative signature of S. The difference dim(S ∩ K-) - dim(S⊥ ∩ K+), provided it is well

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

Science.gov (United States)

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

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

Numerical solution of stiff burnup equation with short half lived nuclides by the Krylov subspace method

International Nuclear Information System (INIS)

Yamamoto, Akio; Tatsumi, Masahiro; Sugimura, Naoki

2007-01-01

The Krylov subspace method is applied to solve nuclide burnup equations used for lattice physics calculations. The Krylov method is an efficient approach for solving ordinary differential equations with stiff nature such as the nuclide burnup with short lived nuclides. Some mathematical fundamentals of the Krylov subspace method and its application to burnup equations are discussed. Verification calculations are carried out in a PWR pin-cell geometry with UO 2 fuel. A detailed burnup chain that includes 193 fission products and 28 heavy nuclides is used in the verification calculations. Shortest half life found in the present burnup chain is approximately 30 s ( 106 Rh). Therefore, conventional methods (e.g., the Taylor series expansion with scaling and squaring) tend to require longer computation time due to numerical stiffness. Comparison with other numerical methods (e.g., the 4-th order Runge-Kutta-Gill) reveals that the Krylov subspace method can provide accurate solution for a detailed burnup chain used in the present study with short computation time. (author)

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

Science.gov (United States)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

Science.gov (United States)

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Visual tracking based on the sparse representation of the PCA subspace

Science.gov (United States)

Chen, Dian-bing; Zhu, Ming; Wang, Hui-li

2017-09-01

We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.

Cumulant-Based Coherent Signal Subspace Method for Bearing and Range Estimation

Directory of Open Access Journals (Sweden)

Bourennane Salah

2007-01-01

Full Text Available A new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown Gaussian noise is proposed. This method uses the MUSIC algorithm with noise subspace estimated by using the slice fourth-order cumulant matrix of the received data. The higher-order statistics aim at the removal of the additive unknown Gaussian noise. The bilinear focusing operator is used to decorrelate the received signals and to estimate the coherent signal subspace. A new source steering vector is proposed including the acoustic scattering model at each sensor. Range and bearing of the objects at each sensor are expressed as a function of those at the first sensor. This leads to the improvement of object localization anywhere, in the near-field or in the far-field zone of the sensor array. Finally, the performances of the proposed method are validated on data recorded during experiments in a water tank.

On the Kalman Filter error covariance collapse into the unstable subspace

Directory of Open Access Journals (Sweden)

A. Trevisan

2011-03-01

Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N⁺ + N⁰ where N⁺ and N⁰ are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.

RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes.

Science.gov (United States)

Ryeznik, Yevgen; Sverdlov, Oleksandr; Wong, Weng Kee

2015-08-01

Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool , a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

Directory of Open Access Journals (Sweden)

Bo Liu

2012-02-01

Full Text Available In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL, for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI. Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD. Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces.

An adaptive random search for short term generation scheduling with network constraints.

Directory of Open Access Journals (Sweden)

J A Marmolejo

Full Text Available This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.

Adapting to an initial self-regulatory task cancels the ego depletion effect.

Science.gov (United States)

Dang, Junhua; Dewitte, Siegfried; Mao, Lihua; Xiao, Shanshan; Shi, Yucai

2013-09-01

The resource-based model of self-regulation provides a pessimistic view of self-regulation that people are destined to lose their self-control after having engaged in any act of self-regulation because these acts deplete the limited resource that people need for successful self-regulation. The cognitive control theory, however, offers an alternative explanation and suggests that the depletion effect reflects switch costs between different cognitive control processes recruited to deal with demanding tasks. This account implies that the depletion effect will not occur once people have had the opportunity to adapt to the self-regulatory task initially engaged in. Consistent with this idea, the present study showed that engaging in a demanding task led to performance deficits on a subsequent self-regulatory task (i.e. the depletion effect) only when the initial demanding task was relatively short but not when it was long enough for participants to adapt. Our results were unrelated to self-efficacy, mood, and motivation. Copyright © 2013 Elsevier Inc. All rights reserved.

Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery

Science.gov (United States)

2016-09-27

Bian, Student Member, IEEE, and Hamid Krim, Fellow, IEEE Abstract The success of sparse models in computer vision and machine learning is due to the...16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world

Cancer-specific self-efficacy and psychosocial and functional adaptation to early stage breast cancer.

Science.gov (United States)

Manne, Sharon L; Ostroff, Jamie S; Norton, Tina R; Fox, Kevin; Grana, Generosa; Goldstein, Lori

2006-04-01

Although self-efficacy is considered a key psychological resource in adapting to chronic physical illness, this construct has received less attention among individuals coping with cancer. To examine changes in cancer self-efficacy over time among women with early stage breast cancer and associations between task-specific domains of self-efficacy and specific psychological, relationship, and functional outcomes. Ninety-five women diagnosed with early stage breast cancer completed surveys postsurgery and 1 year later. Cancer-related self-efficacy was relatively stable over 1 year, with only 2 domains of efficacy-(a) Activity Management and (b) Self-Satisfaction-evidencing significant increases over the 1-year time period. Cross-sectional findings were relatively consistent with predictions and suggested that specific domains of self-efficacy were more strongly related to relevant domains of adaptation. Longitudinal findings were not as consistent with the domain-specificity hypothesis but did suggest several predictive associations between self-efficacy and outcomes. Personal Management self-efficacy was associated with higher relationship satisfaction, higher Communication Self-Efficacy was associated with less functional impairment, and higher Affective Management self-efficacy was associated with higher self-esteem 1 year later. Specific domains of cancer-related self-efficacy are most closely related to relevant areas of adaptation when considered cross-sectionally, but further study is needed to clarify the nature of these relationships over time.

Promoting a Positive Middle School Transition: A Randomized-Controlled Treatment Study Examining Self-Concept and Self-Esteem.

Science.gov (United States)

Coelho, Vitor Alexandre; Marchante, Marta; Jimerson, Shane R

2017-03-01

The middle school transition is a salient developmental experience impacting adolescents around the world. This study employed a randomized-controlled treatment design, with randomization at the school level, to investigate the impact of a school adjustment program for middle school transition and potential gender differences. Participants included 1147 students (M age  = 9.62; SD = 0.30, 45.7 % girls), who were assessed at four time points during the transition, regarding five dimensions of self-concept (academic, social, emotional, physical and family) and self-esteem. Parallel growth curves were employed to analyze the evolution of self-concept. Following the transition to middle school, students reported lower levels of self-concept (academic, emotional and physical) and self-esteem, while participation in the intervention led to increases in self-esteem and gains in social self-concept. No gender differences were found. These results provide preliminary evidence supporting such interventions in early middle school transitions.

View subspaces for indexing and retrieval of 3D models

Science.gov (United States)

Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel

2010-02-01

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

Science.gov (United States)

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Chronic condition self-management support for Aboriginal people: Adapting tools and training.

Science.gov (United States)

Battersby, Malcolm; Lawn, Sharon; Kowanko, Inge; Bertossa, Sue; Trowbridge, Coral; Liddicoat, Raylene

2018-04-22

Chronic conditions are major health problems for Australian Aboriginal people. Self-management programs can improve health outcomes. However, few health workers are skilled in self-management support and existing programs are not always appropriate in Australian Aboriginal contexts. The goal was to increase the capacity of the Australian health workforce to support Australian Aboriginal people to self-manage their chronic conditions by adapting the Flinders Program of chronic condition self-management support for Australian Aboriginal clients and develop and deliver training for health professionals to implement the program. Feedback from health professionals highlighted that the Flinders Program assessment and care planning tools needed to be adapted to suit Australian Aboriginal contexts. Through consultation with Australian Aboriginal Elders and other experts, the tools were condensed into an illustrated booklet called 'My Health Story'. Associated training courses and resources focusing on cultural safety and effective engagement were developed. A total of 825 health professionals  across Australia was trained and 61 people qualified as accredited trainers in the program, ensuring sustainability. The capacity and skills of the Australian health workforce to engage with and support Australian Aboriginal people to self-manage their chronic health problems significantly increased as a result of this project. The adapted tools and training were popular and appreciated by the health care organisations, health professionals and clients involved. The adapted tools have widespread appeal for cultures that do not have Western models of health care and where there are health literacy challenges. My Health Story has already been used internationally. © 2018 National Rural Health Alliance Ltd.

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

Psychological Adaptation, Marital Satisfaction, and Academic Self-Efficacy of International Students

Science.gov (United States)

Bulgan, Gökçe; Çiftçi, Ayse

2017-01-01

The authors investigated marital satisfaction and academic self-efficacy in relation to psychological adaptation (i.e., psychological well-being, life satisfaction) in a sample of 198 married international students. Results of multiple regression analyses indicated that marital satisfaction and academic self-efficacy accounted for 45.9% of…

A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems

NARCIS (Netherlands)

Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik

2003-01-01

The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method

Predicting Career Adaptability through Self-Esteem and Social Support: A Research on Young Adults

Science.gov (United States)

Ataç, Lale Oral; Dirik, Deniz; Tetik, Hilmiye Türesin

2018-01-01

The purpose of this study is to investigate the relationship between career adaptability and self-esteem, and analyze the moderating role of social support in this relationship on a sample of 313 young adults. The results of the study confirm that career adaptability is significantly predicted by self-esteem. Moreover, findings suggest that (1)…

Experimental investigation of biomimetic self-pumping and self-adaptive transpiration cooling.

Science.gov (United States)

Jiang, Pei-Xue; Huang, Gan; Zhu, Yinhai; Xu, Ruina; Liao, Zhiyuan; Lu, Taojie

2017-09-01

Transpiration cooling is an effective way to protect high heat flux walls. However, the pumps for the transpiration cooling system make the system more complex and increase the load, which is a huge challenge for practical applications. A biomimetic self-pumping transpiration cooling system was developed inspired by the process of trees transpiration that has no pumps. An experimental investigation showed that the water coolant automatically flowed from the water tank to the hot surface with a height difference of 80 mm without any pumps. A self-adaptive transpiration cooling system was then developed based on this mechanism. The system effectively cooled the hot surface with the surface temperature kept to about 373 K when the heating flame temperature was 1639 K and the heat flux was about 0.42 MW m -2 . The cooling efficiency reached 94.5%. The coolant mass flow rate adaptively increased with increasing flame heat flux from 0.24 MW m -2 to 0.42 MW m -2 while the cooled surface temperature stayed around 373 K. Schlieren pictures showed a protective steam layer on the hot surface which blocked the flame heat flux to the hot surface. The protective steam layer thickness also increased with increasing heat flux.

The effect of adaptive versus static practicing on student learning - evidence from a randomized field experiment

NARCIS (Netherlands)

van Klaveren, Chris; Vonk, Sebastiaan; Cornelisz, Ilja

2017-01-01

Schools and governments are increasingly investing in adaptive practice software. To date, the evidence whether adaptivity improves learning outcomes is limited and mixed. A large-scale randomized control trial is conducted in Dutch secondary schools to evaluate the effectiveness of an adaptive

Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.

Science.gov (United States)

Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin

2017-07-01

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces

International Nuclear Information System (INIS)

Vourdas, A.

2014-01-01

The orthocomplemented modular lattice of subspaces L[H(d)], of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)]). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H 1 ,H 2 ), which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H 1 ),P(H 2 ), to the subspaces H 1 , H 2 . As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities

Expedited Holonomic Quantum Computation via Net Zero-Energy-Cost Control in Decoherence-Free Subspace.

Science.gov (United States)

Pyshkin, P V; Luo, Da-Wei; Jing, Jun; You, J Q; Wu, Lian-Ao

2016-11-25

Holonomic quantum computation (HQC) may not show its full potential in quantum speedup due to the prerequisite of a long coherent runtime imposed by the adiabatic condition. Here we show that the conventional HQC can be dramatically accelerated by using external control fields, of which the effectiveness is exclusively determined by the integral of the control fields in the time domain. This control scheme can be realized with net zero energy cost and it is fault-tolerant against fluctuation and noise, significantly relaxing the experimental constraints. We demonstrate how to realize the scheme via decoherence-free subspaces. In this way we unify quantum robustness merits of this fault-tolerant control scheme, the conventional HQC and decoherence-free subspace, and propose an expedited holonomic quantum computation protocol.

Quasi-CW diode-pumped self-starting adaptive laser with self-Q-switched output.

Science.gov (United States)

Smith, G; Damzen, M J

2007-05-14

An investigation is made into a quasi-CW (QCW) diode-pumped holographic adaptive laser utilising an ultra high gain (approximately 10(4)) Nd:YVO(4) bounce amplifier. The laser produces pulses at 1064 nm with energy approximately 0.6 mJ, duration laser configuration, the output was amplified to obtain pulses of approximately 5.6 mJ energy, approximately 7 ns duration and approximately 1 MW peak power. The output spatial quality is also M(2)diode-pumped self-adaptive holographic lasers can provide a useful source of high peak power, short duration pulses with excellent spatial quality and narrow linewidth spectrum.

Kernel based subspace projection of near infrared hyperspectral images of maize kernels

DEFF Research Database (Denmark)

Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

2009-01-01

In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients

Energy Technology Data Exchange (ETDEWEB)

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

2017-12-01

We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.

Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

International Nuclear Information System (INIS)

Rao, R.V.; More, K.C.

2017-01-01

Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.

Third-order nonlinear differential operators preserving invariant subspaces of maximal dimension

International Nuclear Information System (INIS)

Qu Gai-Zhu; Zhang Shun-Li; Li Yao-Long

2014-01-01

In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order quadratic operators with constant coefficients is obtained. One example is given to derive special solutions for evolution equations with third-order quadratic operators. (general)

Mechanisms within the Parietal Cortex Correlate with the Benefits of Random Practice in Motor Adaptation

Directory of Open Access Journals (Sweden)

Benjamin Thürer

2017-08-01

Full Text Available The motor learning literature shows an increased retest or transfer performance after practicing under unstable (random conditions. This random practice effect (also known as contextual interference effect is frequently investigated on the behavioral level and discussed in the context of mechanisms of the dorsolateral prefrontal cortex and increased cognitive efforts during movement planning. However, there is a lack of studies examining the random practice effect in motor adaptation tasks and, in general, the underlying neural processes of the random practice effect are not fully understood. We tested 24 right-handed human subjects performing a reaching task using a robotic manipulandum. Subjects learned to adapt either to a blocked or a random schedule of different force field perturbations while subjects’ electroencephalography (EEG was recorded. The behavioral results showed a distinct random practice effect in terms of a more stabilized retest performance of the random compared to the blocked practicing group. Further analyses showed that this effect correlates with changes in the alpha band power in electrodes over parietal areas. We conclude that the random practice effect in this study is facilitated by mechanisms within the parietal cortex during movement execution which might reflect online feedback mechanisms.

Subspace methods for identification of human ankle joint stiffness.

Science.gov (United States)

Zhao, Y; Westwick, D T; Kearney, R E

2011-11-01

Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, since they appear and change together. Therefore, the direct estimation of the intrinsic and reflex stiffnesses is difficult. In this paper, we present a new, two-step procedure to estimate the intrinsic and reflex components of ankle stiffness. In the first step, a discrete-time, subspace-based method is used to estimate a state-space model for overall stiffness from the measured overall torque and then predict the intrinsic and reflex torques. In the second step, continuous-time models for the intrinsic and reflex stiffnesses are estimated from the predicted intrinsic and reflex torques. Simulations and experimental results demonstrate that the algorithm estimates the intrinsic and reflex stiffnesses accurately. The new subspace-based algorithm has three advantages over previous algorithms: 1) It does not require iteration, and therefore, will always converge to an optimal solution; 2) it provides better estimates for data with high noise or short sample lengths; and 3) it provides much more accurate results for data acquired under the closed-loop conditions, that prevail when subjects interact with compliant loads.

REVIEW APPROACHES ECONOMIC DEVELOPMENT OF THE TERRITORY OF THE ARCTIC ZONE OF THE RUSSIAN FEDERATION, PRESENTED IN THE FORM OF TARGET SUBSPACE

Directory of Open Access Journals (Sweden)

N. I. Didenko

2015-01-01

Full Text Available This paper presents a conceptual idea of the organization of management of development of the Arctic area of the Russian Federation in the form of a set of target subspace. Among the possible types of target subspace comprising the Arctic zone of the Russian Federation, allocated seven subspace: basic city mobile Camps, site production of mineral resources, recreational area, fishing area, the Northern Sea Route, infrastructure protection safe existence in the Arctic. The task of determining the most appropriate theoretical approach for the development of each target subspaces. To this end, the theoretical approaches of economic growth and development of the theory of "economic base» (Economic Base Theory; resource theory (Staple Theory; Theory sectors (Sector Theory; theory of growth poles (Growth Pole Theory; neoclassical theory (Neoclassical Growth Theory; theory of inter-regional trade (Interregional Trade Theory; theory of the commodity cycle; entrepreneurial theory (Entrepreneurship Theories.

Self-Testing Static Random-Access Memory

Science.gov (United States)

Chau, Savio; Rennels, David

1991-01-01

Proposed static random-access memory for computer features improved error-detecting and -correcting capabilities. New self-testing scheme provides for detection and correction of errors at any time during normal operation - even while data being written into memory. Faults in equipment causing errors in output data detected by repeatedly testing every memory cell to determine whether it can still store both "one" and "zero", without destroying data stored in memory.

Order out of Randomness: Self-Organization Processes in Astrophysics

Science.gov (United States)

Aschwanden, Markus J.; Scholkmann, Felix; Béthune, William; Schmutz, Werner; Abramenko, Valentina; Cheung, Mark C. M.; Müller, Daniel; Benz, Arnold; Chernov, Guennadi; Kritsuk, Alexei G.; Scargle, Jeffrey D.; Melatos, Andrew; Wagoner, Robert V.; Trimble, Virginia; Green, William H.

2018-03-01

Self-organization is a property of dissipative nonlinear processes that are governed by a global driving force and a local positive feedback mechanism, which creates regular geometric and/or temporal patterns, and decreases the entropy locally, in contrast to random processes. Here we investigate for the first time a comprehensive number of (17) self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous " order out of randomness", during the evolution from an initially disordered system to an ordered quasi-stationary system, mostly by quasi-periodic limit-cycle dynamics, but also by harmonic (mechanical or gyromagnetic) resonances. The global driving force can be due to gravity, electromagnetic forces, mechanical forces (e.g., rotation or differential rotation), thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational (Balbus-Hawley) instability, the convective (Rayleigh-Bénard) instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or a loss-cone instability. Physical models of astrophysical self-organization processes require hydrodynamic, magneto-hydrodynamic (MHD), plasma, or N-body simulations. Analytical formulations of self-organizing systems generally involve coupled differential equations with limit-cycle solutions of the Lotka-Volterra or Hopf-bifurcation type.

Adaptation to the edge of chaos in a self-starting Kerr-lens mode-locked laser

Science.gov (United States)

Hsu, C. C.; Lin, J. H.; Hsieh, W. F.

2009-08-01

We experimentally and numerically demonstrated that self-focusing acts as a slow-varying control parameter that suppresses the transient chaos to reach a stable mode-locking (ML) state in a self-starting Kerr-lens mode-locked Ti:sapphire laser without external modulation and feedback control. Based on Fox-Li’s approach, including the self-focusing effect, the theoretical simulation reveals that the self-focusing effect is responsible for the self-adaptation. The self-adaptation occurs at the boundary between the chaotic and continuous output regions in which the laser system begins with a transient chaotic state with fractal correlation dimension, and then evolves with reducing dimension into the stable ML state.

Code subspaces for LLM geometries

Science.gov (United States)

Berenstein, David; Miller, Alexandra

2018-03-01

We consider effective field theory around classical background geometries with a gauge theory dual, specifically those in the class of LLM geometries. These are dual to half-BPS states of N= 4 SYM. We find that the language of code subspaces is natural for discussing the set of nearby states, which are built by acting with effective fields on these backgrounds. This work extends our previous work by going beyond the strict infinite N limit. We further discuss how one can extract the topology of the state beyond N→∞ and find that, as before, uncertainty and entanglement entropy calculations provide a useful tool to do so. Finally, we discuss obstructions to writing down a globally defined metric operator. We find that the answer depends on the choice of reference state that one starts with. Therefore, within this setup, there is ambiguity in trying to write an operator that describes the metric globally.

Roy's Adaptation Model-Guided Education and Promoting the Adaptation of Veterans With Lower Extremities Amputation.

Science.gov (United States)

Azarmi, Somayeh; Farsi, Zahra

2015-10-01

Any defect in extremities of the body can affect different life aspects. The purpose of this study was to investigate the effect of Roy's adaptation model-guided education on promoting the adaptation of veterans with lower extremities amputation. In a randomized clinical trial, 60 veterans with lower extremities amputation referring to Kowsar Orthotics and Prosthetics Center of veterans clinic in Tehran, Iran, were recruited with convenience method and were randomly assigned to intervention and control groups during 2013 - 2014. For data collection, Roy's adaptation model questionnaire was used. After completing the questionnaires in both groups, maladaptive behaviors were determined in the intervention group and an education program based on Roy's adaptation model was implemented. After two months, both groups completed the questionnaires again. Data was analyzed with SPSS software. Independent t-test showed statistically significant differences between the two groups in the post-test stage in terms of the total score of adaptation (P = 0.001) as well as physiologic (P = 0.0001) and role function modes (P = 0.004). The total score of adaptation (139.43 ± 5.45 to 127.54 ± 14.55, P = 0.006) as well as the scores of physiologic (60.26 ± 5.45 to 53.73 ± 7.79, P = 0.001) and role function (20.30 ± 2.42 to 18.13 ± 3.18, P = 0.01) modes in the intervention group significantly increased, whereas the scores of self-concept (42.10 ± 4.71 to 39.40 ± 5.67, P = 0.21) and interdependence (16.76 ± 2.22 to 16.30 ± 2.57, P = 0.44) modes in the two stages did not have a significant difference. Findings of this research indicated that the Roy's adaptation model-guided education promoted the adaptation level of physiologic and role function modes in veterans with lower extremities amputation. However, this intervention could not promote adaptation in self-concept and interdependence modes. More intervention is advised based on Roy's adaptation model for improving the

Linear Subspace Ranking Hashing for Cross-Modal Retrieval.

Science.gov (United States)

Li, Kai; Qi, Guo-Jun; Ye, Jun; Hua, Kien A

2017-09-01

Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features' ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied up to any specific form of loss function, which is typical for existing cross-modal hashing methods, but rather we can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with only moderate training and testing time.

Asthma Self-Management Model: Randomized Controlled Trial

Science.gov (United States)

Olivera, Carolina M. X.; Vianna, Elcio Oliveira; Bonizio, Roni C.; de Menezes, Marcelo B.; Ferraz, Erica; Cetlin, Andrea A.; Valdevite, Laura M.; Almeida, Gustavo A.; Araujo, Ana S.; Simoneti, Christian S.; de Freitas, Amanda; Lizzi, Elisangela A.; Borges, Marcos C.; de Freitas, Osvaldo

2016-01-01

Information for patients provided by the pharmacist is reflected in adhesion to treatment, clinical results and patient quality of life. The objective of this study was to assess an asthma self-management model for rational medicine use. This was a randomized controlled trial with 60 asthmatic patients assigned to attend five modules presented by…

Modal–Physical Hybrid System Identification of High-rise Building via Subspace and Inverse-Mode Methods

Directory of Open Access Journals (Sweden)

Kohei Fujita

2017-08-01

Full Text Available A system identification (SI problem of high-rise buildings is investigated under restricted data environments. The shear and bending stiffnesses of a shear-bending model (SB model representing the high-rise buildings are identified via the smart combination of the subspace and inverse-mode methods. Since the shear and bending stiffnesses of the SB model can be identified in the inverse-mode method by using the lowest mode of horizontal displacements and floor rotation angles, the lowest mode of the objective building is identified first by using the subspace method. Identification of the lowest mode is performed by using the amplitude of transfer functions derived in the subspace method. Considering the resolution in measuring the floor rotation angles in lower stories, floor rotation angles in most stories are predicted from the floor rotation angle at the top floor. An empirical equation of floor rotation angles is proposed by investigating those for various building models. From the viewpoint of application of the present SI method to practical situations, a non-simultaneous measurement system is also proposed. In order to investigate the reliability and accuracy of the proposed SI method, a 10-story building frame subjected to micro-tremor is examined.

Architecture and Knowledge-Driven Self-Adaptive Security in Smart Space

Directory of Open Access Journals (Sweden)

Antti Evesti

2013-03-01

Full Text Available Dynamic and heterogeneous smart spaces cause challenges for security because it is impossible to anticipate all the possible changes at design-time. Self-adaptive security is an applicable solution for this challenge. This paper presents an architectural approach for security adaptation in smart spaces. The approach combines an adaptation loop, Information Security Measuring Ontology (ISMO and a smart space security-control model. The adaptation loop includes phases to monitor, analyze, plan and execute changes in the smart space. The ISMO offers input knowledge for the adaptation loop and the security-control model enforces dynamic access control policies. The approach is novel because it defines the whole adaptation loop and knowledge required in each phase of the adaptation. The contributions are validated as a part of the smart space pilot implementation. The approach offers reusable and extensible means to achieve adaptive security in smart spaces and up-to-date access control for devices that appear in the space. Hence, the approach supports the work of smart space application developers.

Uncertainty calculation for modal parameters used with stochastic subspace identification: an application to a bridge structure

Science.gov (United States)

Hsu, Wei-Ting; Loh, Chin-Hsiung; Chao, Shu-Hsien

2015-03-01

Stochastic subspace identification method (SSI) has been proven to be an efficient algorithm for the identification of liner-time-invariant system using multivariate measurements. Generally, the estimated modal parameters through SSI may be afflicted with statistical uncertainty, e.g. undefined measurement noises, non-stationary excitation, finite number of data samples etc. Therefore, the identified results are subjected to variance errors. Accordingly, the concept of the stabilization diagram can help users to identify the correct model, i.e. through removing the spurious modes. Modal parameters are estimated at successive model orders where the physical modes of the system are extracted and separated from the spurious modes. Besides, an uncertainty computation scheme was derived for the calculation of uncertainty bounds for modal parameters at some given model order. The uncertainty bounds of damping ratios are particularly interesting, as the estimation of damping ratios are difficult to obtain. In this paper, an automated stochastic subspace identification algorithm is addressed. First, the identification of modal parameters through covariance-driven stochastic subspace identification from the output-only measurements is used for discussion. A systematic way of investigation on the criteria for the stabilization diagram is presented. Secondly, an automated algorithm of post-processing on stabilization diagram is demonstrated. Finally, the computation of uncertainty bounds for each mode with all model order in the stabilization diagram is utilized to determine system natural frequencies and damping ratios. Demonstration of this study on the system identification of a three-span steel bridge under operation condition is presented. It is shown that the proposed new operation procedure for the automated covariance-driven stochastic subspace identification can enhance the robustness and reliability in structural health monitoring.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

Science.gov (United States)

Xu, Y; Li, N

2014-09-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

International Nuclear Information System (INIS)

Xu, Y; Li, N

2014-01-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

Adaptive importance sampling of random walks on continuous state spaces

International Nuclear Information System (INIS)

Baggerly, K.; Cox, D.; Picard, R.

1998-01-01

The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material

Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation

Directory of Open Access Journals (Sweden)

Hong SeungHo

2011-01-01

Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.

SAGE - MULTIDIMENSIONAL SELF-ADAPTIVE GRID CODE

Science.gov (United States)

Davies, C. B.

1994-01-01

SAGE, Self Adaptive Grid codE, is a flexible tool for adapting and restructuring both 2D and 3D grids. Solution-adaptive grid methods are useful tools for efficient and accurate flow predictions. In supersonic and hypersonic flows, strong gradient regions such as shocks, contact discontinuities, shear layers, etc., require careful distribution of grid points to minimize grid error and produce accurate flow-field predictions. SAGE helps the user obtain more accurate solutions by intelligently redistributing (i.e. adapting) the original grid points based on an initial or interim flow-field solution. The user then computes a new solution using the adapted grid as input to the flow solver. The adaptive-grid methodology poses the problem in an algebraic, unidirectional manner for multi-dimensional adaptations. The procedure is analogous to applying tension and torsion spring forces proportional to the local flow gradient at every grid point and finding the equilibrium position of the resulting system of grid points. The multi-dimensional problem of grid adaption is split into a series of one-dimensional problems along the computational coordinate lines. The reduced one dimensional problem then requires a tridiagonal solver to find the location of grid points along a coordinate line. Multi-directional adaption is achieved by the sequential application of the method in each coordinate direction. The tension forces direct the redistribution of points to the strong gradient region. To maintain smoothness and a measure of orthogonality of grid lines, torsional forces are introduced that relate information between the family of lines adjacent to one another. The smoothness and orthogonality constraints are direction-dependent, since they relate only the coordinate lines that are being adapted to the neighboring lines that have already been adapted. Therefore the solutions are non-unique and depend on the order and direction of adaption. Non-uniqueness of the adapted grid is

Differences of adaptation to school and self-resilience before and after sleep education for adolescent

OpenAIRE

Lee, So-Mi; Kim, Jong-Hee

2016-01-01

This study aims to verify the effectiveness of sleep education by identifying the differences of adaption to school and self-resilience of the high school students before and after sleep education for a certain period of time. The conclusion of this study is presented below: First, there were differences in adaptation to school and self-resilience of the high school students before and after sleep education for the high school students. After sleep education, adaptation to school environment ...

Self-Adaptive On-Chip System Based on Cross-Layer Adaptation Approach

Directory of Open Access Journals (Sweden)

Kais Loukil

2013-01-01

Full Text Available The emergence of mobile and battery operated multimedia systems and the diversity of supported applications mount new challenges in terms of design efficiency of these systems which must provide a maximum application quality of service (QoS in the presence of a dynamically varying environment. These optimization problems cannot be entirely solved at design time and some efficiency gains can be obtained at run-time by means of self-adaptivity. In this paper, we propose a new cross-layer hardware (HW/software (SW adaptation solution for embedded mobile systems. It supports application QoS under real-time and lifetime constraints via coordinated adaptation in the hardware, operating system (OS, and application layers. Our method relies on an original middleware solution used on both global and local managers. The global manager (GM handles large, long-term variations whereas the local manager (LM is used to guarantee real-time constraints. The GM acts in three layers whereas the LM acts in application and OS layers only. The main role of GM is to select the best configuration for each application to meet the constraints of the system and respect the preferences of the user. The proposed approach has been applied to a 3D graphics application and successfully implemented on an Altera FPGA.

Invariant subspaces in some function spaces on symmetric spaces. II

International Nuclear Information System (INIS)

Platonov, S S

1998-01-01

Let G be a semisimple connected Lie group with finite centre, K a maximal compact subgroup of G, and M=G/K a Riemannian symmetric space of non-compact type. We study the problem of describing the structure of closed linear subspaces in various function spaces on M that are invariant under the quasiregular representation of the group G. We consider the case when M is a symplectic symmetric space of rank 1

Metastable decoherence-free subspaces and electromagnetically induced transparency in interacting many-body systems

DEFF Research Database (Denmark)

Macieszczak, Katarzyna; Zhou, Yanli; Hofferberth, Sebastian

2017-01-01

to stationarity this leads to a slow dynamics, which renders the typical assumption of fast relaxation invalid. We derive analytically the effective nonequilibrium dynamics in the decoherence-free subspace, which features coherent and dissipative two-body interactions. We discuss the use of this scenario...

Community-based peer-led diabetes self-management: a randomized trial.

Science.gov (United States)

Lorig, Kate; Ritter, Philip L; Villa, Frank J; Armas, Jean

2009-01-01

The purpose of this study is to determine the effectiveness of a community-based diabetes self-management program comparing treatment participants to a randomized usual-care control group at 6 months. A total of 345 adults with type 2 diabetes but no criteria for high A1C were randomized to a usual-care control group or 6-week community-based, peer-led diabetes self-management program (DSMP). Randomized participants were compared at 6 months. The DSMP intervention participants were followed for an additional 6 months (12 months total). A1C and body mass index were measured at baseline, 6 months, and 12 months. All other data were collected by self-administered questionnaires. At 6 months, DSMP participants did not demonstrate improvements in A1C as compared with controls. Baseline A1C was much lower than in similar trials. Participants did have significant improvements in depression, symptoms of hypoglycemia, communication with physicians, healthy eating, and reading food labels (P < .01). They also had significant improvements in patient activation and self-efficacy. At 12 months, DSMP intervention participants continued to demonstrate improvements in depression, communication with physicians, healthy eating, patient activation, and self-efficacy (P < .01). There were no significant changes in utilization measures. These findings suggest that people with diabetes without elevated A1C can benefit from a community-based, peer-led diabetes program. Given the large number of people with diabetes and lack of low-cost diabetes education, the DSMP deserves consideration for implementation.

Adolescent Girls' Self-Concept and Its Related Factors Based on Roy Adaptation Model

OpenAIRE

M. Basiri Moghadam; SH. Khosravan; L. Sadeghmoghadam; N. Ebrahimi Senoo

2017-01-01

Aims: One of the most important factors of individual health in the adolescents is the self-concept. As a nursing model, the Roy adaptation model mainly investigates the factor. The aim of the study was to investigate the self-concept and its related factors in the adolescent girls in Gonabad Township, based on the Roy adaptation model. Instrument & Methods: In the descriptive cross-sectional study, 270 adolescent girls were studied in Gonabad Township, Iran, in 2015. The subjects were s...

Adaptive Backstepping Self-balancing Control of a Two-wheel Electric Scooter

Directory of Open Access Journals (Sweden)

Nguyen Ngoc Son

2014-10-01

Full Text Available This paper introduces an adaptive backstepping control law for a two-wheel electric scooter (eScooter with a nonlinear uncertain model. Adaptive backstepping control is integrated with feedback control that satisfies Lyapunov stability. By using the recursive structure to find the controlled function and estimate uncertain parameters, an adaptive backstepping method allows us to build a feedback control law that efficiently controls a self-balancing controller of the eScooter. Additionally, a controller area network (CAN bus with high reliability is applied for communicating between the modules of the eScooter. Simulation and experimental results demonstrate the robustness and good performance of the proposed adaptive backstepping control.

An Efficient and Self-Adapting Localization in Static Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Wei Dong

2009-08-01

Full Text Available Localization is one of the most important subjects in Wireless Sensor Networks (WSNs. To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL, Adapting MBL (A-MBL, and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL approach to achieve more flexibility and achieve almost the same performance with A-MBL.

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

Directory of Open Access Journals (Sweden)

Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Increased 1-year continuation of DMPA among women randomized to self-administration: results from a randomized controlled trial at Planned Parenthood.

Science.gov (United States)

Kohn, Julia E; Simons, Hannah R; Della Badia, Lisa; Draper, Elissa; Morfesis, Johanna; Talmont, Elizabeth; Beasley, Anitra; McDonald, Melanie; Westhoff, Carolyn L

2018-03-01

Self-administration of subcutaneous depot medroxyprogesterone acetate (DMPA-sc) is feasible, acceptable, and effective. Our objective was to compare one-year continuation of DMPA-sc between women randomized to self-administration versus clinic administration. We randomized 401 females ages 15-44 requesting DMPA at clinics in Texas and New Jersey to self-administration or clinic administration in a 1:1 allocation. Clinic staff taught participants randomized to self-administration to self-inject and observed the first injection; participants received instructions, a sharps container, and three doses for home use. Participants randomized to clinic administration received usual care. All participants received DMPA-sc at no cost and injection reminders via text message or email. We conducted follow-up surveys at six and 12 months. Three hundred thirty-six participants (84%) completed the 12-month survey; 316 completed both follow-up surveys (an 80% response rate excluding eight withdrawals). Participants ranged in age from 16-44. One-year DMPA continuous use was 69% in the self-administration group and 54% in the clinic group (p=.005). There were three self-reported pregnancies during the study period, all occurred in the clinic group; all three women had discontinued DMPA and one reported her pregnancy as intended. Among the self-administration group, 97% reported that self-administration was very or somewhat easy; 87% would recommend self-administration of DMPA-sc to a friend. Among the clinic group, 52% reported interest in self-administration in the future. Satisfaction was similar between groups. No serious adverse events were reported. DMPA self-administration improves contraceptive continuation and is a feasible and acceptable option for women and adolescents. Self-administration of subcutaneous DMPA can improve contraceptive access, autonomy, and continuation, and is a feasible and acceptable option for women and adolescents. It should be made widely available

Finite element method for solving Kohn-Sham equations based on self-adaptive tetrahedral mesh

International Nuclear Information System (INIS)

Zhang Dier; Shen Lihua; Zhou Aihui; Gong Xingao

2008-01-01

A finite element (FE) method with self-adaptive mesh-refinement technique is developed for solving the density functional Kohn-Sham equations. The FE method adopts local piecewise polynomials basis functions, which produces sparsely structured matrices of Hamiltonian. The method is well suitable for parallel implementation without using Fourier transform. In addition, the self-adaptive mesh-refinement technique can control the computational accuracy and efficiency with optimal mesh density in different regions

Adaptive change in self-concept and well-being during conjugal loss in later life.

Science.gov (United States)

Montpetit, Mignon A; Bergeman, C S; Bisconti, Toni L; Rausch, Joseph R

2006-01-01

The present study examines the association between the self-concept and adaptation to conjugal loss; the primary aim was to explore whether those individuals high in self-esteem, environmental mastery, and optimism have more adaptive resources with which to ameliorate the detrimental sequelae of bereavement. Analyses were conducted on data collected from 58 widows every four months over a two-year period. One goal of the research was to explore the adequacy of the theoretically chosen operational definition of the self-concept; another goal was to analyze how changes in the level of self-concept components correlated with changes in levels of depression, health, and grief resolution as individuals adjusted to their losses. Analyses revealed that trajectories of depression and grief resolution were more highly related than health to changes in self-concept.

Residual and Backward Error Bounds in Minimum Residual Krylov Subspace Methods

Czech Academy of Sciences Publication Activity Database

Paige, C. C.; Strakoš, Zdeněk

2002-01-01

Roč. 23, č. 6 (2002), s. 1899-1924 ISSN 1064-8275 R&D Projects: GA AV ČR IAA1030103 Institutional research plan: AV0Z1030915 Keywords : linear equations * eigenproblem * large sparse matrices * iterative solutions * Krylov subspace methods * Arnoldi method * GMRES * modified Gram-Schmidt * least squares * total least squares * singular values Subject RIV: BA - General Mathematics Impact factor: 1.291, year: 2002

Web-Based Decision Aid to Assist Help-Seeking Choices for Young People Who Self-Harm: Outcomes From a Randomized Controlled Feasibility Trial

Science.gov (United States)

Patel, Krisna; French, Rebecca S; Henderson, Claire; Ougrin, Dennis; Slade, Mike; Moran, Paul

2018-01-01

for parental consent is a key barrier to intervention research on self-harm in the school setting. Adaptations to the research design and the intervention are needed before generalizable research about DAs can be successfully conducted in a school setting. Trial Registration International Standard Randomized Controlled Trial registry: ISRCTN11230559; http://www.isrctn.com/ISRCTN11230559 (Archived by WebCite at http://www.webcitation.org/6wqErsYWG) PMID:29382626

Cross-cultural Adaptation of the Self-care of Hypertension Inventory Into Brazilian Portuguese.

Science.gov (United States)

Silveira, Luana Claudia Jacoby; Rabelo-Silva, Eneida Rejane; Ávila, Christiane Whast; Beltrami Moreira, Leila; Dickson, Victoria Vaughan; Riegel, Barbara

Lifestyle changes and treatment adherence still constitute a challenge to healthcare providers involved in the care of persons with hypertension. The lack of validated instruments measuring the ability of hypertensive patients to manage their disease has slowed research progress in this area. The Self-care of Hypertension Inventory, originally developed in the United States, consists of 23 items divided across 3 scales: Self-care Maintenance, Self-care Management, and Self-care Confidence. These scales measure how well patients with hypertension adhere to treatment and manage elevated blood pressure, as well as their confidence in their ability to perform self-care. A rigorous cross-cultural adaptation and validation process is required before this instrument can be used in other countries. The aims of this study were to translate the Self-care of Hypertension Inventory into Brazilian Portuguese with cross-cultural adaptation and to evaluate interobserver reliability and temporal stability. This methodological study involved forward translation, synthesis of forward translations, back-translation, synthesis of back-translations, expert committee review, and pretesting. Interobserver agreement and the temporal stability of the scales were assessed. The expert committee proposed semantic and cultural modifications to some items and the addition of guidance statements to facilitate administration of the scale. Interobserver analysis demonstrated substantial agreement. Analysis of temporal stability showed near-perfect agreement. Cross-cultural adaptation of the Self-care of Hypertension Inventory successfully produced a Portuguese-language version of the instrument for further evaluation of psychometric properties. Once that step is completed, the scale can be used in Brazil.

Subspace-based optimization method for inverse scattering problems with an inhomogeneous background medium

International Nuclear Information System (INIS)

Chen, Xudong

2010-01-01

This paper proposes a version of the subspace-based optimization method to solve the inverse scattering problem with an inhomogeneous background medium where the known inhomogeneities are bounded in a finite domain. Although the background Green's function at each discrete point in the computational domain is not directly available in an inhomogeneous background scenario, the paper uses the finite element method to simultaneously obtain the Green's function at all discrete points. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the orthogonally complementary part is determined by solving a lower dimension optimization problem. This feature significantly speeds up the convergence of the algorithm and at the same time makes it robust against noise. Numerical simulations illustrate the efficacy of the proposed algorithm. The algorithm presented in this paper finds wide applications in nondestructive evaluation, such as through-wall imaging

Self-adaptive demodulation for polarization extinction ratio in distributed polarization coupling.

Science.gov (United States)

Zhang, Hongxia; Ren, Yaguang; Liu, Tiegen; Jia, Dagong; Zhang, Yimo

2013-06-20

A self-adaptive method for distributed polarization extinction ratio (PER) demodulation is demonstrated. It is characterized by dynamic PER threshold coupling intensity (TCI) and nonuniform PER iteration step length (ISL). Based on the preset PER calculation accuracy and original distribution coupling intensity, TCI and ISL can be made self-adaptive to determine contributing coupling points inside the polarizing devices. Distributed PER is calculated by accumulating those coupling points automatically and selectively. Two different kinds of polarization-maintaining fibers are tested, and PERs are obtained after merely 3-5 iterations using the proposed method. Comparison experiments with Thorlabs commercial instrument are also conducted, and results show high consistency. In addition, the optimum preset PER calculation accuracy of 0.05 dB is obtained through many repeated experiments.

The relationships of social support, uncertainty, self-efficacy, and commitment to prenatal psychosocial adaptation.

Science.gov (United States)

Hui Choi, W H; Lee, G L; Chan, Celia H Y; Cheung, Ray Y H; Lee, Irene L Y; Chan, Cecilia L W

2012-12-01

To report a study of the relations of prenatal psychosocial adaptation, social support, demographic and obstetric characteristics, uncertainty, information-seeking behaviour, motherhood normalization, self-efficacy, and commitment to pregnancy. Prenatal psychosocial assessment is recommended to identify psychosocial risk factors early to prevent psychiatric morbidities of mothers and children. However, knowledge on psychosocial adaptation and its explanatory variables is inconclusive. This study was non-experimental, with a cross-sectional, correlational, prospective design. The study investigated Hong Kong Chinese women during late pregnancy. Convenience sampling methods were used, with 550 women recruited from the low-risk clinics of three public hospitals. Data was collected between January-April 2007. A self-reported questionnaire was used, consisting of a number of measurements derived from an integrated framework of the Life Transition Theory and Theory of Uncertainty in Illness. Explanatory variables of psychosocial adaptation were identified using a structural equation modelling programme. The four explanatory variables of the psychosocial adaptation were social support, uncertainty, self-efficacy, and commitment to pregnancy. In the established model, which had good fit indices, greater psychosocial adaptation was associated with higher social support, higher self-efficacy, higher commitment to pregnancy, and lower uncertainty. The findings give clinicians and midwives guidance in the aspects to focus on when providing psychosocial assessment in routine prenatal screening. Since there are insufficient reliable screening tools to assist that assessment, midwives should receive adequate training, and effective screening instruments have to be identified. The explanatory role of uncertainty found in this study should encourage inquiries into the relationship between uncertainty and psychosocial adaptation in pregnancy. © 2012 Blackwell Publishing Ltd.

An adaptive physical activity intervention for overweight adults: a randomized controlled trial.

Directory of Open Access Journals (Sweden)

Marc A Adams

Full Text Available Physical activity (PA interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.Participants (N = 20 were randomized to one of two 6-month treatments: 1 static intervention (SI or 2 adaptive intervention (AI. Inactive overweight adults (85% women, M = 36.9 ± 9.2 years, 35% non-white in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001 and a group by study phase interaction was observed (p  .017. The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74.The adaptive intervention outperformed the static intervention for increasing

Free Energy Self-Averaging in Protein-Sized Random Heteropolymers

International Nuclear Information System (INIS)

Chuang, Jeffrey; Grosberg, Alexander Yu.; Kardar, Mehran

2001-01-01

Current theories of heteropolymers are inherently macroscopic, but are applied to mesoscopic proteins. To compute the free energy over sequences, one assumes self-averaging -- a property established only in the macroscopic limit. By enumerating the states and energies of compact 18, 27, and 36mers on a lattice with an ensemble of random sequences, we test the self-averaging approximation. We find that fluctuations in the free energy between sequences are weak, and that self-averaging is valid at the scale of real proteins. The results validate sequence design methods which exponentially speed up computational design and simplify experimental realizations

Adaptation of Self-Control and Self-Management Scale (SCMS) into Turkish Culture: A Study on Reliability and Validity

Science.gov (United States)

Ercoskun, Muhammet Hanifi

2016-01-01

The aim of this study is to adapt self-control and self-management scale (SCMS) developed by Mezo into Turkish and to test it considering gender and academic achievement variables. The scale was translated from English to Turkish for linguistic validity and then this scale was translated into English using back translation. The original and…

Are adaptations self-organized, autonomous, and harmonious? Assessing the social-ecological resilience literature

Directory of Open Access Journals (Sweden)

Thomas Hahn

2017-03-01

Full Text Available The paper analyzes how adaptability (adaptive capacity and adaptations is constructed in the literature on resilience of social-ecological systems (SES. According to some critics, this literature views adaptability as the capacity of SES to self-organize in an autonomous harmonious consensus-building process, ignoring strategies, conflicting goals, and power issues. We assessed 183 papers, coding two dimensions of adaptability: autonomous vs. intentional and descriptive vs. normative. We found a plurality of framings, where 51% of the papers perceived adaptability as autonomous, but one-third constructed adaptability as intentional processes driven by stakeholders; where social learning and networking are often used as strategies for changing power structures and achieving sustainability transformations. For the other dimension, adaptability was used normatively in 59% of the assessed papers, but one-third used descriptive framings. We found no evidence that the SES literature in general assumes a priori that adaptations are harmonious consensus-building processes. It is, rather, conflicts that are assumed, not spelled out, and assertions of "desirable" that are often not clarified by reference to policy documents or explicit normative frameworks. We discuss alternative definitions of adaptability and transformability to clarify or avoid the notion of desirability. Complex adaptive systems framing often precludes analysis of agency, but lately self-organization and emergence have been used to study actors with intentions, strategies, and conflicting interests. Transformations and power structures are increasingly being addressed in the SES literature. We conclude that ontological clashes between social science and SES research have resulted in multiple constructive pathways.

Self-adapting metal-ceramic coating for biomass and waste incineration plants

Energy Technology Data Exchange (ETDEWEB)

Faulstich, Martin [Technische Univ. Muenchen (Germany); Fehr, Karl Thomas; Ye, Ya-Ping [Ludwig-Maximilians-Univ., Muenchen (Germany); Loeh, Ingrid; Mocker, Mario; Wolf, Gerhard [ATZ Entwicklungszentrum, Sulzbach-Rosenberg (Germany)

2010-07-01

Thermally sprayed coatings might become a reasonable alternative to cost-intensive cladding of heat exchangers in biomass and waste incineration. Shortcomings of these coatings might be overcome by a double-layer system, consisting of Alloy 625 covered with yttria-stabilized zirconia. Under appropriate conditions, re-crystallized zirconium oxide and chromium oxide form a dense, self-adapting and self-healing barrier against further infiltration of gaseous species. (orig.)

Impact of Self-Interference on the Performance of Joint Partial RAKE Receiver and Adaptive Modulation

KAUST Repository

Nam, Sung Sik

2016-11-23

In this paper, we investigate the impact of self-interference on the performance of a joint partial RAKE (PRAKE) receiver and adaptive modulation over both independent and identically distributed and independent but non-identically distributed Rayleigh fading channels. To better observe the impact of self-interference, our approach starts from considering the signal to interference plus noise ratio. Specifically, we accurately analyze the outage probability, the average spectral efficiency, and the average bit error rate as performance measures in the presence of self-interference. Several numerical and simulation results are selected to present the performance of the joint PRAKE receiver and adaptive modulation subject to self-interference.

Prediction of adaptive self-regulatory responses to arthritis pain anxiety in exercising adults: does pain acceptance matter?

Science.gov (United States)

Cary, Miranda Ashley; Gyurcsik, Nancy C; Brawley, Lawrence R

2015-01-01

Exercising for ≥ 150 min/week is a recommended strategy for self-managing arthritis. However, exercise nonadherence is a problem. Arthritis pain anxiety may interfere with regular exercise. According to the fear-avoidance model, individuals may confront their pain anxiety by using adaptive self-regulatory responses (eg, changing exercise type or duration). Furthermore, the anxiety-self-regulatory responses relationship may vary as a function of individuals' pain acceptance levels. To investigate pain acceptance as a moderator of the pain anxiety-adaptive self-regulatory responses relationship. The secondary objective was to examine whether groups of patients who differed in meeting exercise recommendations also differed in pain-related and self-regulatory responses. Adults (mean [± SD] age 49.75 ± 13.88 years) with medically diagnosed arthritis completed online measures of arthritis pain-related variables and self-regulatory responses at baseline, and exercise participation two weeks later. Individuals meeting (n=87) and not meeting (n=49) exercise recommendations were identified. Hierarchical multiple regression analysis revealed that pain acceptance moderated the anxiety-adaptive self-regulatory responses relationship. When pain anxiety was lower, greater pain acceptance was associated with less frequent use of adaptive responses. When anxiety was higher, adaptive responses were used regardless of pain acceptance level. MANOVA findings revealed that participants meeting the recommended exercise dose reported significantly lower pain and pain anxiety, and greater pain acceptance (Pself-regulatory capacity to cope with additional challenges to exercise adherence (eg, busy schedule).

Projected Gauss-Seidel subspace minimization method for interactive rigid body dynamics

DEFF Research Database (Denmark)

Silcowitz-Hansen, Morten; Abel, Sarah Maria Niebe; Erleben, Kenny

2010-01-01

artifacts such as viscous or damped contact response. In this paper, we present a new approach to contact force determination. We formulate the contact force problem as a nonlinear complementarity problem, and discretize the problem to derive the Projected Gauss–Seidel method. We combine the Projected Gauss......–Seidel method with a subspace minimization method. Our new method shows improved qualities and superior convergence properties for specific configurations....

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

A Krylov Subspace Method for Unstructured Mesh SN Transport Computation

International Nuclear Information System (INIS)

Yoo, Han Jong; Cho, Nam Zin; Kim, Jong Woon; Hong, Ser Gi; Lee, Young Ouk

2010-01-01

Hong, et al., have developed a computer code MUST (Multi-group Unstructured geometry S N Transport) for the neutral particle transport calculations in three-dimensional unstructured geometry. In this code, the discrete ordinates transport equation is solved by using the discontinuous finite element method (DFEM) or the subcell balance methods with linear discontinuous expansion. In this paper, the conventional source iteration in the MUST code is replaced by the Krylov subspace method to reduce computing time and the numerical test results are given

Study on the pressure self-adaptive water-tight junction box in underwater vehicle

Directory of Open Access Journals (Sweden)

Haocai Huang

2012-09-01

Full Text Available Underwater vehicles play a very important role in underwater engineering. Water-tight junction box (WJB is one of the key components in underwater vehicle. This paper puts forward a pressure self-adaptive water-tight junction box (PSAWJB which improves the reliability of the WJB significantly by solving the sealing and pressure problems in conventional WJB design. By redundancy design method, the pressure self-adaptive equalizer (PSAE is designed in such a way that it consists of a piston pressure-adaptive compensator (PPAC and a titanium film pressure-adaptive compensator (TFPAC. According to hydro-mechanical simulations, the operating volume of the PSAE is more than or equal to 11.6 % of the volume of WJB liquid system. Furthermore, the required operating volume of the PSAE also increases as the gas content of oil, hydrostatic pressure or temperature difference increases. The reliability of the PSAWJB is proved by hyperbaric chamber tests.

Self-adaptive strain-relaxation optimization for high-energy lithium storage material through crumpling of graphene.

Science.gov (United States)

Zhao, Yunlong; Feng, Jiangang; Liu, Xue; Wang, Fengchao; Wang, Lifen; Shi, Changwei; Huang, Lei; Feng, Xi; Chen, Xiyuan; Xu, Lin; Yan, Mengyu; Zhang, Qingjie; Bai, Xuedong; Wu, Hengan; Mai, Liqiang

2014-08-01

High-energy lithium battery materials based on conversion/alloying reactions have tremendous potential applications in new generation energy storage devices. However, these applications are limited by inherent large volume variations and sluggish kinetics. Here we report a self-adaptive strain-relaxed electrode through crumpling of graphene to serve as high-stretchy protective shells on metal framework, to overcome these limitations. The graphene sheets are self-assembled and deeply crumpled into pinecone-like structure through a contraction-strain-driven crumpling method. The as-prepared electrode exhibits high specific capacity (2,165 mAh g(-1)), fast charge-discharge rate (20 A g(-1)) with no capacity fading in 1,000 cycles. This kind of crumpled graphene has self-adaptive behaviour of spontaneous unfolding-folding synchronized with cyclic expansion-contraction volumetric variation of core materials, which can release strain and maintain good electric contact simultaneously. It is expected that such findings will facilitate the applications of crumpled graphene and the self-adaptive materials.

Effect of Treatment Education Based on the Roy Adaptation Model on Adjustment of Hemodialysis Patients.

Science.gov (United States)

Kacaroglu Vicdan, Ayse; Gulseven Karabacak, Bilgi

2016-01-01

The Roy Adaptation Model examines the individual in 4 fields: physiological mode, self-concept mode, role function mode, and interdependence mode. Hemodialysis treatment is associated with the Roy Adaptation Model as it involves fields that might be needed by the individual with chronic renal disease. This research was conducted as randomized controlled experiment with the aim of determining the effect of the education given in accordance with the Roy Adaptation Model on physiological, psychological, and social adaptation of individuals undergoing hemodialysis treatment. This was a random controlled experimental study. The study was conducted at a dialysis center in Konya-Aksehir in Turkey between July 1 and December 31, 2012. The sample was composed of 82 individuals-41 experimental and 41 control. In the second interview, there was a decrease in the systolic blood pressures and body weights of the experimental group, an increase in the scores of functional performance and self-respect, and a decrease in the scores of psychosocial adaptation. In the control group, on the other hand, there was a decrease in the scores of self-respect and an increase in the scores of psychosocial adaptation. The 2 groups were compared in terms of adaptation variables and a difference was determined on behalf of the experimental group. The training that was provided and evaluated for individuals receiving hemodialysis according to 4 modes of the Roy Adaptation Model increased physical, psychological, and social adaptation.

Prospective evaluation of psychosocial adaptation to stoma surgery: the role of self-efficacy.

NARCIS (Netherlands)

Bekkers, M.J.T.; Knippenberg, F.C.E. van; Borne, H.W. van den; Berge-Henegouwen, G.P. van

1996-01-01

Self-efficacy, one's expectations regarding the ability to perform some specific task, was studied prospectively in the adaptation process of stoma patients. One week after surgery, stoma-related self-efficacy was assessed in 59 patients (26 cancer patients and 33 patients with benign diseases) who

Testing self-regulation interventions to increase walking using factorial randomized N-of-1 trials.

Science.gov (United States)

Sniehotta, Falko F; Presseau, Justin; Hobbs, Nicola; Araújo-Soares, Vera

2012-11-01

To investigate the suitability of N-of-1 randomized controlled trials (RCTs) as a means of testing the effectiveness of behavior change techniques based on self-regulation theory (goal setting and self-monitoring) for promoting walking in healthy adult volunteers. A series of N-of-1 RCTs in 10 normal and overweight adults ages 19-67 (M = 36.9 years). We randomly allocated 60 days within each individual to text message-prompted daily goal-setting and/or self-monitoring interventions in accordance with a 2 (step-count goal prompt vs. alternative goal prompt) × 2 (self-monitoring: open vs. blinded Omron-HJ-113-E pedometer) factorial design. Aggregated data were analyzed using random intercept multilevel models. Single cases were analyzed individually. The primary outcome was daily pedometer step counts over 60 days. Single-case analyses showed that 4 participants significantly increased walking: 2 on self-monitoring days and 2 on goal-setting days, compared with control days. Six participants did not benefit from the interventions. In aggregated analyses, mean step counts were higher on goal-setting days (8,499.9 vs. 7,956.3) and on self-monitoring days (8,630.3 vs. 7,825.9). Multilevel analyses showed a significant effect of the self-monitoring condition (p = .01), the goal-setting condition approached significance (p = .08), and there was a small linear increase in walking over time (p = .03). N-of-1 randomized trials are a suitable means to test behavioral interventions in individual participants.

Robust Switching Control and Subspace Identification for Flutter of Flexible Wing

Directory of Open Access Journals (Sweden)

Yizhe Wang

2018-01-01

Full Text Available Active flutter suppression and subspace identification for a flexible wing model using micro fiber composite actuator were experimentally studied in a low speed wind tunnel. NACA0006 thin airfoil model was used for the experimental object to verify the performance of identification algorithm and designed controller. The equation of the fluid, vibration, and piezoelectric coupled motion was theoretically analyzed and experimentally identified under the open-loop and closed-loop condition by subspace method for controller design. A robust pole placement algorithm in terms of linear matrix inequality that accommodates the model uncertainty caused by identification deviation and flow speed variation was utilized to stabilize the divergent aeroelastic system. For further enlarging the flutter envelope, additional controllers were designed subject to the models beyond the flutter speed. Wind speed was measured online as the decision parameter of switching between the controllers. To ensure the stability of arbitrary switching, Common Lyapunov function method was applied to design the robust pole placement controllers for different models to ensure that the closed-loop system shared a common Lyapunov function. Wind tunnel result showed that the designed controllers could stabilize the time varying aeroelastic system over a wide range under arbitrary switching.

An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

Directory of Open Access Journals (Sweden)

Xiangchun Yu

2018-01-01

Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

Structural damage diagnosis based on on-line recursive stochastic subspace identification

International Nuclear Information System (INIS)

Loh, Chin-Hsiung; Weng, Jian-Huang; Liu, Yi-Cheng; Lin, Pei-Yang; Huang, Shieh-Kung

2011-01-01

This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted

Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.

Science.gov (United States)

Holliday, Jason A; Wang, Tongli; Aitken, Sally

2012-09-01

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm 'Random Forest' to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits--autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

DEFF Research Database (Denmark)

Hansen, Per Christian; Jensen, Søren Holdt

2007-01-01

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... with working Matlab code and applications in speech processing....

Goal orientation and work role performance: predicting adaptive and proactive work role performance through self-leadership strategies.

Science.gov (United States)

Marques-Quinteiro, Pedro; Curral, Luís Alberto

2012-01-01

This article explores the relationship between goal orientation, self-leadership dimensions, and adaptive and proactive work role performances. The authors hypothesize that learning orientation, in contrast to performance orientation, positively predicts proactive and adaptive work role performances and that this relationship is mediated by self-leadership behavior-focused strategies. It is posited that self-leadership natural reward strategies and thought pattern strategies are expected to moderate this relationship. Workers (N = 108) from a software company participated in this study. As expected, learning orientation did predict adaptive and proactive work role performance. Moreover, in the relationship between learning orientation and proactive work role performance through self-leadership behavior-focused strategies, a moderated mediation effect was found for self-leadership natural reward and thought pattern strategies. In the end, the authors discuss the results and implications are discussed and future research directions are proposed.

Pediatric Basic Life Support Self-training is Comparable to Instructor-led Training: A randomized manikin study

DEFF Research Database (Denmark)

Vestergaard, L. D.; Løfgren, Bo; Jessen, C.

2011-01-01

Pediatric Basic Life Support Self-training is comparable to Instructor-led Training: A randomized manikin study.......Pediatric Basic Life Support Self-training is comparable to Instructor-led Training: A randomized manikin study....

A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

Directory of Open Access Journals (Sweden)

Li Liu

2015-01-01

we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

Self-Adaptive Event-Driven Simulation of Multi-Scale Plasma Systems

Science.gov (United States)

Omelchenko, Yuri; Karimabadi, Homayoun

2005-10-01

Multi-scale plasmas pose a formidable computational challenge. The explicit time-stepping models suffer from the global CFL restriction. Efficient application of adaptive mesh refinement (AMR) to systems with irregular dynamics (e.g. turbulence, diffusion-convection-reaction, particle acceleration etc.) may be problematic. To address these issues, we developed an alternative approach to time stepping: self-adaptive discrete-event simulation (DES). DES has origin in operations research, war games and telecommunications. We combine finite-difference and particle-in-cell techniques with this methodology by assuming two caveats: (1) a local time increment, dt for a discrete quantity f can be expressed in terms of a physically meaningful quantum value, df; (2) f is considered to be modified only when its change exceeds df. Event-driven time integration is self-adaptive as it makes use of causality rules rather than parametric time dependencies. This technique enables asynchronous flux-conservative update of solution in accordance with local temporal scales, removes the curse of the global CFL condition, eliminates unnecessary computation in inactive spatial regions and results in robust and fast parallelizable codes. It can be naturally combined with various mesh refinement techniques. We discuss applications of this novel technology to diffusion-convection-reaction systems and hybrid simulations of magnetosonic shocks.

Banach C*-algebras not containing a subspace isomorphic to C0

International Nuclear Information System (INIS)

Basit, B.

1989-09-01

If X is a locally Hausdorff space and C 0 (X) the Banach algebra of continuous functions defined on X vanishing at infinity, we showed that a subalgebra A of C 0 (X) is finite dimensional if it does not contain a subspace isomorphic to the Banach space C 0 of convergent to zero complex sequences. In this paper we extend this result to noncommutative Banach C*-algebras and Banach* algebras. 10 refs

Perturbation for Frames for a Subspace of a Hilbert Space

DEFF Research Database (Denmark)

Christensen, Ole; deFlicht, C.; Lennard, C.

1997-01-01

We extend a classical result stating that a sufficiently small perturbation$\\{ g_i \\}$ of a Riesz sequence $\\{ f_i \\}$ in a Hilbert space $H$ is again a Riesz sequence. It turns out that the analog result for a frame does not holdunless the frame is complete. However, we are able to prove a very...... similarresult for frames in the case where the gap between the subspaces$\\overline{span} \\{f_i \\}$ and $\\overline{span} \\{ g_i \\}$ is small enough. We give a geometric interpretation of the result....

Behavioral self-regulation for weight loss in young adults: a randomized controlled trial

Directory of Open Access Journals (Sweden)

Wing Rena R

2009-02-01

Full Text Available Abstract Objective To determine the feasibility of recruiting and retaining young adults in a brief behavioral weight loss intervention tailored for this age group, and to assess the preliminary efficacy of an intervention that emphasizes daily self-weighing within the context of a self-regulation model. Methods Forty young adults (29.1 ± 3.9 years, range 21–35, average BMI of 33.36 ± 3.4 were randomized to one of two brief behavioral weight loss interventions: behavioral self-regulation (BSR or adapted standard behavioral treatment (SBT. Assessments were conducted at baseline, post-treatment (10 weeks, and follow-up (20 weeks. Intent to treat analyses were conducted using general linear modeling in SPSS version 14.0. Results Participants in both groups attended an average of 8.7 out of 10 group meetings, and retention rates were 93% and 88% for post-treatment and follow-up assessments, respectively. Both groups achieved significant weight losses at post-treatment (BSR = -6.4 kg (4.0; SBT = -6.2 kg (4.5 and follow-up (BSR = -6.6 kg (5.5; SBT = -5.8 kg (5.2, p p = .84. Across groups, there was a positive association between frequency of weighing at follow-up and overall weight change at follow-up (p = .01. Daily weighing was not associated with any adverse changes in psychological symptoms. Conclusion Young adults can be recruited and retained in a behavioral weight loss program tailored to their needs, and significant weight losses can be achieved and maintained through this brief intervention. Future research on the longer-term efficacy of a self-regulation approach using daily self-weighing for weight loss in this age group is warranted. Clinical Trials Registration # NCT00488228

Self-medication as adaptive plasticity: increased ingestion of plant toxins by parasitized caterpillars.

Directory of Open Access Journals (Sweden)

Michael S Singer

Full Text Available Self-medication is a specific therapeutic behavioral change in response to disease or parasitism. The empirical literature on self-medication has so far focused entirely on identifying cases of self-medication in which particular behaviors are linked to therapeutic outcomes. In this study, we frame self-medication in the broader realm of adaptive plasticity, which provides several testable predictions for verifying self-medication and advancing its conceptual significance. First, self-medication behavior should improve the fitness of animals infected by parasites or pathogens. Second, self-medication behavior in the absence of infection should decrease fitness. Third, infection should induce self-medication behavior. The few rigorous studies of self-medication in non-human animals have not used this theoretical framework and thus have not tested fitness costs of self-medication in the absence of disease or parasitism. Here we use manipulative experiments to test these predictions with the foraging behavior of woolly bear caterpillars (Grammia incorrupta; Lepidoptera: Arctiidae in response to their lethal endoparasites (tachinid flies. Our experiments show that the ingestion of plant toxins called pyrrolizidine alkaloids improves the survival of parasitized caterpillars by conferring resistance against tachinid flies. Consistent with theoretical prediction, excessive ingestion of these toxins reduces the survival of unparasitized caterpillars. Parasitized caterpillars are more likely than unparasitized caterpillars to specifically ingest large amounts of pyrrolizidine alkaloids. This case challenges the conventional view that self-medication behavior is restricted to animals with advanced cognitive abilities, such as primates, and empowers the science of self-medication by placing it in the domain of adaptive plasticity theory.

Development of Subspace-based Hybrid Monte Carlo-Deterministric Algorithms for Reactor Physics Calculations

International Nuclear Information System (INIS)

Abdel-Khalik, Hany S.; Zhang, Qiong

2014-01-01

The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calculations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 10 3 - 10 5 times to properly characterize the few-group cross-sections for downstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the favorable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.

Random lasing actions in self-assembled perovskite nanoparticles

Science.gov (United States)

Liu, Shuai; Sun, Wenzhao; Li, Jiankai; Gu, Zhiyuan; Wang, Kaiyang; Xiao, Shumin; Song, Qinghai

2016-05-01

Solution-based perovskite nanoparticles have been intensively studied in the past few years due to their applications in both photovoltaic and optoelectronic devices. Here, based on the common ground between solution-based perovskite and random lasers, we have studied the mirrorless lasing actions in self-assembled perovskite nanoparticles. After synthesis from a solution, discrete lasing peaks have been observed from optically pumped perovskites without any well-defined cavity boundaries. We have demonstrated that the origin of the random lasing emissions is the scattering between the nanostructures in the perovskite microplates. The obtained quality (Q) factors and thresholds of random lasers are around 500 and 60 μJ/cm2, respectively. Both values are comparable to the conventional perovskite microdisk lasers with polygon-shaped cavity boundaries. From the corresponding studies on laser spectra and fluorescence microscope images, the lasing actions are considered random lasers that are generated by strong multiple scattering in random gain media. In additional to conventional single-photon excitation, due to the strong nonlinear effects of perovskites, two-photon pumped random lasers have also been demonstrated for the first time. We believe this research will find its potential applications in low-cost coherent light sources and biomedical detection.

Walking adaptability therapy after stroke: study protocol for a randomized controlled trial.

Science.gov (United States)

Timmermans, Celine; Roerdink, Melvyn; van Ooijen, Marielle W; Meskers, Carel G; Janssen, Thomas W; Beek, Peter J

2016-08-26

Walking in everyday life requires the ability to adapt walking to the environment. This adaptability is often impaired after stroke, and this might contribute to the increased fall risk after stroke. To improve safe community ambulation, walking adaptability training might be beneficial after stroke. This study is designed to compare the effects of two interventions for improving walking speed and walking adaptability: treadmill-based C-Mill therapy (therapy with augmented reality) and the overground FALLS program (a conventional therapy program). We hypothesize that C-Mill therapy will result in better outcomes than the FALLS program, owing to its expected greater amount of walking practice. This is a single-center parallel group randomized controlled trial with pre-intervention, post-intervention, retention, and follow-up tests. Forty persons after stroke (≥3 months) with deficits in walking or balance will be included. Participants will be randomly allocated to either C-Mill therapy or the overground FALLS program for 5 weeks. Both interventions will incorporate practice of walking adaptability and will be matched in terms of frequency, duration, and therapist attention. Walking speed, as determined by the 10 Meter Walking Test, will be the primary outcome measure. Secondary outcome measures will pertain to walking adaptability (10 Meter Walking Test with context or cognitive dual-task and Interactive Walkway assessments). Furthermore, commonly used clinical measures to determine walking ability (Timed Up-and-Go test), walking independence (Functional Ambulation Category), balance (Berg Balance Scale), and balance confidence (Activities-specific Balance Confidence scale) will be used, as well as a complementary set of walking-related assessments. The amount of walking practice (the number of steps taken per session) will be registered using the treadmill's inbuilt step counter (C-Mill therapy) and video recordings (FALLS program). This process measure will

A Self Adaptive Differential Evolution Algorithm for Global Optimization

Science.gov (United States)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

Effect of an Empowerment Program on Self-Efficacy of Epileptic Child's Mothers in Psychological Adaptation, Gaining Support and Receiving Information

Directory of Open Access Journals (Sweden)

S. Gholami

2016-09-01

Full Text Available Aims: Epilepsy is one of the most prevalent childhood neurological disorders. As the primary caregivers, the mothers of epileptic children undergo different psychological pressures. The aim of the study was to investigate the effects of empowerment on the self-efficacy of the mothers of the epileptic children, concerning psychological adaptation, gaining support, and receiving information. Materials & Methods: In the controlled two-group random clinical trial with pretest and posttest steps, 100 mothers of epileptic children hospitalized in the Neurology Ward of Ghaem Hospital of Mashhad were studied in 2014. The subjects, selected via convenience sampling method, were randomly divided into two groups including experimental (n=50 and control (n=50 groups. Data was collected using the caregiver’s self-efficacy questionnaire. Only experimental group received the empowerment program, and no intervention was conducted in control group. The mothers’ self-efficacy was measured before and after the intervention in both groups. Data was analyzed by SPSS 11.5 software using independent T, paired T, Chi-square, Fisher’s exact, and covariance tests. Findings: The mean scores of self-efficacy, including psychological adjustment, gain a support, and receiving information, were not significantly different between the groups before the intervention (p>0.05. Nevertheless, the groups were significantly different after the intervention (p<0.001. In addition, the mean score after the intervention in experimental group was significantly higher than the score in the same group before the intervention (p<0.001. Conclusion: The empowerment program enhances the self-efficacy of the mothers of the epileptic children in psychological adjustment, gain a support, and receiving information.

Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing

Science.gov (United States)

Rowe, C. A.; Stead, R. J.; Begnaud, M. L.

2013-12-01

Because of the great resolving power of seismic arrays, the application of automated processing to array data is critically important in treaty verification work. A significant problem in array analysis is the inclusion of bad sensor channels in the beamforming process. We are testing an approach to automated, on-the-fly quality control (QC) to aid in the identification of poorly performing sensor channels prior to beam-forming in routine event detection or location processing. The idea stems from methods used for large computer servers, when monitoring traffic at enormous numbers of nodes is impractical on a node-by node basis, so the dimensionality of the node traffic is instead monitoried for anomalies that could represent malware, cyber-attacks or other problems. The technique relies upon the use of subspace dimensionality or principal components of the overall system traffic. The subspace technique is not new to seismology, but its most common application has been limited to comparing waveforms to an a priori collection of templates for detecting highly similar events in a swarm or seismic cluster. In the established template application, a detector functions in a manner analogous to waveform cross-correlation, applying a statistical test to assess the similarity of the incoming data stream to known templates for events of interest. In our approach, we seek not to detect matching signals, but instead, we examine the signal subspace dimensionality in much the same way that the method addresses node traffic anomalies in large computer systems. Signal anomalies recorded on seismic arrays affect the dimensional structure of the array-wide time-series. We have shown previously that this observation is useful in identifying real seismic events, either by looking at the raw signal or derivatives thereof (entropy, kurtosis), but here we explore the effects of malfunctioning channels on the dimension of the data and its derivatives, and how to leverage this effect for

Directed self-avoiding walks in random media

International Nuclear Information System (INIS)

Santra, S. B.; Seitz, W. A.; Klein, D. J.

2001-01-01

Two types of directed self-avoiding walks (SAW's), namely, three-choice directed SAW and outwardly directed SAW, have been studied on infinite percolation clusters on the square lattice in two dimensions. The walks on the percolation clusters are generated via a Monte Carlo technique. The longitudinal extension R N and the transverse fluctuation W N have been measured as a function of the number of steps N. Slight swelling is observed in the longitudinal direction on the random lattices. A crossover from shrinking to swelling of the transverse fluctuations is found at a certain length N c of the walks. The exponents related to the transverse fluctuations are seen to be unchanged in the random media even as the percolation threshold is reached. The scaling function form of the extensions are verified

Life goal attainment in the adaptation process after acquired brain injury: the influence of self-efficacy and of flexibility and tenacity in goal pursuit.

Science.gov (United States)

Brands, Ingrid; Stapert, Sven; Köhler, Sebastian; Wade, Derick; van Heugten, Caroline

2015-06-01

To investigate attainment of important life goals and to examine whether self-efficacy, tenacity in goal pursuit and flexibility in goal adjustment contribute to adaptation by affecting levels of emotional distress and quality of life in patients with newly acquired brain injury. Data were collected from a prospective clinical cohort study of 148 patients assessed after discharge home (mean time since injury = 15 weeks) and one year later. At follow-up, attainment of life goals (set at baseline) and satisfaction with attainment was scored (10-point scale) and patients were asked how they adjusted unattained goals. Emotional distress was measured with the Hospital Anxiety and Depression Scale (HADS), quality of life with the Life Satisfaction Questionnaire (LiSat-9), self-efficacy with the TBI Self-efficacy Questionnaire (SEsx) and tenacity and flexibility with the Assimilative/Accommodative Coping Questionnaire (AACQ). Random effects regression analyses and structural equation modelling were used. In total, only 13 % of initial life goals were achieved in one year. Patients who maintained efforts to reach their original goals had higher average levels of tenacity, but did not differ in level of self-efficacy compared with patients that disengaged. Patients with higher self-efficacy were more successful in attaining important life goals, which correlated with higher quality of life. Patients with higher self-efficacy, higher tenacity in goal pursuit, and higher flexibility in goal adjustment were less emotionally distressed, again correlating with higher quality of life. To optimise adaptation it seems appropriate to promote self-efficacy and both tenacity and flexibility during rehabilitation treatment. © The Author(s) 2014.

Adapting the Revised Self-Leadership Questionnaire to the Portuguese Context

Science.gov (United States)

Marques-Quinteiro, Pedro; Curral, Luis Alberto; Passos, Ana Margarida

2012-01-01

This study aimed to adapt the Revised Self-Leadership Questionnaire (RSLQ) (Houghton and Neck in J Manag Psychol 17(8):672-691, 2002) for the Portuguese population. 720 professionals, and university and post-graduate students participated in this study. The RSLQ factorial structure was accessed through exploratory and multi group confirmatory…

A randomized controlled trial of culturally adapted motivational interviewing for Hispanic heavy drinkers: Theory of Adaptation and Study Protocol

Science.gov (United States)

Lee, Christina S.; Colby, Suzanne M.; Magill, Molly; Almeida, Joanna; Tavares, Tonya; Rohsenow, Damaris J.

2016-01-01

Background The NIH Strategic Plan prioritizes health disparities research for socially disadvantaged Hispanics, to reduce the disproportionate burden of alcohol-related negative consequences compared to other racial/ethnic groups. Cultural adaptation of evidence-based treatments, such as motivational interviewing (MI), can improve access and response to alcohol treatment. However, the lack of rigorous clinical trials designed to test the efficacy and theoretical underpinnings of cultural adaptation has made proof of concept difficult. Objective The CAMI2 (Culturally Adapted Motivational Interviewing) study design and its theoretical model, is described to illustrate how MI adapted to social and cultural factors (CAMI) can be discriminated against non-adapted MI. Methods and Design CAMI2, a large, 12 month randomized prospective trial, examines the efficacy of CAMI and MI among heavy drinking Hispanics recruited from the community (n=257). Outcomes are reductions in heavy drinking days (Time Line Follow-Back) and negative consequences of drinking among Hispanics (Drinkers Inventory of Consequences). A second aim examines perceived acculturation stress as a moderator of treatment outcomes in the CAMI condition. Summary The CAMI2 study design protocol is presented and the theory of adaptation is presented. Findings from the trial described may yield important recommendations on the science of cultural adaptation and improve MI dissemination to Hispanics with alcohol risk. PMID:27565832

A block Krylov subspace time-exact solution method for linear ordinary differential equation systems

NARCIS (Netherlands)

Bochev, Mikhail A.

2013-01-01

We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of

Randomized Clinical Trial of a Self-Adhering Flowable Composite for Class I Restorations: 2-Year Results

Directory of Open Access Journals (Sweden)

J. Sabbagh

2017-01-01

Full Text Available Objectives. To compare the clinical performances of a self-adhering resin composite and a conventional flowable composite with a self-etch bonding system on permanent molars. The influence of using rubber dam versus cotton roll isolation was also investigated. Materials and Methods. Patients aged between 6 and 12 years and presenting at least two permanent molars in need of small class I restorations were selected. Thirty-four pairs of restorations were randomly placed by the same operator. Fifteen patients were treated under rubber dam and nineteen using cotton rolls isolation and saliva ejector. They were evaluated according to the modified USPHS criteria at baseline, 6 months, and 1 and 2 years by two independent evaluators. Results. All patients attended the two-year recall. For all measured variables, there was no significant difference between rubber dam and cotton after 2 years of restoration with Premise Flowable or Vertise Flow (p value > 0.05. The percentage of restorations scored alpha decreased significantly over time with Premise Flowable and Vertise Flow for marginal adaptation and surface texture as well as marginal discoloration while it did not vary significantly for color matching. After 2 years, Vertise Flow showed a similar behaviour to the Premise Flowable used with a self-adhesive resin system.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

Science.gov (United States)

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.

Science.gov (United States)

Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar; Gagoski, Borjan; Ye, Huihui; Ma, Dan; Jiang, Yun; Ellen Grant, P; Griswold, Mark A; Wald, Lawrence L

2018-02-01

This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T 1 , T 2 , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Self-Adaptive Step Firefly Algorithm

Directory of Open Access Journals (Sweden)

Shuhao Yu

2013-01-01

Full Text Available In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.

Domain Adaptation for Opinion Classification: A Self-Training Approach

Directory of Open Access Journals (Sweden)

Yu, Ning

2013-03-01

Full Text Available Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

Science.gov (United States)

Piastra, Marco

2013-05-01

Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

Activation and Self-Efficacy in a Randomized Trial of a Depression Self-Care Intervention.

Science.gov (United States)

McCusker, Jane; Lambert, Sylvie D; Cole, Martin G; Ciampi, Antonio; Strumpf, Erin; Freeman, Ellen E; Belzile, Eric

2016-12-01

In a sample of primary care participants with chronic physical conditions and comorbid depressive symptoms: to describe the cross-sectional and longitudinal associations of activation and self-efficacy with demographic, physical and mental health status, health behaviors, depression self-care, health care utilization, and use of self-care tools; and to examine the effects of a depression self-care coaching intervention on these two outcomes. Design/Study Setting. A secondary analysis of activation and self-efficacy data collected as part of a randomized trial to compare the effects of a telephone-based coached depression self-care intervention with a noncoached intervention. Activation (Patient Activation Measure) was measured at baseline and 6 months. Depression self-care self-efficacy was assessed at baseline, at 3 months, and at 6 months. In multivariable cross-sectional analyses (n = 215), activation and/or self-efficacy were associated with language, birthplace, better physical and mental health, individual exercise, specialist visits, and antidepressant nonuse. In longitudinal analyses (n = 158), an increase in activation was associated with increased medication adherence; an increase in self-efficacy was associated with use of cognitive self-care strategies and increases in social and solitary activities. There were significant improvements from baseline to 6 months in activation and self-efficacy scores both among coached and noncoached groups. The self-care coaching intervention did not affect 6-month activation or self-efficacy but was associated with quicker improvement in self-efficacy. Overall, the results for activation and self-efficacy were similar, although self-efficacy correlated more consistently than activation with depression-specific behaviors and was responsive to a depression self-care coaching intervention. © 2016 Society for Public Health Education.

A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we...

Video-feedback Intervention to promote Positive Parenting adapted to Autism (VIPP-AUTI): A randomized controlled trial.

Science.gov (United States)

Poslawsky, Irina E; Naber, Fabiënne Ba; Bakermans-Kranenburg, Marian J; van Daalen, Emma; van Engeland, Herman; van IJzendoorn, Marinus H

2015-07-01

In a randomized controlled trial, we evaluated the early intervention program Video-feedback Intervention to promote Positive Parenting adapted to Autism (VIPP-AUTI) with 78 primary caregivers and their child (16-61 months) with Autism Spectrum Disorder. VIPP-AUTI is a brief attachment-based intervention program, focusing on improving parent-child interaction and reducing the child's individual Autism Spectrum Disorder-related symptomatology in five home visits. VIPP-AUTI, as compared with usual care, demonstrated efficacy in reducing parental intrusiveness. Moreover, parents who received VIPP-AUTI showed increased feelings of self-efficacy in child rearing. No significant group differences were found on other aspects of parent-child interaction or on child play behavior. At 3-months follow-up, intervention effects were found on child-initiated joint attention skills, not mediated by intervention effects on parenting. Implementation of VIPP-AUTI in clinical practice is facilitated by the use of a detailed manual and a relatively brief training of interveners. © The Author(s) 2014.

Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation

Science.gov (United States)

Huepe, David; Roca, María; Salas, Natalia; Canales-Johnson, Andrés; Rivera-Rei, Álvaro A.; Zamorano, Leandro; Concepción, Aimée; Manes, Facundo; Ibañez, Agustín

2011-01-01

Background While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. Methodology/Principal Findings A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM) and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire), domestic abuse of adolescents (Conflict Tactic Scale), drug intake (ONUDD), self-esteem (Rosenberg's Self Esteem Scale) and the Perceived Mental Health Scale (Spanish adaptation). Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher. Conclusions/Significance Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts. PMID:21957464

Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation.

Science.gov (United States)

Huepe, David; Roca, María; Salas, Natalia; Canales-Johnson, Andrés; Rivera-Rei, Álvaro A; Zamorano, Leandro; Concepción, Aimée; Manes, Facundo; Ibañez, Agustín

2011-01-01

While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM) and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire), domestic abuse of adolescents (Conflict Tactic Scale), drug intake (ONUDD), self-esteem (Rosenberg's Self Esteem Scale) and the Perceived Mental Health Scale (Spanish adaptation). Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher. Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts.

Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation.

Directory of Open Access Journals (Sweden)

David Huepe

Full Text Available While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized.A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire, domestic abuse of adolescents (Conflict Tactic Scale, drug intake (ONUDD, self-esteem (Rosenberg's Self Esteem Scale and the Perceived Mental Health Scale (Spanish adaptation. Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher.Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts.

Gene selection for microarray data classification via subspace learning and manifold regularization.

Science.gov (United States)

Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

2017-12-19

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

Science.gov (United States)

Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

2017-06-01

A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

The adaptation of a Danish version of the Pain Self-Efficacy Questionnaire

DEFF Research Database (Denmark)

Rasmussen, M. U.; Rydahl-Hansen, Susan; Amris, K.

2016-01-01

The aim of this study was to translate, culturally adapt and evaluate the psychometric properties of the Pain Self-Efficacy Questionnaire (PSEQ) in a population of patients with fibromyalgia in Denmark. The study sample included 102 patients diagnosed with fibromyalgia referred to a specialist...... clinic. The PSEQ was translated and adapted to a Danish setting using a standard stepw-ise forward-backward translation procedure, followed by initial testing and focus group interview. Reliability was examined by analysing internal consistency and test-retest agreement. Construct validity was exami......-factor model and IRT models supported acceptable construct validity. The PSEQ-DK showed acceptable psychometric properties and can therefore represent a reliable and valid measure for evaluating self-efficacy in patients with fibromyalgia in Denmark. © 2016 Nordic College of Caring Science....

Adaptive Mean Queue Size and Its Rate of Change: Queue Management with Random Dropping

OpenAIRE

Karmeshu; Patel, Sanjeev; Bhatnagar, Shalabh

2016-01-01

The Random early detection (RED) active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping (AQMRD) algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of th...

Towards Static Analysis of Policy-Based Self-adaptive Computing Systems

DEFF Research Database (Denmark)

Margheri, Andrea; Nielson, Hanne Riis; Nielson, Flemming

2016-01-01

For supporting the design of self-adaptive computing systems, the PSCEL language offers a principled approach that relies on declarative definitions of adaptation and authorisation policies enforced at runtime. Policies permit managing system components by regulating their interactions...... and by dynamically introducing new actions to accomplish task-oriented goals. However, the runtime evaluation of policies and their effects on system components make the prediction of system behaviour challenging. In this paper, we introduce the construction of a flow graph that statically points out the policy...... evaluations that can take place at runtime and exploit it to analyse the effects of policy evaluations on the progress of system components....

Random unitary maps for quantum state reconstruction

International Nuclear Information System (INIS)

Merkel, Seth T.; Riofrio, Carlos A.; Deutsch, Ivan H.; Flammia, Steven T.

2010-01-01

We study the possibility of performing quantum state reconstruction from a measurement record that is obtained as a sequence of expectation values of a Hermitian operator evolving under repeated application of a single random unitary map, U 0 . We show that while this single-parameter orbit in operator space is not informationally complete, it can be used to yield surprisingly high-fidelity reconstruction. For a d-dimensional Hilbert space with the initial observable in su(d), the measurement record lacks information about a matrix subspace of dimension ≥d-2 out of the total dimension d 2 -1. We determine the conditions on U 0 such that the bound is saturated, and show they are achieved by almost all pseudorandom unitary matrices. When we further impose the constraint that the physical density matrix must be positive, we obtain even higher fidelity than that predicted from the missing subspace. With prior knowledge that the state is pure, the reconstruction will be perfect (in the limit of vanishing noise) and for arbitrary mixed states, the fidelity is over 0.96, even for small d, and reaching F>0.99 for d>9. We also study the implementation of this protocol based on the relationship between random matrices and quantum chaos. We show that the Floquet operator of the quantum kicked top provides a means of generating the required type of measurement record, with implications on the relationship between quantum chaos and information gain.

Study design and protocol for a culturally adapted cognitive behavioral stress and self-management intervention for localized prostate cancer: The Encuentros de Salud study.

Science.gov (United States)

Penedo, Frank J; Antoni, Michael H; Moreno, Patricia I; Traeger, Lara; Perdomo, Dolores; Dahn, Jason; Miller, Gregory E; Cole, Steve; Orjuela, Julian; Pizarro, Edgar; Yanez, Betina

2018-06-14

Almost 2.8 million men in the U.S. are living with prostate cancer (PC), accounting for 40% of all male cancer survivors. Men diagnosed with prostate cancer may experience chronic and debilitating treatment side effects, including sexual and urinary dysfunction, pain and fatigue. Side effects can be stressful and can also lead to poor psychosocial functioning. Prior trials reveal that group-based cognitive behavioral stress and self-management (CBSM) is effective in reducing stress and mitigating some of these symptoms, yet little is known about the effects of culturally-translated CBSM among Spanish-speaking men with PC. This manuscript describes the rationale and study design of a multi-site, randomized controlled trial to determine whether participation in a culturally adapted cognitive behavioral stress management (C-CBSM) intervention leads to significantly greater reductions in symptom burden and improvements in health-related quality of life relative to participation in a non-culturally adapted cognitive behavioral stress management (CBSM) intervention. Participants (N = 260) will be Spanish-speaking Hispanic/Latino men randomized to the standard, non-culturally adapted CBSM intervention (e.g., cognitive behavioral strategies, stress management, and health maintenance) or the culturally adapted C-CBSM intervention (e.g., content adapted to be compatible with Hispanic/Latino cultural patterns and belief systems, meanings, values and social context) for 10 weeks. Primary outcomes (i.e., disease-specific symptom burden and health-related quality of life) will be assessed across time. We hypothesize that a culturally adapted C-CBSM intervention will be more efficacious in reducing symptom burden and improving health-related quality of life among Hispanic/Latino men when compared to a non-culturally adapted CBSM intervention. Copyright © 2017. Published by Elsevier Inc.

High resolution through-the-wall radar image based on beamspace eigenstructure subspace methods

Science.gov (United States)

Yoon, Yeo-Sun; Amin, Moeness G.

2008-04-01

Through-the-wall imaging (TWI) is a challenging problem, even if the wall parameters and characteristics are known to the system operator. Proper target classification and correct imaging interpretation require the application of high resolution techniques using limited array size. In inverse synthetic aperture radar (ISAR), signal subspace methods such as Multiple Signal Classification (MUSIC) are used to obtain high resolution imaging. In this paper, we adopt signal subspace methods and apply them to the 2-D spectrum obtained from the delay-andsum beamforming image. This is in contrast to ISAR, where raw data, in frequency and angle, is directly used to form the estimate of the covariance matrix and array response vector. Using beams rather than raw data has two main advantages, namely, it improves the signal-to-noise ratio (SNR) and can correctly image typical indoor extended targets, such as tables and cabinets, as well as point targets. The paper presents both simulated and experimental results using synthesized and real data. It compares the performance of beam-space MUSIC and Capon beamformer. The experimental data is collected at the test facility in the Radar Imaging Laboratory, Villanova University.

Parallel algorithms for unconstrained optimization by multisplitting with inexact subspace search - the abstract

Energy Technology Data Exchange (ETDEWEB)

Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)

1994-12-31

In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.

Random access with adaptive packet aggregation in LTE/LTE-A.

Science.gov (United States)

Zhou, Kaijie; Nikaein, Navid

While random access presents a promising solution for efficient uplink channel access, the preamble collision rate can significantly increase when massive number of devices simultaneously access the channel. To address this issue and improve the reliability of the random access, an adaptive packet aggregation method is proposed. With the proposed method, a device does not trigger a random access for every single packet. Instead, it starts a random access when the number of aggregated packets reaches a given threshold. This method reduces the packet collision rate at the expense of an extra latency, which is used to accumulate multiple packets into a single transmission unit. Therefore, the tradeoff between packet loss rate and channel access latency has to be carefully selected. We use semi-Markov model to derive the packet loss rate and channel access latency as functions of packet aggregation number. Hence, the optimal amount of aggregated packets can be found, which keeps the loss rate below the desired value while minimizing the access latency. We also apply for the idea of packet aggregation for power saving, where a device aggregates as many packets as possible until the latency constraint is reached. Simulations are carried out to evaluate our methods. We find that the packet loss rate and/or power consumption are significantly reduced with the proposed method.

A chaos wolf optimization algorithm with self-adaptive variable step-size

Science.gov (United States)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Damage Detection in Bridge Structure Using Vibration Data under Random Travelling Vehicle Loads

International Nuclear Information System (INIS)

Loh, C H; Hung, T Y; Chen, S F; Hsu, W T

2015-01-01

Due to the random nature of the road excitation and the inherent uncertainties in bridge-vehicle system, damage identification of bridge structure through continuous monitoring under operating situation become a challenge problem. Methods for system identification and damage detection of a continuous two-span concrete bridge structure in time domain is presented using interaction forces from random moving vehicles as excitation. The signals recorded in different locations of the instrumented bridge are mixed with signals from different internal and external (road roughness) vibration sources. The damage structure is also modelled as the stiffness reduction in one of the beam element. For the purpose of system identification and damage detection three different output-only modal analysis techniques are proposed: The covariance-driven stochastic subspace identification (SSI-COV), the blind source separation algorithms (called Second Order Blind Identification) and the multivariate AR model. The advantages and disadvantages of the three algorithms are discussed. Finally, the null-space damage index, subspace damage indices and mode shape slope change are used to detect and locate the damage. The proposed approaches has been tested in simulation and proved to be effective for structural health monitoring. (paper)

A Self-adaptive Scope Allocation Scheme for Labeling Dynamic XML Documents

NARCIS (Netherlands)

Shen, Y.; Feng, L.; Shen, T.; Wang, B.

This paper proposes a self-adaptive scope allocation scheme for labeling dynamic XML documents. It is general, light-weight and can be built upon existing data retrieval mechanisms. Bayesian inference is used to compute the actual scope allocated for labeling a certain node based on both the prior

The Efficacy of Adapted MBCT on Core Symptoms and Executive Functioning in Adults With ADHD: A Preliminary Randomized Controlled Trial.

Science.gov (United States)

Hepark, Sevket; Janssen, Lotte; de Vries, Alicia; Schoenberg, Poppy L A; Donders, Rogier; Kan, Cornelis C; Speckens, Anne E M

2015-11-20

The aim of this study was to examine the effectiveness of mindfulness as a treatment for adults diagnosed with ADHD. A 12-week-adapted mindfulness-based cognitive therapy (MBCT) program is compared with a waiting list (WL) group. Adults with ADHD were randomly allocated to MBCT (n = 55) or waitlist (n = 48). Outcome measures included investigator-rated ADHD symptoms (primary), self-reported ADHD symptoms, executive functioning, depressive and anxiety symptoms, patient functioning, and mindfulness skills. MBCT resulted in a significant reduction of ADHD symptoms, both investigator-rated and self-reported, based on per-protocol and intention-to-treat analyses. Significant improvements in executive functioning and mindfulness skills were found. Additional analyses suggested that the efficacy of MBCT in reducing ADHD symptoms and improving executive functioning is partially mediated by an increase in the mindfulness skill "Act With Awareness." No improvements were observed for depressive and anxiety symptoms, and patient functioning. This study provides preliminary support for the effectiveness of MBCT for adults with ADHD. © The Author(s) 2015.

Sexual Orientation Self-Concept Ambiguity: Scale Adaptation and Validation.

Science.gov (United States)

Talley, Amelia E; Stevens, Jordan E

2017-07-01

The current article describes the adaptation of a measure of sexual orientation self-concept ambiguity (SSA) from an existing measure of general self-concept clarity. Latent "trait" scores of SSA reflect the extent to which a person's beliefs about their own sexual orientation are perceived as inconsistent, unreliable, or incongruent. Sexual minority and heterosexual women ( n = 348), ages 18 to 30, completed a cross-sectional survey. Categorical confirmatory factor analysis guided the selection of items to form a 10-item, self-report measure of SSA. In the current report, we also examine (a) reliability of the 10-item scale score, (b) measurement invariance based on respondents' sexual identity status and age group, and (c) correlations with preexisting surveys that purport to measure similar constructs and theoretical correlates. Evidence for internal reliability, measurement invariance (based on respondent sex), and convergent validity was also investigated in an independent, validation sample. The lowest SSA scores were reported by women who self-ascribed an exclusively heterosexual or exclusively lesbian/gay sexual identity, whereas those who reported a bisexual, mostly lesbian/gay, or mostly heterosexual identity, reported relatively higher SSA scores.

The self-adaptation to dynamic failures for efficient virtual organization formations in grid computing context

International Nuclear Information System (INIS)

Han Liangxiu

2009-01-01

Grid computing aims to enable 'resource sharing and coordinated problem solving in dynamic, multi-institutional virtual organizations (VOs)'. However, due to the nature of heterogeneous and dynamic resources, dynamic failures in the distributed grid environment usually occur more than in traditional computation platforms, which cause failed VO formations. In this paper, we develop a novel self-adaptive mechanism to dynamic failures during VO formations. Such a self-adaptive scheme allows an individual and member of VOs to automatically find other available or replaceable one once a failure happens and therefore makes systems automatically recover from dynamic failures. We define dynamic failure situations of a system by using two standard indicators: mean time between failures (MTBF) and mean time to recover (MTTR). We model both MTBF and MTTR as Poisson distributions. We investigate and analyze the efficiency of the proposed self-adaptation mechanism to dynamic failures by comparing the success probability of VO formations before and after adopting it in three different cases: (1) different failure situations; (2) different organizational structures and scales; (3) different task complexities. The experimental results show that the proposed scheme can automatically adapt to dynamic failures and effectively improve the dynamic VO formation performance in the event of node failures, which provide a valuable addition to the field.

A Randomized trial of an Asthma Internet Self-management Intervention (RAISIN): study protocol for a randomized controlled trial.

Science.gov (United States)

Morrison, Deborah; Wyke, Sally; Thomson, Neil C; McConnachie, Alex; Agur, Karolina; Saunderson, Kathryn; Chaudhuri, Rekha; Mair, Frances S

2014-05-24

The financial costs associated with asthma care continue to increase while care remains suboptimal. Promoting optimal self-management, including the use of asthma action plans, along with regular health professional review has been shown to be an effective strategy and is recommended in asthma guidelines internationally. Despite evidence of benefit, guided self-management remains underused, however the potential for online resources to promote self-management behaviors is gaining increasing recognition. The aim of this paper is to describe the protocol for a pilot evaluation of a website 'Living well with asthma' which has been developed with the aim of promoting self-management behaviors shown to improve outcomes. The study is a parallel randomized controlled trial, where adults with asthma are randomly assigned to either access to the website for 12 weeks, or usual asthma care for 12 weeks (followed by access to the website if desired). Individuals are included if they are over 16-years-old, have a diagnosis of asthma with an Asthma Control Questionnaire (ACQ) score of greater than, or equal to 1, and have access to the internet. Primary outcomes for this evaluation include recruitment and retention rates, changes at 12 weeks from baseline for both ACQ and Asthma Quality of Life Questionnaire (AQLQ) scores, and quantitative data describing website usage (number of times logged on, length of time logged on, number of times individual pages looked at, and for how long). Secondary outcomes include clinical outcomes (medication use, health services use, lung function) and patient reported outcomes (including adherence, patient activation measures, and health status). Piloting of complex interventions is considered best practice and will maximise the potential of any future large-scale randomized controlled trial to successfully recruit and be able to report on necessary outcomes. Here we will provide results across a range of outcomes which will provide estimates of

QoS-aware self-adaptation of communication protocols in a pervasive service middleware

DEFF Research Database (Denmark)

Zhang, Weishan; Hansen, Klaus Marius; Fernandes, João

2010-01-01

Pervasive computing is characterized by heterogeneous devices that usually have scarce resources requiring optimized usage. These devices may use different communication protocols which can be switched at runtime. As different communication protocols have different quality of service (Qo......S) properties, this motivates optimized self-adaption of protocols for devices, e.g., considering power consumption and other QoS requirements, e.g. round trip time (RTT) for service invocations, throughput, and reliability. In this paper, we present an extensible approach for self-adaptation of communication...... protocols for pervasive web services, where protocols are designed as reusable connectors and our middleware infrastructure can hide the complexity of using different communication protocols to upper layers. We also propose to use Genetic Algorithms (GAs) to find optimized configurations at runtime...

D Semantic Labeling of ALS Data Based on Domain Adaption by Transferring and Fusing Random Forest Models

Science.gov (United States)

Wu, J.; Yao, W.; Zhang, J.; Li, Y.

2018-04-01

Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

International Nuclear Information System (INIS)

Xiao Yunhai; Hu Qingjie

2008-01-01

An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

Experimental Study of Generalized Subspace Filters for the Cocktail Party Situation

DEFF Research Database (Denmark)

Christensen, Knud Bank; Christensen, Mads Græsbøll; Boldt, Jesper B.

2016-01-01

This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/ distortion ratio, speech quality rating and speech...... intelligibility rating are mapped as functions of two algorithm parameters, revealing clear trade-off options between noise, distortion and subjective performances and a recommended choice of trade-off. Given sufficiently good noise statistics, SNR improvements around 20 dB as well as PESQ quality and STOI...

Practical Low Data-Complexity Subspace-Trail Cryptanalysis of Round-Reduced PRINCE

DEFF Research Database (Denmark)

Grassi, Lorenzo; Rechberger, Christian

2016-01-01

Subspace trail cryptanalysis is a very recent new cryptanalysis technique, and includes differential, truncated differential, impossible differential, and integral attacks as special cases. In this paper, we consider PRINCE, a widely analyzed block cipher proposed in 2012. After the identification......-plaintext category. The attacks have been verified using a C implementation. Of independent interest, we consider a variant of PRINCE in which ShiftRows and MixLayer operations are exchanged in position. In particular, our result shows that the position of ShiftRows and MixLayer operations influences the security...

Extension and customization of self-stability control in compliant legged systems

International Nuclear Information System (INIS)

Ernst, M; Blickhan, R; Geyer, H

2012-01-01

Several recent studies on the control of legged locomotion in animal and robot running focus on the influence of different leg parameters on gait stability. In a preceding investigation self-stability controls showing deadbeat behavior could be obtained by studying the dynamics of the system in dependence of the leg orientation carefully adjusted during the flight phase. Such controls allow to accommodate disturbances of the ground level without having to detect them. Here we further this method in two ways. Besides the leg orientation, we allow changes in leg stiffness during flight and show that this extension substantially improves the rejection of ground disturbances. In a human like example the tolerance of random variation in ground level over many steps increased from 3.5% to 35% of leg length. In single steps changes of about 70% leg length (either up or down) could be negotiated. The variable leg stiffness not only allows to start with flat leg orientations maximizing step tolerances but also increase the control subspace. This allows to customize self-stability controls and to consider physical and technical limitations found in animals and robots. (paper)

Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions

Science.gov (United States)

Klasnja, Predrag; Hekler, Eric B.; Shiffman, Saul; Boruvka, Audrey; Almirall, Daniel; Tewari, Ambuj; Murphy, Susan A.

2015-01-01

Objective This paper presents an experimental design, the micro-randomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals’ health behaviors. Micro-randomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. Methods The paper describes the micro-randomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Results Micro-randomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Conclusions Micro-randomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions’ effects, enabling creation of more effective JITAIs. PMID:26651463

Random projections and the optimization of an algorithm for phase retrieval

International Nuclear Information System (INIS)

Elser, Veit

2003-01-01

Iterative phase retrieval algorithms typically employ projections onto constraint subspaces to recover the unknown phases in the Fourier transform of an image, or, in the case of x-ray crystallography, the electron density of a molecule. For a general class of algorithms, where the basic iteration is specified by the difference map, solutions are associated with fixed points of the map, the attractive character of which determines the effectiveness of the algorithm. The behaviour of the difference map near fixed points is controlled by the relative orientation of the tangent spaces of the two constraint subspaces employed by the map. Since the dimensionalities involved are always large in practical applications, it is appropriate to use random matrix theory ideas to analyse the average-case convergence at fixed points. Optimal values of the γ parameters of the difference map are found which differ somewhat from the values previously obtained on the assumption of orthogonal tangent spaces

On Optimal Short Recurrences for Generating Orthogonal Krylov Subspace Bases. Dedicated to Gene Golub

Czech Academy of Sciences Publication Activity Database

Liesen, J.; Strakoš, Zdeněk

2008-01-01

Roč. 50, č. 3 (2008), s. 485-503 ISSN 0036-1445 R&D Projects: GA AV ČR 1ET400300415; GA AV ČR IAA100300802 Institutional research plan: CEZ:AV0Z10300504 Keywords : Krylov subspace methods * orthogonal bases * short reccurences * conjugate gradient -like methods Subject RIV: IN - Informatics, Computer Science Impact factor: 2.739, year: 2008

Parents' Self-Efficacy Beliefs and Their Children's Psychosocial Adaptation during Adolescence

Science.gov (United States)

Steca, Patrizia; Bassi, Marta; Caprara, Gian Vittorio; Fave, Antonella Delle

2011-01-01

Research has shown that parents' perceived parental self-efficacy (PSE) plays a pivotal role in promoting their children's successful adjustment. In this study, we further explored this issue by comparing psychosocial adaptation in children of parents with high and low PSE during adolescence. One hundred and thirty Italian teenagers (55 males and…

Fast Adaptive Blind MMSE Equalizer for Multichannel FIR Systems

Directory of Open Access Journals (Sweden)

Abed-Meraim Karim

2006-01-01

Full Text Available We propose a new blind minimum mean square error (MMSE equalization algorithm of noisy multichannel finite impulse response (FIR systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from to , where is the number of emitted signals, the data vector length, and the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.

Dynamic Self-Adaptive Reliability Control for Electric-Hydraulic Systems

Directory of Open Access Journals (Sweden)

Yi Wan

2015-02-01

Full Text Available The high-speed electric-hydraulic proportional control is a new development of the hydraulic control technique with high reliability, low cost, efficient energy, and easy maintenance; it is widely used in industrial manufacturing and production. However, there are still some unresolved challenges, the most notable being the requirements of high stability and real-time by the classical control algorithm due to its high nonlinear characteristics. We propose a dynamic self-adaptive mixed control method based on the least squares support vector machine (LSSVM and the genetic algorithm for high-speed electric-hydraulic proportional control systems in this paper; LSSVM is used to identify and adjust online a nonlinear electric-hydraulic proportional system, and the genetic algorithm is used to optimize the control law of the controlled system and dynamic self-adaptive internal model control and predictive control are implemented by using the mixed intelligent method. The internal model and the inverse control model are online adjusted together. At the same time, a time-dependent Hankel matrix is constructed based on sample data; thus finite dimensional solution can be optimized on finite dimensional space. The results of simulation experiments show that the dynamic characteristics are greatly improved by the mixed intelligent control strategy, and good tracking and high stability are met in condition of high frequency response.

Effects of a Culture-Adaptive Forgiveness Intervention for Chinese College Students

Science.gov (United States)

Ji, Mingxia; Hui, Eadaoin; Fu, Hong; Watkins, David; Tao, Linjin; Lo, Sing Kai

2016-01-01

The understanding and application of forgiveness varies across cultures. The current study aimed to examine the effect of a culture-adaptive Forgiveness Intervention on forgiveness attitude, self-esteem, empathy and anxiety of Mainland Chinese college students. Thirty-six participants were randomly allocated to either experimental groups or a…

Prevention of: self harm in British South Asian women: study protocol of an exploratory RCT of culturally adapted manual assisted Problem Solving Training (C- MAP

Directory of Open Access Journals (Sweden)

Nagaraj Diwaker

2011-06-01

Full Text Available Abstract Background Suicide is a major public health problem worldwide. In the UK suicide is the second most common cause of death in people aged 15-24 years. Self harm is one of the commonest reasons for medical admission in the UK. In the year following a suicide attempt the risk of a repeat attempt or death by suicide may be up to 100 times greater than in people who have never attempted suicide. Research evidence shows increased risk of suicide and attempted suicide among British South Asian women. There are concerns about the current service provision and its appropriateness for this community due to the low numbers that get involved with the services. Both problem solving and interpersonal forms of psychotherapy are beneficial in the treatment of patients who self harm and could potentially be helpful in this ethnic group. The paper describes the trial protocol of adapting and evaluating a culturally appropriate psychological treatment for the adult British South Asian women who self harm. Methods We plan to test a culturally adapted Problem Solving Therapy (C- MAP in British South Asian women who self harm. Eight sessions of problem solving each lasting approximately 50 minutes will be delivered over 3 months. The intervention will be assessed using a prospective rater blind randomized controlled design comparing with treatment as usual (TAU. Outcome assessments will be carried out at 3 and 6 months. A sub group of the participants will be invited for qualitative interviews. Discussion This study will test the feasibility and acceptability of the C- MAP in British South Asian women. We will be informed on whether a culturally adapted brief psychological intervention compared with treatment as usual for self-harm results in decreased hopelessness and suicidal ideation. This will also enable us to collect necessary information on recruitment, effect size, the optimal delivery method and acceptability of the intervention in preparation for a

Prevention of: self harm in British South Asian women: study protocol of an exploratory RCT of culturally adapted manual assisted Problem Solving Training (C- MAP).

Science.gov (United States)

Husain, Nusrat; Chaudhry, Nasim; Durairaj, Steevart V; Chaudhry, Imran; Khan, Sarah; Husain, Meher; Nagaraj, Diwaker; Naeem, Farooq; Waheed, Waquas

2011-06-21

Suicide is a major public health problem worldwide. In the UK suicide is the second most common cause of death in people aged 15-24 years. Self harm is one of the commonest reasons for medical admission in the UK. In the year following a suicide attempt the risk of a repeat attempt or death by suicide may be up to 100 times greater than in people who have never attempted suicide. Research evidence shows increased risk of suicide and attempted suicide among British South Asian women. There are concerns about the current service provision and its appropriateness for this community due to the low numbers that get involved with the services. Both problem solving and interpersonal forms of psychotherapy are beneficial in the treatment of patients who self harm and could potentially be helpful in this ethnic group.The paper describes the trial protocol of adapting and evaluating a culturally appropriate psychological treatment for the adult British South Asian women who self harm. We plan to test a culturally adapted Problem Solving Therapy (C- MAP) in British South Asian women who self harm. Eight sessions of problem solving each lasting approximately 50 minutes will be delivered over 3 months. The intervention will be assessed using a prospective rater blind randomized controlled design comparing with treatment as usual (TAU). Outcome assessments will be carried out at 3 and 6 months. A sub group of the participants will be invited for qualitative interviews. This study will test the feasibility and acceptability of the C- MAP in British South Asian women. We will be informed on whether a culturally adapted brief psychological intervention compared with treatment as usual for self-harm results in decreased hopelessness and suicidal ideation. This will also enable us to collect necessary information on recruitment, effect size, the optimal delivery method and acceptability of the intervention in preparation for a definitive RCT using repetition of self harm and cost

A chaos wolf optimization algorithm with self-adaptive variable step-size

Directory of Open Access Journals (Sweden)

Yong Zhu

2017-10-01

Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

Science.gov (United States)

Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

2014-01-01

Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

Extending and implementing the Self-adaptive Virtual Processor for distributed memory architectures

NARCIS (Netherlands)

van Tol, M.W.; Koivisto, J.

2011-01-01

Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current

Single and multiple object tracking using log-euclidean Riemannian subspace and block-division appearance model.

Science.gov (United States)

Hu, Weiming; Li, Xi; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen; Zhang, Zhongfei

2012-12-01

Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Directory of Open Access Journals (Sweden)

Omar Eldwaik

2018-01-01

Full Text Available Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

Adaptive Markov Random Fields for Example-Based Super-resolution of Faces

Directory of Open Access Journals (Sweden)

Stephenson Todd A

2006-01-01

Full Text Available Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution. For example, hallucination and Markov random field (MRF methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.

Adaptive Markov Random Fields for Example-Based Super-resolution of Faces

Science.gov (United States)

Stephenson, Todd A.; Chen, Tsuhan

2006-12-01

Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.

Simulations research of the global predictive control with self-adaptive in the gas turbine of the nuclear power plant

International Nuclear Information System (INIS)

Su Jie; Xia Guoqing; Zhang Wei

2007-01-01

For further improving the dynamic control capabilities of the gas turbine of the nuclear power plant, this paper puts forward to apply the algorithm of global predictive control with self-adaptive in the rotate speed control of the gas turbine, including control structure and the design of controller in the base of expounding the math model of the gas turbine of the nuclear power plant. the simulation results show that the respond of the change of the gas turbine speed under the control algorithm of global predictive control with self-adaptive is ten second faster than that under the PID control algorithm, and the output value of the gas turbine speed under the PID control algorithm is 1%-2% higher than that under the control slgorithm of global predictive control with self-adaptive. It shows that the algorithm of global predictive control with self-adaptive can better control the output of the speed of the gas turbine of the nuclear power plant and get the better control effect. (authors)

Margin-Wide Earthquake Subspace Scanning Along the Cascadia Subduction Zone Using the Cascadia Initiative Amphibious Dataset

Science.gov (United States)

Morton, E.; Bilek, S. L.; Rowe, C. A.

2017-12-01

Understanding the spatial extent and behavior of the interplate contact in the Cascadia Subduction Zone (CSZ) may prove pivotal to preparation for future great earthquakes, such as the M9 event of 1700. Current and historic seismic catalogs are limited in their integrity by their short duration, given the recurrence rate of great earthquakes, and by their rather high magnitude of completeness for the interplate seismic zone, due to its offshore distance from these land-based networks. This issue is addressed via the 2011-2015 Cascadia Initiative (CI) amphibious seismic array deployment, which combined coastal land seismometers with more than 60 ocean-bottom seismometers (OBS) situated directly above the presumed plate interface. We search the CI dataset for small, previously undetected interplate earthquakes to identify seismic patches on the megathrust. Using the automated subspace detection method, we search for previously undetected events. Our subspace comprises eigenvectors derived from CI OBS and on-land waveforms extracted for existing catalog events that appear to have occurred on the plate interface. Previous work focused on analysis of two repeating event clusters off the coast of Oregon spanning all 4 years of deployment. Here we expand earlier results to include detection and location analysis to the entire CSZ margin during the first year of CI deployment, with more than 200 new events detected for the central portion of the margin. Template events used for subspace scanning primarily occurred beneath the land surface along the coast, at the downdip edge of modeled high slip patches for the 1700 event, with most concentrated at the northwestern edge of the Olympic Peninsula.

Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

Directory of Open Access Journals (Sweden)

Majid Shakhsi Dastgahian

2016-11-01

Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

A parallel direct solver for the self-adaptive hp Finite Element Method

KAUST Repository

Paszyński, Maciej R.; Pardo, David; Torres-Verdí n, Carlos; Demkowicz, Leszek F.; Calo, Victor M.

2010-01-01

measurement simulations problems. We measure the execution time and memory usage of the solver over a large regular mesh with 1.5 million degrees of freedom as well as on the highly non-regular mesh, generated by the self-adaptive h p-FEM, with finite elements

A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Baoguo Yu

2016-01-01

Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

A no-go theorem for a two-dimensional self-correcting quantum memory based on stabilizer codes

International Nuclear Information System (INIS)

Bravyi, Sergey; Terhal, Barbara

2009-01-01

We study properties of stabilizer codes that permit a local description on a regular D-dimensional lattice. Specifically, we assume that the stabilizer group of a code (the gauge group for subsystem codes) can be generated by local Pauli operators such that the support of any generator is bounded by a hypercube of size O(1). Our first result concerns the optimal scaling of the distance d with the linear size of the lattice L. We prove an upper bound d=O(L D-1 ) which is tight for D=1, 2. This bound applies to both subspace and subsystem stabilizer codes. Secondly, we analyze the suitability of stabilizer codes for building a self-correcting quantum memory. Any stabilizer code with geometrically local generators can be naturally transformed to a local Hamiltonian penalizing states that violate the stabilizer condition. A degenerate ground state of this Hamiltonian corresponds to the logical subspace of the code. We prove that for D=1, 2, different logical states can be mapped into each other by a sequence of single-qubit Pauli errors such that the energy of all intermediate states is upper bounded by a constant independent of the lattice size L. The same result holds if there are unused logical qubits that are treated as 'gauge qubits'. It demonstrates that a self-correcting quantum memory cannot be built using stabilizer codes in dimensions D=1, 2. This result is in sharp contrast with the existence of a classical self-correcting memory in the form of a two-dimensional (2D) ferromagnet. Our results leave open the possibility for a self-correcting quantum memory based on 2D subsystem codes or on 3D subspace or subsystem codes.

Consistency analysis of subspace identification methods based on a linear regression approach

DEFF Research Database (Denmark)

Knudsen, Torben

2001-01-01

In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...... not include important model structures as e.g. Box-Jenkins. Based on a simple least squares approach this paper shows the possible inconsistency under the weak assumptions and develops only slightly stricter assumptions sufficient for consistency and which includes any model structure...

The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

Directory of Open Access Journals (Sweden)

Lun-Hui Xu

2013-01-01

Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

Operator quantum error-correcting subsystems for self-correcting quantum memories

International Nuclear Information System (INIS)

Bacon, Dave

2006-01-01

The most general method for encoding quantum information is not to encode the information into a subspace of a Hilbert space, but to encode information into a subsystem of a Hilbert space. Recently this notion has led to a more general notion of quantum error correction known as operator quantum error correction. In standard quantum error-correcting codes, one requires the ability to apply a procedure which exactly reverses on the error-correcting subspace any correctable error. In contrast, for operator error-correcting subsystems, the correction procedure need not undo the error which has occurred, but instead one must perform corrections only modulo the subsystem structure. This does not lead to codes which differ from subspace codes, but does lead to recovery routines which explicitly make use of the subsystem structure. Here we present two examples of such operator error-correcting subsystems. These examples are motivated by simple spatially local Hamiltonians on square and cubic lattices. In three dimensions we provide evidence, in the form a simple mean field theory, that our Hamiltonian gives rise to a system which is self-correcting. Such a system will be a natural high-temperature quantum memory, robust to noise without external intervening quantum error-correction procedures

Usability of an adaptive computer assistant that improves self-care and health literacy of older adults

NARCIS (Netherlands)

Blanson Henkemans, O.A.; Rogers, W.A.; Fisk, A.D.; Neerincx, M.A.; Lindenberg, J.; Mast, C.A.P.G. van der

2008-01-01

Objectives: We developed an adaptive computer assistant for the supervision of diabetics' self-care, to support limiting illness and need for acute treatment, and improve health literacy. This assistant monitors self-care activities logged in the patient's electronic diary. Accordingly, it provides

Process convergence of self-normalized sums of i.i.d. random ...

Indian Academy of Sciences (India)

The study of the asymptotics of the self-normalized sums are also interesting. Logan ... if the constituent random variables are from the domain of attraction of a normal dis- tribution ... index of stability α which equals 2 (for definition, see §2).

3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS

Directory of Open Access Journals (Sweden)

J. Wu

2018-04-01

Full Text Available Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain to new data scenes (target domain, which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling; another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city. Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

Adaptation, Validation, Reliability and Factorial Equivalence of the Rosenberg Self-Esteem Scale in Colombian and Spanish Population.

Science.gov (United States)

Gómez-Lugo, Mayra; Espada, José P; Morales, Alexandra; Marchal-Bertrand, Laurent; Soler, Franklin; Vallejo-Medina, Pablo

2016-10-14

The Rosenberg Self-Esteem Scale is the most widely used instrument to assess self-esteem. In light of the absence of adaptations in Colombia, this study seeks to validate and adapt this scale in the Colombian population, and perform factorial equivalence with the Spanish version. A total of 1,139 seniors (633 Colombians and 506 Spaniards) were evaluated; the individuals answered the Rosenberg Self-Esteem Scale and sexual self-esteem scale. The average score of the items was similar to the questionnaire's theoretical average, and standard deviations were close to one. The psychometric properties of the items are generally adequate with alphas of .83 and .86 and significant (CI = .95) and correlations with the sexual self-esteem scale ranging from .31 and .41. Factorial equivalence was confirmed by means of a structural equation model (CFI = .912 and RMSEA = .079), thus showing a strong level of invariance.

Social functioning and self-esteem in young people with disabilities participating in adapted competitive sport.

Science.gov (United States)

Dinomais, M; Gambart, G; Bruneau, A; Bontoux, L; Deries, X; Tessiot, C; Richard, I

2010-08-01

The aim of this study was to investigate social functioning quality of life and self-esteem in young people with disabilities taking part in adapted competitive sport. A sample of 496 athletes (mean age 16 years 4 months, range: 9 years to 20 years 9 months) was obtained from the 540 participants (91.8%) involved in a French national championship. The main outcome measurements were a social functioning inventory (PedsQL 4.0 social functioning) and a self-esteem inventory in physical areas (physical self inventory 6 PSI-6). The mean PedsQL SF score was 74.6 (SD: 17.7). Comparisons of PedsQL SF according to gender, age, self mobility and training revealed no significant differences between the groups. PedsQL SF was weakly but significantly correlated with all subscales of the PSI-6 in the total population. PSI-6 scores were significantly different between boys and girls, with better self-esteem for boys on general self-esteem (7.7 vs. 6.9, P=0.018), physical condition (6.8 vs. 6.0, P=0.023) and attractive body subscores (6.5 vs. 5.1, Pself-concept, social functioning quality of life and participation in adapted sport activities require further studies. Georg Thieme Verlag KG Stuttgart.New York.

Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

Directory of Open Access Journals (Sweden)

Hui Liu

2015-01-01

Full Text Available The key problem of computer-aided diagnosis (CAD of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO pulmonary nodules than other typical algorithms.

Brief report: Poor self-regulation as a predictor of individual differences in adaptive functioning in young children with autism spectrum disorder.

Science.gov (United States)

Uljarević, Mirko; Hedley, Darren; Nevill, Rose; Evans, David W; Cai, Ru Ying; Butter, Eric; Mulick, James A

2018-04-06

The present study examined the link between poor self-regulation (measured by the child behavior checklist dysregulated profile [DP]) and core autism symptoms, as well as with developmental level, in a sample of 107 children with autism spectrum disorder (ASD) aged 19-46 months. We further examined the utility of DP in predicting individual differences in adaptive functioning, relative to the influence of ASD severity, chronological age (CA), and developmental level. Poor self-regulation was unrelated to CA, developmental level, and severity of ADOS-2 restricted and repetitive behaviors, but was associated with lower ADOS-2 social affect severity. Hierarchical regression identified poor self-regulation as a unique independent predictor of adaptive behavior, with more severe dysregulation predicting poorer adaptive functioning. Results highlight the importance of early identification of deficits in self-regulation, and more specifically, of the utility of DP, when designing individually tailored treatments for young children with ASD. Autism Res 2018. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. This study explored the relationship between poor self-regulation and age, verbal and non-verbal developmental level, severity of autism symptoms and adaptive functioning in 107 children with autism under 4 years of age. Poor self-regulation was unrelated to age, developmental level, and severity of restricted and repetitive behaviors but was associated with lower social affect severity. Importantly, more severe self-regulation deficits predicted poorer adaptive functioning. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.

Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

International Nuclear Information System (INIS)

Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei

2017-01-01

Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

Culturally Adapted Cognitive Behavioral Guided Self-Help for Binge Eating: A Feasibility Study with Mexican Americans

Science.gov (United States)

Cachelin, Fary M.; Shea, Munyi; Phimphasone, Phoutdavone; Wilson, G. Terence; Thompson, Douglas R.; Striegel, Ruth H.

2014-01-01

Objective was to test feasibility and preliminary efficacy of a culturally adapted cognitive-behavioral self-help program to treat binge eating and related problems in Mexican Americans. Participants were 31 women recruited from the Los Angeles area and diagnosed with binge eating disorder, recurrent binge eating or bulimia nervosa. Participants completed a culturally adapted version of a CBT-based self-help program with 8 guidance sessions over a 3-month period. Treatment efficacy was evaluated in terms of binge eating, psychological functioning, and weight loss. Intent-to-treat analyses revealed 35.5% abstinence from binge eating at post-treatment and 38.7% diagnostic remission. Results indicated significant pre-treatment to post-treatment improvement on distress level, BMI, eating disorder psychopathology, and self-esteem. Satisfaction with the program was high. Findings demonstrate that the program is acceptable, feasible, and efficacious in reducing binge eating and associated symptoms for Mexican American women. Study provides “proof of concept” for implementation of culturally adapted forms of evidence-based programs. PMID:25045955

Designing Networks that are Capable of Self-Healing and Adapting

Science.gov (United States)

2017-04-01

from statistical mechanics, combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we... principles for self-healing networks, and applications, and construct an all-possible-paths model for network adaptation. 2015-11-16 UNIT CONVERSION...kg m –3 ) pound-force (lbf avoirdupois) 4.448 222 newton (N) Energy/Work/Power electron volt (eV) 1.602 177 × 10 –19 joule (J) erg 1 × 10 –7

Predictors of Career Adaptability Skill among Higher Education Students in Nigeria

Science.gov (United States)

Ebenehi, Amos Shaibu; Rashid, Abdullah Mat; Bakar, Ab Rahim

2016-01-01

This paper examined predictors of career adaptability skill among higher education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study. A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used…

Parametric Adaptive Radar Detector with Enhanced Mismatched Signals Rejection Capabilities

Directory of Open Access Journals (Sweden)

Liu Bin

2010-01-01

Full Text Available We consider the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. We propose a parametric radar detector by introducing a design parameter to trade off the target sensitivity with sidelobes energy rejection. The resulting detector merges the statistics of Kelly's GLRT and of the Rao test and so covers Kelly's GLRT and the Rao test as special cases. Both invariance properties and constant false alarm rate (CFAR behavior for this detector are studied. At the analysis stage, the performance of the new receiver is assessed and compared with several traditional adaptive detectors. The results highlight better rejection capabilities of this proposed detector for mismatched signals. Further, we develop two two-stage detectors, one of which consists of an adaptive matched filter (AMF followed by the aforementioned detector, and the other is obtained by cascading a GLRT-based Subspace Detector (SD and the proposed adaptive detector. We show that the former two-stage detector outperforms traditional two-stage detectors in terms of selectivity, and the latter yields more robustness.

On Self-Adaptive Method for General Mixed Variational Inequalities

Directory of Open Access Journals (Sweden)

Abdellah Bnouhachem

2008-01-01

Full Text Available We suggest and analyze a new self-adaptive method for solving general mixed variational inequalities, which can be viewed as an improvement of the method of (Noor 2003. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method. Since the general mixed variational inequalities include general variational inequalities, quasivariational inequalities, and nonlinear (implicit complementarity problems as special cases, results proved in this paper continue to hold for these problems.

Relational Database Extension Oriented, Self-adaptive Imagery Pyramid Model

Directory of Open Access Journals (Sweden)

HU Zhenghua

2015-06-01

Full Text Available With the development of remote sensing technology, especially the improvement of sensor resolution, the amount of image data is increasing. This puts forward higher requirements to manage huge amount of data efficiently and intelligently. And how to access massive remote sensing data with efficiency and smartness becomes an increasingly popular topic. In this paper, against current development status of Spatial Data Management System, we proposed a self-adaptive strategy for image blocking and a method for LoD(level of detailmodel construction that adapts, with the combination of database storage, network transmission and the hardware of the client. Confirmed by experiments, this imagery management mechanism can achieve intelligent and efficient storage and access in a variety of different conditions of database, network and client. This study provides a feasible idea and method for efficient image data management, contributing to the efficient access and management for remote sensing image data which are based on database technology under network environment of C/S architecture.

Performance feedback, self-esteem, and cardiovascular adaptation to recurring stressors.

Science.gov (United States)

Brown, Eoin G; Creaven, Ann-Marie

2017-05-01

This study sought to examine the effects of performance feedback and individual differences in self-esteem on cardiovascular habituation to repeat stress exposure. Sixty-six university students (n = 39 female) completed a self-esteem measure and completed a cardiovascular stress-testing protocol involving repeated exposure to a mental arithmetic task. Cardiovascular functioning was sampled across four phases: resting baseline, initial stress exposure, a recovery period, and repeated stress exposure. Participants were randomly assigned to receive fictional positive feedback, negative feedback, or no feedback following the recovery period. Negative feedback was associated with a sensitized blood pressure response to a second exposure of the stress task. Positive feedback was associated with decreased cardiovascular and psychological responses to a second exposure. Self-esteem was also found to predict reactivity and this interacted with the type of feedback received. These findings suggest that negative performance feedback sensitizes cardiovascular reactivity to stress, whereas positive performance feedback increases both cardiovascular and psychological habituation to repeat exposure to stressors. Furthermore, an individual's self-esteem also appears to influence this process.

Adaptive versus proactive behavior in service recovery: The role of self-managing teams

NARCIS (Netherlands)

Jong, de A.; Ruyter, de J.C.

2004-01-01

In this article, we develop a conceptual model of adaptive versus proactive recovery behavior by self-managing teams (SMTs) in service recovery operations. To empirically test the conceptual model a combination of bank employee, customer, and archival data is collected. The results demonstrate

Self-Trapping Self-Repelling Random Walks

Science.gov (United States)

Grassberger, Peter

2017-10-01

Although the title seems self-contradictory, it does not contain a misprint. The model we study is a seemingly minor modification of the "true self-avoiding walk" model of Amit, Parisi, and Peliti in two dimensions. The walks in it are self-repelling up to a characteristic time T* (which depends on various parameters), but spontaneously (i.e., without changing any control parameter) become self-trapping after that. For free walks, T* is astronomically large, but on finite lattices the transition is easily observable. In the self-trapped regime, walks are subdiffusive and intermittent, spending longer and longer times in small areas until they escape and move rapidly to a new area. In spite of this, these walks are extremely efficient in covering finite lattices, as measured by average cover times.

STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction.

Science.gov (United States)

Zhou, Zechen; Wang, Jinnan; Balu, Niranjan; Li, Rui; Yuan, Chun

2016-02-01

A new subspace-based iterative reconstruction method, termed Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction (STEP), is presented and evaluated in comparison to the existing autocalibrating method SPIRiT and calibrationless method SAKE. In STEP, two tailored schemes including k-space partition and basis selection are proposed to promote spatially variant signal subspace and incorporated into a self-supporting structured low rank model to enforce properties of locality, sparsity, and rank deficiency, which can be formulated into a constrained optimization problem and solved by an iterative algorithm. Simulated and in vivo datasets were used to investigate the performance of STEP in terms of overall image quality and detail structure preservation. The advantage of STEP on image quality is demonstrated by retrospectively undersampled multichannel Cartesian data with various patterns. Compared with SPIRiT and SAKE, STEP can provide more accurate reconstruction images with less residual aliasing artifacts and reduced noise amplification in simulation and in vivo experiments. In addition, STEP has the capability of combining compressed sensing with arbitrary sampling trajectory. Using k-space partition and basis selection can further improve the performance of parallel imaging reconstruction with or without calibration signals. © 2015 Wiley Periodicals, Inc.

A study on directional resistivity logging-while-drilling based on self-adaptive hp-FEM

Science.gov (United States)

Liu, Dejun; Li, Hui; Zhang, Yingying; Zhu, Gengxue; Ai, Qinghui

2014-12-01

Numerical simulation of resistivity logging-while-drilling (LWD) tool response provides guidance for designing novel logging instruments and interpreting real-time logging data. In this paper, based on self-adaptive hp-finite element method (hp-FEM) algorithm, we analyze LWD tool response against model parameters and briefly illustrate geosteering capabilities of directional resistivity LWD. Numerical simulation results indicate that the change of source spacing is of obvious influence on the investigation depth and detecting precision of resistivity LWD tool; the change of frequency can improve the resolution of low-resistivity formation and high-resistivity formation. The simulation results also indicate that the self-adaptive hp-FEM algorithm has good convergence speed and calculation accuracy to guide the geologic steering drilling and it is suitable to simulate the response of resistivity LWD tools.

Goal-Oriented Self-Adaptive hp Finite Element Simulation of 3D DC Borehole Resistivity Simulations

KAUST Repository

Calo, Victor M.

2011-05-14

In this paper we present a goal-oriented self-adaptive hp Finite Element Method (hp-FEM) with shared data structures and a parallel multi-frontal direct solver. The algorithm automatically generates (without any user interaction) a sequence of meshes delivering exponential convergence of a prescribed quantity of interest with respect to the number of degrees of freedom. The sequence of meshes is generated from a given initial mesh, by performing h (breaking elements into smaller elements), p (adjusting polynomial orders of approximation) or hp (both) refinements on the finite elements. The new parallel implementation utilizes a computational mesh shared between multiple processors. All computational algorithms, including automatic hp goal-oriented adaptivity and the solver work fully in parallel. We describe the parallel self-adaptive hp-FEM algorithm with shared computational domain, as well as its efficiency measurements. We apply the methodology described to the three-dimensional simulation of the borehole resistivity measurement of direct current through casing in the presence of invasion.

Internet-based self-management plus education compared with usual care in asthma: a randomized trial

NARCIS (Netherlands)

van der Meer, Victor; Bakker, Moira J.; van den Hout, Wilbert B.; Rabe, Klaus F.; Sterk, Peter J.; Kievit, Job; Assendelft, Willem J. J.; Sont, Jacob K.; Assendelft, W. J. J.; Thiadens, H. A.; Bakker, M. J.; van den Hout, W. B.; Kievit, J.; van der Meer, V.; Sont, J. K.; Kaptein, A. A.; Rikkers-Mutsaerts, E. R. V. M.; Rabe, K. F.; Bel, E. H. D.; Detmar, S. B.; Otten, W.; van Stel, H. F.; Roldaan, A. C.; de Jongste, J. C.; Toussaint, P. J.

2009-01-01

BACKGROUND: The Internet may support patient self-management of chronic conditions, such as asthma. OBJECTIVE: To evaluate the effectiveness of Internet-based asthma self-management. DESIGN: Randomized, controlled trial. SETTING: 37 general practices and 1 academic outpatient department in the

A randomized trial of videoconference-delivered cognitive behavioral therapy for survivors of breast cancer with self-reported cognitive dysfunction.

Science.gov (United States)

Ferguson, Robert J; Sigmon, Sandra T; Pritchard, Andrew J; LaBrie, Sharon L; Goetze, Rachel E; Fink, Christine M; Garrett, A Merrill

2016-06-01

Long-term chemotherapy-related cognitive dysfunction (CRCD) affects a large number of cancer survivors. To the authors' knowledge, to date there is no established treatment for this survivorship problem. The authors herein report results of a small randomized controlled trial of a cognitive behavioral therapy (CBT), Memory and Attention Adaptation Training (MAAT), compared with an attention control condition. Both treatments were delivered over a videoconference device. A total of 47 survivors of female breast cancer who reported CRCD were randomized to MAAT or supportive therapy and were assessed at baseline, after treatment, and at 2 months of follow-up. Participants completed self-report measures of cognitive symptoms and quality of life and a brief telephone-based neuropsychological assessment. MAAT participants made gains in perceived (self-reported) cognitive impairments (P = .02), and neuropsychological processing speed (P = .03) compared with supportive therapy controls. A large MAAT effect size was observed at the 2-month follow-up with regard to anxiety concerning cognitive problems (Cohen's d for standard differences in effect sizes, 0.90) with medium effects noted in general function, fatigue, and anxiety. Survivors rated MAAT and videoconference delivery with high satisfaction. MAAT may be an efficacious psychological treatment of CRCD that can be delivered through videoconference technology. This research is important because it helps to identify a treatment option for survivors that also may improve access to survivorship services. Cancer 2016;122:1782-91. © 2016 American Cancer Society. © 2016 American Cancer Society.

mHealth intervention to support asthma self-management in adolescents : The ADAPT study

NARCIS (Netherlands)

Kosse, R.C.; Bouvy, M.L.; de Vries, T.W.; Kaptein, A.A.; Geers, H.C.J.; van Dijk, Liset; Koster, E.S.

2017-01-01

Purpose: Poor medication adherence in adolescents with asthma results in poorly controlled disease and increased morbidity. The aim of the ADolescent Adherence Patient Tool (ADAPT) study is to develop an mHealth intervention to support self-management and to evaluate the effectiveness in improving

mHealth intervention to support asthma self-management in adolescents: the ADAPT study.

NARCIS (Netherlands)

Kosse, R.C.; Bouvy, M.L.; Vries, T.W. de; Kaptein, A.A.; Geers, H.C.J.; Dijk, L. van; Koster, E.S.

2017-01-01

Purpose: Poor medication adherence in adolescents with asthma results in poorly controlled disease and increased morbidity. The aim of the ADolescent Adherence Patient Tool (ADAPT) study is to develop an mHealth intervention to support self-management and to evaluate the effectiveness in improving

Towards automatic music transcription: note extraction based on independent subspace analysis

Science.gov (United States)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Optimization by GRASP greedy randomized adaptive search procedures

CERN Document Server

Resende, Mauricio G C

2016-01-01

This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimizat...

Adaptive filtration of speech signals in the presence of correlated noise with random variation of probabilistic characteristics

OpenAIRE

M. O. Partala; S. Ya. Zhuk

2007-01-01

On the base of mixed Markoff process in discrete time optimal and quasioptimal algorithms is designed for adaptive filtration of speech signals in the presence of correlated noise with random variation of probabilistic characteristics.

Conformal invariance self-avoiding walks in the plane or on a random surface

International Nuclear Information System (INIS)

Duplantier, B.

1988-01-01

The two-dimensional (2D) properties of polymers embedded in a solvent, are studied. They are modeled on a lattice by self-avoiding walks. The polymer properties either in the plane with a fixed metric, or on a random 2D surface, where the metric has critical fluctuations, are considered. In the scope of the work, the following topics are discussed: the watermelon topology; the O(n) model and Coulomb gas technique; the model and critical behaviours of polymers on a two-dimensional random lattice; the conformal invariance in a random surface and higher topologies

Diversity in random subspacing ensembles

NARCIS (Netherlands)

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

2004-01-01

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

Self-* and Adaptive Mechanisms for Large Scale Distributed Systems

Science.gov (United States)

Fragopoulou, P.; Mastroianni, C.; Montero, R.; Andrjezak, A.; Kondo, D.

Large-scale distributed computing systems and infrastructure, such as Grids, P2P systems and desktop Grid platforms, are decentralized, pervasive, and composed of a large number of autonomous entities. The complexity of these systems is such that human administration is nearly impossible and centralized or hierarchical control is highly inefficient. These systems need to run on highly dynamic environments, where content, network topologies and workloads are continuously changing. Moreover, they are characterized by the high degree of volatility of their components and the need to provide efficient service management and to handle efficiently large amounts of data. This paper describes some of the areas for which adaptation emerges as a key feature, namely, the management of computational Grids, the self-management of desktop Grid platforms and the monitoring and healing of complex applications. It also elaborates on the use of bio-inspired algorithms to achieve self-management. Related future trends and challenges are described.

Optimal image alignment with random projections of manifolds: algorithm and geometric analysis.

Science.gov (United States)

Kokiopoulou, Effrosyni; Kressner, Daniel; Frossard, Pascal

2011-06-01

This paper addresses the problem of image alignment based on random measurements. Image alignment consists of estimating the relative transformation between a query image and a reference image. We consider the specific problem where the query image is provided in compressed form in terms of linear measurements captured by a vision sensor. We cast the alignment problem as a manifold distance minimization problem in the linear subspace defined by the measurements. The transformation manifold that represents synthesis of shift, rotation, and isotropic scaling of the reference image can be given in closed form when the reference pattern is sparsely represented over a parametric dictionary. We show that the objective function can then be decomposed as the difference of two convex functions (DC) in the particular case where the dictionary is built on Gaussian functions. Thus, the optimization problem becomes a DC program, which in turn can be solved globally by a cutting plane method. The quality of the solution is typically affected by the number of random measurements and the condition number of the manifold that describes the transformations of the reference image. We show that the curvature, which is closely related to the condition number, remains bounded in our image alignment problem, which means that the relative transformation between two images can be determined optimally in a reduced subspace.

Von Neuman representations on self-dual Hilbert W* moduli

International Nuclear Information System (INIS)

Frank, M.

1987-01-01

Von Neumann algebras M of bounded operators on self-dual Hilbert W* moduli H possessing a cyclic-separating element x-bar in H are considered. The close relation of them to certain real subspaces of H is established. Under the supposition that the underlying W*-algebra is commutative, a Tomita-Takesaki type theorem is stated. The natural cone in H arising from the pair (M, x-bar) is investigated and its properties are obtained

Parallel random number generator for inexpensive configurable hardware cells

Science.gov (United States)

Ackermann, J.; Tangen, U.; Bödekker, B.; Breyer, J.; Stoll, E.; McCaskill, J. S.

2001-11-01

A new random number generator ( RNG) adapted to parallel processors has been created. This RNG can be implemented with inexpensive hardware cells. The correlation between neighboring cells is suppressed with smart connections. With such connection structures, sequences of pseudo-random numbers are produced. Numerical tests including a self-avoiding random walk test and the simulation of the order parameter and energy of the 2D Ising model give no evidence for correlation in the pseudo-random sequences. Because the new random number generator has suppressed the correlation between neighboring cells which is usually observed in cellular automaton implementations, it is applicable for extended time simulations. It gives an immense speed-up factor if implemented directly in configurable hardware, and has recently been used for long time simulations of spatially resolved molecular evolution.

Performance optimization of PM-16QAM transmission system enabled by real-time self-adaptive coding.

Science.gov (United States)

Qu, Zhen; Li, Yao; Mo, Weiyang; Yang, Mingwei; Zhu, Shengxiang; Kilper, Daniel C; Djordjevic, Ivan B

2017-10-15

We experimentally demonstrate self-adaptive coded 5×100  Gb/s WDM polarization multiplexed 16 quadrature amplitude modulation transmission over a 100 km fiber link, which is enabled by a real-time control plane. The real-time optical signal-to-noise ratio (OSNR) is measured using an optical performance monitoring device. The OSNR measurement is processed and fed back using control plane logic and messaging to the transmitter side for code adaptation, where the binary data are adaptively encoded with three types of low-density parity-check (LDPC) codes with code rates of 0.8, 0.75, and 0.7 of large girth. The total code-adaptation latency is measured to be 2273 ms. Compared with transmission without adaptation, average net capacity improvements of 102%, 36%, and 7.5% are obtained, respectively, by adaptive LDPC coding.

Adaptive automatic generation control with superconducting magnetic energy storage in power systems

International Nuclear Information System (INIS)

Tripathy, S.C.; Balasubramanian, R.; Nair, P.S.C.

1992-01-01

An improved automatic generation control (AGC) employing self-tuning adaptive control for both main AGC loop and superconducting magnetic energy storage (SMES) is presented in this paper. Computer simulations on a two-area interconnected power system show that the proposed adaptive control scheme is very effective in damping out oscillations caused by load disturbances and its performance is quite insensitive to controller gain parameter changes of SMES. A comprehensive comparative performance evaluation of control schemes using adaptive and non-adaptive controllers in the main AGC and in the SMES control loops is presented. The improvement in performance brought in by the adaptive scheme is particularly pronounced for load changes of random magnitude and duration. The proposed controller can be easily implemented using microprocessors

Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID

Science.gov (United States)

Chen, Zhigang; Qu, Jiangang

2017-09-01

In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.

Self-Adaptive Operator Scheduling using the Religion-Based EA

DEFF Research Database (Denmark)

Thomsen, Rene; Krink, Thiemo

2002-01-01

of their application is determined by a constant parameter, such as a fixed mutation rate. However, recent studies have shown that the optimal usage of a variation operator changes during the EA run. In this study, we combined the idea of self-adaptive mutation operator scheduling with the Religion-Based EA (RBEA......), which is an agent model with spatially structured and variable sized subpopulations (religions). In our new model (OSRBEA), we used a selection of different operators, such that each operator type was applied within one specific subpopulation only. Our results indicate that the optimal choice...

Self-adaptive robot training of stroke survivors for continuous tracking movements

Directory of Open Access Journals (Sweden)

Morasso Pietro

2010-03-01

Full Text Available Abstract Background Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements. Methods The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1 a force field generator that combines a non linear attractive field and a viscous field; 2 a performance evaluation module; 3 an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control. Results The preliminary results with a small group of patients (10 chronic hemiplegic subjects show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients. Conclusions The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale

Effect of randomness on multi-frequency aeroelastic responses resolved by Unsteady Adaptive Stochastic Finite Elements

International Nuclear Information System (INIS)

Witteveen, Jeroen A.S.; Bijl, Hester

2009-01-01

The Unsteady Adaptive Stochastic Finite Elements (UASFE) method resolves the effect of randomness in numerical simulations of single-mode aeroelastic responses with a constant accuracy in time for a constant number of samples. In this paper, the UASFE framework is extended to multi-frequency responses and continuous structures by employing a wavelet decomposition pre-processing step to decompose the sampled multi-frequency signals into single-frequency components. The effect of the randomness on the multi-frequency response is then obtained by summing the results of the UASFE interpolation at constant phase for the different frequency components. Results for multi-frequency responses and continuous structures show a three orders of magnitude reduction of computational costs compared to crude Monte Carlo simulations in a harmonically forced oscillator, a flutter panel problem, and the three-dimensional transonic AGARD 445.6 wing aeroelastic benchmark subject to random fields and random parameters with various probability distributions.

Opinion dynamics on an adaptive random network

Science.gov (United States)

Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.

2009-04-01

We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.

[Adaptation of self-image level and defense mechanisms in elderly patients with complicated stoma].

Science.gov (United States)

Ortiz-Rivas, Miriam Karina; Moreno-Pérez, Norma Elvira; Vega-Macías, Héctor Daniel; Jiménez-González, María de Jesús; Navarro-Elías, María de Guadalupe

2014-01-01

Ostomy patients face a number of problems that impact negatively on their personal welfare. The aim of this research is determine the nature and intensity of the relationship between the level of self-concept adaptive mode and the consistent use of coping strategies of older adults with a stoma. Quantitative, correlational and transversal. VIVEROS 03 and CAPS surveys were applied in 3 hospitals in the City of Durango, México. The study included 90 older adults with an intestinal elimination stoma with complications. Kendall's Tau-b coefficient was the non-parametric test used to measure this association. Most older adults analyzed (61.3 < % < 79.9) are not completely adapted to the condition of living with an intestinal stoma. There is also a moderate positive correlation (0,569) between the level of adaptation of the older adults with a stoma and the conscious use of coping strategies. The presence of an intestinal stoma represents a physical and psychological health problem that is reflected in the level of adaptation of the self-image. Elderly people with a stoma use only a small part of defense mechanisms as part of coping process. This limits their ability to face the adversities related to their condition, potentially causing major health complications. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

Directory of Open Access Journals (Sweden)

Quanli Xu

2018-03-01

Full Text Available Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

pSum-SaDE: A Modified p-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization

Directory of Open Access Journals (Sweden)

Rasim M. Alguliev

2011-01-01

Full Text Available Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.

Self-adaptive method to distinguish inner and outer contours of industrial computed tomography image for rapid prototype

International Nuclear Information System (INIS)

Duan Liming; Ye Yong; Zhang Xia; Zuo Jian

2013-01-01

A self-adaptive identification method is proposed for realizing more accurate and efficient judgment about the inner and outer contours of industrial computed tomography (CT) slice images. The convexity-concavity of the single-pixel-wide closed contour is identified with angle method at first. Then, contours with concave vertices are distinguished to be inner or outer contours with ray method, and contours without concave vertices are distinguished with extreme coordinate value method. The method was chosen to automatically distinguish contours by means of identifying the convexity and concavity of the contours. Thus, the disadvantages of single distinguishing methods, such as ray method's time-consuming and extreme coordinate method's fallibility, can be avoided. The experiments prove the adaptability, efficiency, and accuracy of the self-adaptive method. (authors)

A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG-Based Emotion Recognition

Directory of Open Access Journals (Sweden)

Xin Chai

2017-05-01

Full Text Available Electroencephalography (EEG-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects. Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE, which

A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes

Science.gov (United States)

Carpenter, Mark H.; Vuik, C.; Lucas, Peter; vanGijzen, Martin; Bijl, Hester

2010-01-01

A general algorithm is developed that reuses available information to accelerate the iterative convergence of linear systems with multiple right-hand sides A x = b (sup i), which are commonly encountered in steady or unsteady simulations of nonlinear equations. The algorithm is based on the classical GMRES algorithm with eigenvector enrichment but also includes a Galerkin projection preprocessing step and several novel Krylov subspace reuse strategies. The new approach is applied to a set of test problems, including an unsteady turbulent airfoil, and is shown in some cases to provide significant improvement in computational efficiency relative to baseline approaches.

Randomized controlled trial of Mindfulness-Based Stress Reduction versus aerobic exercise: effects on the self-referential brain network in social anxiety disorder

Directory of Open Access Journals (Sweden)

Philippe eGoldin

2012-11-01

Full Text Available Background: Social Anxiety Disorder (SAD is characterized by distorted self-views. The goal of this study was to examine whether Mindfulness-Based Stress Reduction (MBSR alters behavioral and brain measures of negative and positive self-views. Methods: 56 adult patients with generalized SAD were randomly assigned to MBSR or a comparison aerobic exercise (AE program. A self-referential encoding task was administered at baseline and post-intervention to examine changes in behavioral and neural responses in the self-referential brain network during functional magnetic resonance imaging. Patients were cued to decide whether positive and negative social trait adjectives were self-descriptive or in upper case font. Results: Behaviorally, compared to AE, MBSR produced greater decreases in negative self-views, and equivalent increases in positive self-views. Neurally, during negative self vs. case, compared to AE, MBSR led to increased brain responses in the posterior cingulate cortex (PCC. There were no differential changes for positive self vs. case. Secondary analyses showed that changes in endorsement of negative and positive self-views were associated with decreased social anxiety symptom severity for MBSR, but not AE. Additionally, MBSR-related increases in DMPFC activity during negative self-view vs. case were associated with decreased social anxiety-related disability and increased mindfulness. Analysis of neural temporal dynamics revealed MBSR-related changes in the timing of neural responses in the DMPFC and PCC for negative self-view vs. case.Conclusions: These findings suggest that MBSR attenuates maladaptive habitual self-views by facilitating automatic (i.e., uninstructed recruitment of cognitive and attention regulation neural networks. This highlights potentially important links between self-referential and cognitive-attention regulation systems and suggests that MBSR may enhance more adaptive social self-referential processes in

Characteristics of Social-Psychological Adaptation and Self-Regulation in Patients with Diabetes Mellitus

Science.gov (United States)

Tsv?tkova, Nadezhda A.; Aleksandrova, Marina I.; Rybakova, Anna Igorevna; Starovoitova, Larisa I.; Kononova, Tatiana B.

2016-01-01

The article presents the results of searching for answers to the following questions: Which are the characteristics of socio-psychological adaptation and self-regulation behavior in patients with diabetes mellitus type II? What is the nature of the relationship between these personal characteristics? In particular, it contains results of…

On the selection of user-defined parameters in data-driven stochastic subspace identification

Science.gov (United States)

Priori, C.; De Angelis, M.; Betti, R.

2018-02-01

The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

CD8 T Cell Sensory Adaptation Dependent on TCR Avidity for Self-Antigens

DEFF Research Database (Denmark)

Marquez, M.-E.; Ellmeier, W.; Sanchez-Guajardo, Vanesa Maria

2005-01-01

dephosphorylation of linker for activation of T cells and ERK upon activation. Normal TCR levels and cytokine production were restored by culturing cells in the absence of TCR/spMHC interaction, demonstrating dynamic tuning of peripheral T cell responses. The effect of avidity for self-ligand(s) on this sensory...... ZAP-YEEI cells were enhanced. Our data provide support for central and peripheral sensory T cell adaptation induced as a function of TCR avidity for self-ligands and signaling level. This may contribute to buffer excessive autoreactivity while optimizing TCR repertoire usage....

Compartmentalization Technologies via Self-Assembly and Cross-Linking of Amphiphilic Random Block Copolymers in Water.

Science.gov (United States)

Matsumoto, Mayuko; Terashima, Takaya; Matsumoto, Kazuma; Takenaka, Mikihito; Sawamoto, Mitsuo

2017-05-31

Orthogonal self-assembly and intramolecular cross-linking of amphiphilic random block copolymers in water afforded an approach to tailor-make well-defined compartments and domains in single polymer chains and nanoaggregates. For a double compartment single-chain polymer, an amphiphilic random block copolymer bearing hydrophilic poly(ethylene glycol) (PEG) and hydrophobic dodecyl, benzyl, and olefin pendants was synthesized by living radical polymerization (LRP) and postfunctionalization; the dodecyl and benzyl units were incorporated into the different block segments, whereas PEG pendants were statistically attached along a chain. The copolymer self-folded via the orthogonal self-assembly of hydrophobic dodecyl and benzyl pendants in water, followed by intramolecular cross-linking, to form a single-chain polymer carrying double yet distinct hydrophobic nanocompartments. A single-chain cross-linked polymer with a chlorine terminal served as a globular macroinitiator for LRP to provide an amphiphilic tadpole macromolecule comprising a hydrophilic nanoparticle and a hydrophobic polymer tail; the tadpole thus self-assembled into multicompartment aggregates in water.

A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

Energy Technology Data Exchange (ETDEWEB)

Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping

2015-01-15

A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations.

A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

International Nuclear Information System (INIS)

Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping

2015-01-01

A self-adaptive genetic algorithm for estimating Jiles–Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet’s hysteresis loops, and the results are in good agreement with published data. - Highlights: • We present a method to find JA parameters for both major and minor loops. • Fitness function is based on distances between key points of normalized loops. • The selection pressure is adjusted adaptively based on generations

[Effects of group psychological counseling on self-confidence and social adaptation of burn patients].

Science.gov (United States)

Dang, Rui; Wang, Yishen; Li, Na; He, Ting; Shi, Mengna; Liang, Yanyan; Zhu, Chan; Zhou, Yongbo; Qi, Zongshi; Hu, Dahai

2014-12-01

To explore the effects of group psychological counseling on the self-confidence and social adaptation of burn patients during the course of rehabilitation. Sixty-four burn patients conforming to the inclusion criteria and hospitalized from January 2012 to January 2014 in Xijing Hospital were divided into trial group and control group according to the method of rehabilitation, with 32 cases in each group. Patients in the two groups were given ordinary rehabilitation training for 8 weeks, and the patients in trial group were given a course of group psychological counseling in addition. The Rosenberg's Self-Esteem Scale was used to evaluate the changes in self-confidence levels, and the number of patients with inferiority complex, normal feeling, self-confidence, and over self-confidence were counted before and after treatment. The Abbreviated Burn-Specific Health Scale was used to evaluate physical function, psychological function, social relationship, health condition, and general condition before and after treatment to evaluate the social adaptation of patients. Data were processed with t test, chi-square test, Mann-Whitney U test, and Wilcoxon test. (1) After treatment, the self-confidence levels of patients in trial group were significantly higher than those in control group (Z = -2.573, P 0.05). (2) After treatment, the scores of psychological function, social relationship, health condition, and general condition were (87 ± 3), (47.8 ± 3.6), (49 ± 3), and (239 ± 10) points in trial group, which were significantly higher than those in control group [(79 ± 4), (38.3 ± 5.6), (46 ± 4), and (231 ± 9) points, with t values respectively -8.635, -8.125, -3.352, -3.609, P values below 0.01]. After treatment, the scores of physical function, psychological function, social relationship, health condition, and general condition in trial group were significantly higher than those before treatment (with t values from -33.282 to -19.515, P values below 0.05). The scores

Randomized controlled trial of video self-modeling following speech restructuring treatment for stuttering.

Science.gov (United States)

Cream, Angela; O'Brian, Sue; Jones, Mark; Block, Susan; Harrison, Elisabeth; Lincoln, Michelle; Hewat, Sally; Packman, Ann; Menzies, Ross; Onslow, Mark

2010-08-01

In this study, the authors investigated the efficacy of video self-modeling (VSM) following speech restructuring treatment to improve the maintenance of treatment effects. The design was an open-plan, parallel-group, randomized controlled trial. Participants were 89 adults and adolescents who undertook intensive speech restructuring treatment. Post treatment, participants were randomly assigned to 2 trial arms: standard maintenance and standard maintenance plus VSM. Participants in the latter arm viewed stutter-free videos of themselves each day for 1 month. The addition of VSM did not improve speech outcomes, as measured by percent syllables stuttered, at either 1 or 6 months postrandomization. However, at the latter assessment, self-rating of worst stuttering severity by the VSM group was 10% better than that of the control group, and satisfaction with speech fluency was 20% better. Quality of life was also better for the VSM group, which was mildly to moderately impaired compared with moderate impairment in the control group. VSM intervention after treatment was associated with improvements in self-reported outcomes. The clinical implications of this finding are discussed.

A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method

Directory of Open Access Journals (Sweden)

O. Tsakiridis

2016-01-01

Full Text Available A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique.

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

Science.gov (United States)

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

Self-adaptation in Software-intensive Cyber-physical Systems: From System Goals to Architecture Configurations

Czech Academy of Sciences Publication Activity Database

Gerostathopoulos, I.; Bureš, Tomáš; Hnětynka, P.; Keznikl, Jaroslav; Kit, M.; Plášil, F.; Plouzeau, N.

2016-01-01

Roč. 122, December (2016), s. 378-397 ISSN 0164-1212 Grant - others:GA MŠk(CZ) LD15051 Institutional support: RVO:67985807 Keywords : cyber–physical systems * self-adaptivity * dependability Subject RIV: JC - Computer Hardware ; Software Impact factor: 2.444, year: 2016

Curve Evolution in Subspaces and Exploring the Metameric Class of Histogram of Gradient Orientation based Features using Nonlinear Projection Methods

DEFF Research Database (Denmark)

Tatu, Aditya Jayant

This thesis deals with two unrelated issues, restricting curve evolution to subspaces and computing image patches in the equivalence class of Histogram of Gradient orientation based features using nonlinear projection methods. Curve evolution is a well known method used in various applications like...... tracking interfaces, active contour based segmentation methods and others. It can also be used to study shape spaces, as deforming a shape can be thought of as evolving its boundary curve. During curve evolution a curve traces out a path in the infinite dimensional space of curves. Due to application...... specific requirements like shape priors or a given data model, and due to limitations of the computer, the computed curve evolution forms a path in some finite dimensional subspace of the space of curves. We give methods to restrict the curve evolution to a finite dimensional linear or implicitly defined...

Can models of self-management support be adapted across cancer types? A comparison of unmet self-management needs for patients with breast or colorectal cancer.

Science.gov (United States)

Mansfield, Elise; Mackenzie, Lisa; Carey, Mariko; Peek, Kerry; Shepherd, Jan; Evans, Tiffany-Jane

2018-03-01

There is an increased focus on supporting patients with cancer to actively participate in their healthcare, an approach commonly termed 'self-management'. Comparing unmet self-management needs across cancer types may reveal opportunities to adapt effective self-management support strategies from one cancer type to another. Given that breast and colorectal cancers are prevalent, and have high survival rates, we compared these patients' recent need for help with self-management. Data on multiple aspects of self-management were collected from 717 patients with breast cancer and 336 patients with colorectal cancer attending one of 13 Australian medical oncology treatment centres. There was no significant difference between the proportion of patients with breast or colorectal cancer who reported a need for help with at least one aspect of self-management. Patients with breast cancer were significantly more likely to report needing help with exercising more, while patients with colorectal cancer were more likely to report needing help with reducing alcohol consumption. When controlling for treatment centre, patients who were younger, experiencing distress or had not received chemotherapy were more likely to report needing help with at least one aspect of self-management. A substantial minority of patients reported an unmet need for self-management support. This indicates that high-quality intervention research is needed to identify effective self-management support strategies, as well as implementation trials to identify approaches to translating these strategies into practice. Future research should continue to explore whether self-management support strategies could be adapted across cancer types.

Acoustic levitation with self-adaptive flexible reflectors.

Science.gov (United States)

Hong, Z Y; Xie, W J; Wei, B

2011-07-01

Two kinds of flexible reflectors are proposed and examined in this paper to improve the stability of single-axis acoustic levitator, especially in the case of levitating high-density and high-temperature samples. One kind is those with a deformable reflecting surface, and the other kind is those with an elastic support, both of which are self-adaptive to the change of acoustic radiation pressure. High-density materials such as iridium (density 22.6 gcm(-3)) are stably levitated at room temperature with a soft reflector made of colloid as well as a rigid reflector supported by a spring. In addition, the containerless melting and solidification of binary In-Bi eutectic alloy (melting point 345.8 K) and ternary Ag-Cu-Ge eutectic alloy (melting point 812 K) are successfully achieved by applying the elastically supported reflector with the assistance of a laser beam.

Impact of Self-Interference on the Performance of Joint Partial RAKE Receiver and Adaptive Modulation

KAUST Repository

Nam, Sung Sik; Choi, Yungho; Alouini, Mohamed-Slim; Choi, Seyeong

2016-01-01

In this paper, we investigate the impact of self-interference on the performance of a joint partial RAKE (PRAKE) receiver and adaptive modulation over both independent and identically distributed and independent but non-identically distributed

Automatic synthesis of MEMS devices using self-adaptive hybrid metaheuristics

DEFF Research Database (Denmark)

Tutum, Cem Celal; Fan, Zhun

2011-01-01

- multaneous minimization of size and power input of a MEMS device, while investigating optimum geometrical conguration as the main concern. The major contribution of this paper is the application of self-adaptive memetic computing in MEMS design. An evolutionary multi-objective optimization (EMO) technique......, in particular non-dominated sorting genetic algorithm (NSGA-II), has been applied to- gether with a pattern recognition statistical tool, i.e. Principal Component Analysis (PCA), to nd multiple trade-o solutions in an ecient manner. Following this, a gradient- based local search, i.e. sequential quadratic...

Adaptive Randomization of Neratinib in Early Breast Cancer.

Science.gov (United States)

Park, John W; Liu, Minetta C; Yee, Douglas; Yau, Christina; van 't Veer, Laura J; Symmans, W Fraser; Paoloni, Melissa; Perlmutter, Jane; Hylton, Nola M; Hogarth, Michael; DeMichele, Angela; Buxton, Meredith B; Chien, A Jo; Wallace, Anne M; Boughey, Judy C; Haddad, Tufia C; Chui, Stephen Y; Kemmer, Kathleen A; Kaplan, Henry G; Isaacs, Claudine; Nanda, Rita; Tripathy, Debasish; Albain, Kathy S; Edmiston, Kirsten K; Elias, Anthony D; Northfelt, Donald W; Pusztai, Lajos; Moulder, Stacy L; Lang, Julie E; Viscusi, Rebecca K; Euhus, David M; Haley, Barbara B; Khan, Qamar J; Wood, William C; Melisko, Michelle; Schwab, Richard; Helsten, Teresa; Lyandres, Julia; Davis, Sarah E; Hirst, Gillian L; Sanil, Ashish; Esserman, Laura J; Berry, Donald A

2016-07-07

The heterogeneity of breast cancer makes identifying effective therapies challenging. The I-SPY 2 trial, a multicenter, adaptive phase 2 trial of neoadjuvant therapy for high-risk clinical stage II or III breast cancer, evaluated multiple new agents added to standard chemotherapy to assess the effects on rates of pathological complete response (i.e., absence of residual cancer in the breast or lymph nodes at the time of surgery). We used adaptive randomization to compare standard neoadjuvant chemotherapy plus the tyrosine kinase inhibitor neratinib with control. Eligible women were categorized according to eight biomarker subtypes on the basis of human epidermal growth factor receptor 2 (HER2) status, hormone-receptor status, and risk according to a 70-gene profile. Neratinib was evaluated against control with regard to 10 biomarker signatures (prospectively defined combinations of subtypes). The primary end point was pathological complete response. Volume changes on serial magnetic resonance imaging were used to assess the likelihood of such a response in each patient. Adaptive assignment to experimental groups within each disease subtype was based on Bayesian probabilities of the superiority of the treatment over control. Enrollment in the experimental group was stopped when the 85% Bayesian predictive probability of success in a confirmatory phase 3 trial of neoadjuvant therapy reached a prespecified threshold for any biomarker signature ("graduation"). Enrollment was stopped for futility if the probability fell to below 10% for every biomarker signature. Neratinib reached the prespecified efficacy threshold with regard to the HER2-positive, hormone-receptor-negative signature. Among patients with HER2-positive, hormone-receptor-negative cancer, the mean estimated rate of pathological complete response was 56% (95% Bayesian probability interval [PI], 37 to 73%) among 115 patients in the neratinib group, as compared with 33% among 78 controls (95% PI, 11 to 54

Effects on cognitive and clinical insight with the use of Guided Self-Determination in outpatients with schizophrenia: A randomized open trial.

Science.gov (United States)

Jørgensen, R; Licht, R W; Lysaker, P H; Munk-Jørgensen, P; Buck, K D; Jensen, S O W; Hansson, L; Zoffmann, V

2015-07-01

Poor insight has a negative impact on the outcome in schizophrenia; consequently, poor insight is a logical target for treatment. However, neither medication nor psychosocial interventions have been demonstrated to improve poor insight. A method originally designed for diabetes patients to improve their illness management, Guided Self-Determination (GSD), has been adapted for use in patients with schizophrenia (GSD-SZ). The purpose of this study was to investigate the effect on insight of GSD-SZ as a supplement to treatment as usual (TAU) as compared to TAU alone in outpatients diagnosed with schizophrenia. The design was an open randomized trial. The primary hypothesis was cognitive insight would improve in those patients who received GSD-SZ+TAU as assessed by the BCIS. We additionally explored whether the intervention led to changes in clinical insight, self-perceived recovery, self-esteem, social functioning and symptom severity. Assessments were conducted at baseline, and at 3-, 6- and 12-month follow-up. Analysis was based on the principles of intention to treat and potential confounders were taken into account through applying a multivariate approach. A total of 101 participants were randomized to GSD-SZ+TAU (n=50) or to TAU alone (n=51). No statistically significant differences were found on the cognitive insight. However, at 12-month follow-up, clinical insight (measured by G12 from the Positive and Negative Syndrome Scale), symptom severity, and social functioning had statistically significantly improved in the intervention group as compared to the control group. "Improving insight in patients diagnosed with schizophrenia", NCT01282307, http://clinicaltrials.gov/. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

The potential for induction of autoimmune disease by a randomly-mutated self-antigen

DEFF Research Database (Denmark)

Pedersen, Anders Elm

2007-01-01

-antigens can be immunogenic and lead to autoimmunity against wildtype self-antigens. In theory, modified self-antigens can arise by random errors and mutations during protein synthesis and would be recognized as foreign antigens by naïve B and T lymphocytes. Here, it is postulated that the initial auto......, a relation to an infectious disease is described, and it is thought that microbes can play a direct role in induction of autoimmunity, for instance by molecular mimicry or bystander activation of autoreactive T cells. In contrast, less attention has been given to the possibility that modified self......-antigen is not a germline self-antigen, but rather a mutated self-antigen. This mutated self-antigen might interfere with peripheral tolerance if presented to the immune system during an infection. The infection lead to bystander activation of naïve T and B cells with specificity for mutated self-antigen and this can lead...

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

Science.gov (United States)

Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

2016-07-12

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Application of Physiological Self-Regulation and Adaptive Task Allocation Techniques for Controlling Operator Hazardous States of Awareness

Science.gov (United States)

Prinzel, Lawrence J., III; Pope, Alan T.; Freeman, Frederick G.

2001-01-01

Prinzel, Hadley, Freeman, and Mikulka found that adaptive task allocation significantly enhanced performance only when used at the endpoints of the task workload continuum (i.e., very low or high workload), but that the technique degraded performance if invoked during other levels of task demand. These researchers suggested that other techniques should be used in conjunction with adaptive automation to help minimize the onset of hazardous states of awareness (HSA) and keep the operator 'in-the-loop.' The paper reports on such a technique that uses psychophysiological self-regulation to modulate the level of task engagement. Eighteen participants were assigned to three groups (self-regulation, false feedback, and control) and performed a compensatory tracking task that was cycled between three levels of task difficulty on the basis of the electroencephalogram (EEG) record. Those participants who had received self-regulation training performed significantly better and reported lower NASA-TLX scores than participants in the false feedback and control groups. Furthermore, the false feedback and control groups had significantly more task allocations resulting in return-to-manual performance decrements and higher EEG difference scores. Theoretical and practical implications of these results for adaptive automation are discussed.

A dynamically adaptive wavelet approach to stochastic computations based on polynomial chaos - capturing all scales of random modes on independent grids

International Nuclear Information System (INIS)

Ren Xiaoan; Wu Wenquan; Xanthis, Leonidas S.

2011-01-01

Highlights: → New approach for stochastic computations based on polynomial chaos. → Development of dynamically adaptive wavelet multiscale solver using space refinement. → Accurate capture of steep gradients and multiscale features in stochastic problems. → All scales of each random mode are captured on independent grids. → Numerical examples demonstrate the need for different space resolutions per mode. - Abstract: In stochastic computations, or uncertainty quantification methods, the spectral approach based on the polynomial chaos expansion in random space leads to a coupled system of deterministic equations for the coefficients of the expansion. The size of this system increases drastically when the number of independent random variables and/or order of polynomial chaos expansions increases. This is invariably the case for large scale simulations and/or problems involving steep gradients and other multiscale features; such features are variously reflected on each solution component or random/uncertainty mode requiring the development of adaptive methods for their accurate resolution. In this paper we propose a new approach for treating such problems based on a dynamically adaptive wavelet methodology involving space-refinement on physical space that allows all scales of each solution component to be refined independently of the rest. We exemplify this using the convection-diffusion model with random input data and present three numerical examples demonstrating the salient features of the proposed method. Thus we establish a new, elegant and flexible approach for stochastic problems with steep gradients and multiscale features based on polynomial chaos expansions.

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

Science.gov (United States)

Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

2017-01-01

To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

Predictors of Career Adaptability Skill among Higher Education Students in Nigeria

Directory of Open Access Journals (Sweden)

Amos Shaibu Ebenehi

2016-12-01

Full Text Available This paper examined predictors of career adaptability skill among higher  education students in Nigeria. A sample of 603 higher education students randomly selected from six colleges of education in Nigeria participated in this study.  A set of self-reported questionnaire was used for data collection, and multiple linear regression analysis was used to analyze the data.  Results indicated that 33.3% of career adaptability skill was explained by the model.  Four out of the five predictor variables significantly predicted career adaptability skill among higher education students in Nigeria.  Among the four predictors, career self-efficacy sources was the most statistically significant predictor of career adaptability skill among higher education students in Nigeria, followed by personal goal orientation, career future concern, and perceived social support respectively.  Vocational identity did not statistically predict career adaptability skill among higher education students in Nigeria.  The study suggested that similar study should be replicated in other parts of the world in view of the importance of career adaptability skill to the smooth transition of graduates from school to the labor market.  The study concluded by requesting stakeholders of higher institutions in Nigeria to provide career exploration database for the students, and encourage career intervention program in order to enhance career adaptability skill among the students.

Using Acceptance and Commitment Therapy to Increase Self-Compassion: A Randomized Controlled Trial.

Science.gov (United States)

Yadavaia, James E; Hayes, Steven C; Vilardaga, Roger

2014-10-01

Self-compassion has been shown to be related to several types of psychopathology, including traumatic stress, and has been shown to improve in response to various kinds of interventions. Current conceptualizations of self-compassion fit well with the psychological flexibility model, which underlies acceptance and commitment therapy (ACT). However, there has been no research on ACT interventions specifically aimed at self-compassion. This randomized trial therefore compared a 6-hour ACT-based workshop targeting self-compassion to a wait-list control. From pretreatment to 2-month follow-up, ACT was significantly superior to the control condition in self-compassion, general psychological distress, and anxiety. Process analyses revealed psychological flexibility to be a significant mediator of changes in self-compassion, general psychological distress, depression, anxiety, and stress. Exploratory moderation analyses revealed the intervention to be of more benefit in terms of depression, anxiety, and stress to those with greater trauma history.

Optimizing the data acquisition rate for a remotely controllable structural monitoring system with parallel operation and self-adaptive sampling

International Nuclear Information System (INIS)

Sheng, Wenjuan; Guo, Aihuang; Liu, Yang; Azmi, Asrul Izam; Peng, Gang-Ding

2011-01-01

We present a novel technique that optimizes the real-time remote monitoring and control of dispersed civil infrastructures. The monitoring system is based on fiber Bragg gating (FBG) sensors, and transfers data via Ethernet. This technique combines parallel operation and self-adaptive sampling to increase the data acquisition rate in remote controllable structural monitoring systems. The compact parallel operation mode is highly efficient at achieving the highest possible data acquisition rate for the FBG sensor based local data acquisition system. Self-adaptive sampling is introduced to continuously coordinate local acquisition and remote control for data acquisition rate optimization. Key issues which impact the operation of the whole system, such as the real-time data acquisition rate, data processing capability, and buffer usage, are investigated. The results show that, by introducing parallel operation and self-adaptive sampling, the data acquisition rate can be increased by several times without affecting the system operating performance on both local data acquisition and remote process control

A Self-adaptive Dynamic Evaluation Model for Diabetes Mellitus, Based on Evolutionary Strategies

Directory of Open Access Journals (Sweden)

An-Jiang Lu

2016-03-01

Full Text Available In order to evaluate diabetes mellitus objectively and accurately, this paper builds a self-adaptive dynamic evaluation model for diabetes mellitus, based on evolutionary strategies. First of all, on the basis of a formalized description of the evolutionary process of diabetes syndromes, using a state transition function, it judges whether a disease is evolutionary, through an excitation parameter. It then, provides evidence for the rebuilding of the evaluation index system. After that, by abstracting and rebuilding the composition of evaluation indexes, it makes use of a heuristic algorithm to determine the composition of the evolved evaluation index set of diabetes mellitus, It then, calculates the weight of each index in the evolved evaluation index set of diabetes mellitus by building a dependency matrix and realizes the self-adaptive dynamic evaluation of diabetes mellitus under an evolutionary environment. Using this evaluation model, it is possible to, quantify all kinds of diagnoses and treatment experiences of diabetes and finally to adopt ideal diagnoses and treatment measures for different patients with diabetics.

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

Science.gov (United States)

Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

2016-08-03

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Chronic pain self-management for older adults: a randomized controlled trial [ISRCTN11899548

Directory of Open Access Journals (Sweden)

Cain Kevin C

2004-07-01

Full Text Available Abstract Background Chronic pain is a common and frequently disabling problem in older adults. Clinical guidelines emphasize the need to use multimodal therapies to manage persistent pain in this population. Pain self-management training is a multimodal therapy that has been found to be effective in young to middle-aged adult samples. This training includes education about pain as well as instruction and practice in several management techniques, including relaxation, physical exercise, modification of negative thoughts, and goal setting. Few studies have examined the effectiveness of this therapy in older adult samples. Methods/Design This is a randomized, controlled trial to assess the effectiveness of a pain self-management training group intervention, as compared with an education-only control condition. Participants are recruited from retirement communities in the Pacific Northwest of the United States and must be 65 years or older and experience persistent, noncancer pain that limits their activities. The primary outcome is physical disability, as measured by the Roland-Morris Disability Questionnaire. Secondary outcomes are depression (Geriatric Depression Scale, pain intensity (Brief Pain Inventory, and pain-related interference with activities (Brief Pain Inventory. Randomization occurs by facility to minimize cross-contamination between groups. The target sample size is 273 enrolled, which assuming a 20% attrition rate at 12 months, will provide us with 84% power to detect a moderate effect size of .50 for the primary outcome. Discussion Few studies have investigated the effects of multimodal pain self-management training among older adults. This randomized controlled trial is designed to assess the efficacy of a pain self-management program that incorporates physical and psychosocial pain coping skills among adults in the mid-old to old-old range.

Low adolescent self-esteem leads to multiple interpersonal problems: a test a social-adaptation theory.

Science.gov (United States)

Kahle, L R; Kulka, R A; Klingel, D M

1980-09-01

This article reports the results of a study that annually monitored the self-esteem and interpersonal problems of over 100 boys during their sophomore, junior, and senior years of high school. Cross-lagged panel correlation differences show that low self-esteem leads to interpersonal problems in all three time lags when multiple interpersonal problems constitute the dependent variable but not when single interpersonal problem criteria constitute the dependent variable. These results are interpreted as supporting social-adaptation theory rather than self-perception theory. Implications for the conceptual status of personality variables as causal antecedents and for the assessment of individual differences are discussed.

Self-adaptive change detection in streaming data with non-stationary distribution

KAUST Repository

Zhang, Xiangliang

2010-01-01

Non-stationary distribution, in which the data distribution evolves over time, is a common issue in many application fields, e.g., intrusion detection and grid computing. Detecting the changes in massive streaming data with a non-stationary distribution helps to alarm the anomalies, to clean the noises, and to report the new patterns. In this paper, we employ a novel approach for detecting changes in streaming data with the purpose of improving the quality of modeling the data streams. Through observing the outliers, this approach of change detection uses a weighted standard deviation to monitor the evolution of the distribution of data streams. A cumulative statistical test, Page-Hinkley, is employed to collect the evidence of changes in distribution. The parameter used for reporting the changes is self-adaptively adjusted according to the distribution of data streams, rather than set by a fixed empirical value. The self-adaptability of the novel approach enhances the effectiveness of modeling data streams by timely catching the changes of distributions. We validated the approach on an online clustering framework with a benchmark KDDcup 1999 intrusion detection data set as well as with a real-world grid data set. The validation results demonstrate its better performance on achieving higher accuracy and lower percentage of outliers comparing to the other change detection approaches. © 2010 Springer-Verlag.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

Directory of Open Access Journals (Sweden)

Alexander Caicedo

2016-11-01

Full Text Available Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP, assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + _. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first three days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

Science.gov (United States)

Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

2016-01-01

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the

Holland's SDS Applied to Chinese College Students: A Revisit to Cross-Culture Adaptation

Science.gov (United States)

Kong, Jin; Xu, Yonghong Jade; Zhang, Hao

2016-01-01

In this study, data collected from 875 college freshman and sophomore students enrolled in a 4-year university in central China are used to examine the applicability and validity of a Chinese version of Holland's Self-Directed Search (SDS) that was adapted in the 1990s. The total sample was randomly divided into two groups. Data from the first…

An additive subspace preconditioning method for the iterative solution of some problems with extreme contrasts in coefficients

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe

2014-01-01

Roč. 22, č. 4 (2014), s. 289-310 ISSN 1570-2820 R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:68145535 Keywords : preconditioning * additive subspace * small eigenvalues Subject RIV: BA - General Mathematics Impact factor: 2.310, year: 2014 http://www.degruyter.com/view/j/jnma.2014.22.issue-4/jnma-2014-0013/jnma-2014-0013. xml

Cross-Cultural Adaptation and Psychometric Testing of the Brazilian Version of the Self-Care of Heart Failure Index Version 6.2

Science.gov (United States)

Ávila, Christiane Wahast; Riegel, Barbara; Pokorski, Simoni Chiarelli; Camey, Suzi; Silveira, Luana Claudia Jacoby; Rabelo-Silva, Eneida Rejane

2013-01-01

Objective. To adapt and evaluate the psychometric properties of the Brazilian version of the SCHFI v 6.2. Methods. With the approval of the original author, we conducted a complete cross-cultural adaptation of the instrument (translation, synthesis, back translation, synthesis of back translation, expert committee review, and pretesting). The adapted version was named Brazilian version of the self-care of heart failure index v 6.2. The psychometric properties assessed were face validity and content validity (by expert committee review), construct validity (convergent validity and confirmatory factor analysis), and reliability. Results. Face validity and content validity were indicative of semantic, idiomatic, experimental, and conceptual equivalence. Convergent validity was demonstrated by a significant though moderate correlation (r = −0.51) on comparison with equivalent question scores of the previously validated Brazilian European heart failure self-care behavior scale. Confirmatory factor analysis supported the original three-factor model as having the best fit, although similar results were obtained for inadequate fit indices. The reliability of the instrument, as expressed by Cronbach's alpha, was 0.40, 0.82, and 0.93 for the self-care maintenance, self-care management, and self-care confidence scales, respectively. Conclusion. The SCHFI v 6.2 was successfully adapted for use in Brazil. Nevertheless, further studies should be carried out to improve its psychometric properties. PMID:24163765

An adaptive angle-doppler compensation method for airborne bistatic radar based on PAST

Science.gov (United States)

Hang, Xu; Jun, Zhao

2018-05-01

Adaptive angle-Doppler compensation method extract the requisite information based on the data itself adaptively, thus avoiding the problem of performance degradation caused by inertia system error. However, this method requires estimation and egiendecomposition of sample covariance matrix, which has a high computational complexity and limits its real-time application. In this paper, an adaptive angle Doppler compensation method based on projection approximation subspace tracking (PAST) is studied. The method uses cyclic iterative processing to quickly estimate the positions of the spectral center of the maximum eigenvector of each range cell, and the computational burden of matrix estimation and eigen-decompositon is avoided, and then the spectral centers of all range cells is overlapped by two dimensional compensation. Simulation results show the proposed method can effectively reduce the no homogeneity of airborne bistatic radar, and its performance is similar to that of egien-decomposition algorithms, but the computation load is obviously reduced and easy to be realized.

Self-adaptive multimethod optimization applied to a tailored heating forging process

Science.gov (United States)

Baldan, M.; Steinberg, T.; Baake, E.

2018-05-01

The presented paper describes an innovative self-adaptive multi-objective optimization code. Investigation goals concern proving the superiority of this code compared to NGSA-II and applying it to an inductor’s design case study addressed to a “tailored” heating forging application. The choice of the frequency and the heating time are followed by the determination of the turns number and their positions. Finally, a straightforward optimization is performed in order to minimize energy consumption using “optimal control”.

Randomized Trial of a Calling-Infused Career Workshop Incorporating Counselor Self-Disclosure

Science.gov (United States)

Dik, Bryan J.; Steger, Michael F.

2008-01-01

A randomized controlled trial was used to test (1) the efficacy of a two-session career development workshop for college student participants; (2) the effect of counselor self-disclosure on outcomes; and (3) the effect of infusing calling and vocation concepts on outcomes. Both standard (person-environment fit) and calling/vocation-infused…

Cross-Cultural Adaptation of the Male Genital Self-Image Scale in Iranian Men.

Science.gov (United States)

Saffari, Mohsen; Pakpour, Amir H; Burri, Andrea

2016-03-01

Certain sexual health problems in men can be attributed to genital self-image. Therefore, a culturally adapted version of a Male Genital Self-Image Scale (MGSIS) could help health professionals understand this concept and its associated correlates. To translate the original English version of the MGSIS into Persian and to assess the psychometric properties of this culturally adapted version (MGSIS-I) for use in Iranian men. In total, 1,784 men were recruited for this cross-sectional study. Backward and forward translations of the MGSIS were used to produce the culturally adapted version. Reliability of the MGSIS-I was assessed using Cronbach α and intra-class correlation coefficients. Divergent and convergent validities were examined using Pearson correlation and known-group validity was assessed in subgroups of participants with different sociodemographic statuses. Factor validity of the scale was investigated using exploratory and confirmatory factor analyses. Demographic information, the International Index of Erectile Function, the Body Appreciation Scale, the Rosenberg Self-Esteem Scale, and the MGSIS. Mean age of participants was 38.13 years (SD = 11.45) and all men were married. Cronbach α of the MGSIS-I was 0.89 and interclass correlation coefficients ranged from 0.70 to 0.94. Significant correlations were found between the MGSIS-I and the International Index of Erectile Function (P scale with non-similar scales was lower than with similar scale (confirming convergent and divergent validity). The scale could differentiate between subgroups in age, smoking status, and income (known-group validity). A single-factor solution that explained 70% variance of the scale was explored using exploratory factor analysis (confirming uni-dimensionality); confirmatory factor analysis indicated better fitness for the five-item version than the seven-item version of the MGSIS-I (root mean square error of approximation = 0.05, comparative fit index > 1.00 vs root mean

A principle of organization which facilitates broad Lamarckian-like adaptations by improvisation.

Science.gov (United States)

Soen, Yoav; Knafo, Maor; Elgart, Michael

2015-12-02

During the lifetime of an organism, every individual encounters many combinations of diverse changes in the somatic genome, epigenome and microbiome. This gives rise to many novel combinations of internal failures which are unique to each individual. How any individual can tolerate this high load of new, individual-specific scenarios of failure is not clear. While stress-induced plasticity and hidden variation have been proposed as potential mechanisms of tolerance, the main conceptual problem remains unaddressed, namely: how largely non-beneficial random variation can be rapidly and safely organized into net benefits to every individual. We propose an organizational principle which explains how every individual can alleviate a high load of novel stressful scenarios using many random variations in flexible and inherently less harmful traits. Random changes which happen to reduce stress, benefit the organism and decrease the drive for additional changes. This adaptation (termed 'Adaptive Improvisation') can be further enhanced, propagated, stabilized and memorized when beneficial changes reinforce themselves by auto-regulatory mechanisms. This principle implicates stress not only in driving diverse variations in cells tissues and organs, but also in organizing these variations into adaptive outcomes. Specific (but not exclusive) examples include stress reduction by rapid exchange of mobile genetic elements (or exosomes) in unicellular, and rapid changes in the symbiotic microorganisms of animals. In all cases, adaptive changes can be transmitted across generations, allowing rapid improvement and assimilation in a few generations. We provide testable predictions derived from the hypothesis. The hypothesis raises a critical, but thus far overlooked adaptation problem and explains how random variation can self-organize to confer a wide range of individual-specific adaptations beyond the existing outcomes of natural selection. It portrays gene regulation as an

A randomized control trial of the effect of yoga on Gunas (personality) and Self esteem in normal healthy volunteers.

Science.gov (United States)

Deshpande, Sudheer; Nagendra, H R; Nagarathna, Raghuram

2009-01-01

To study the efficacy of yoga on Gunas (personality) and self esteem in normal adults through a randomized comparative study. Of the 1228 persons who attended motivational lectures, 226 subjects aged 18-71 years, of both sexes, who satisfied the inclusion and exclusion criteria, and who consented to participate in the study were randomly allocated into two groups. The Yoga (Y) group practised an integrated yoga module that included asanas, pranayama, meditation, notional correction, and devotional sessions. The comparison group practised mild to moderate physical exercises (PE). Both groups had supervised practices for one hour daily, six days a week, for eight weeks. Guna (personality) was assessed before and after eight weeks using the self-administered "The 'Gita" Inventory of Personality" (GIN) to assess Sattva, Rajas, and Tamas. Self esteem in terms of competency (COM), global self esteem (GSE), moral and self esteem (MSE), social esteem (SET), family self esteem (FSE), body and physical appearance (BPA), and the lie scale (LIS) were assessed using the self esteem questionnaire (SEQ). The baseline scores for all domains for both the groups did not differ significantly (P > 0.05 independent samples t-test). There were significant pre-post improvements in all domains in both groups (P self esteem in the Y group is greater than for the PE group in three out of seven domains. This randomized controlled study has shown the influence of Yoga on Gunas and self esteem in comparison to physical exercise.

Random SU(2) invariant tensors

Science.gov (United States)

Li, Youning; Han, Muxin; Ruan, Dong; Zeng, Bei

2018-04-01

SU(2) invariant tensors are states in the (local) SU(2) tensor product representation but invariant under the global group action. They are of importance in the study of loop quantum gravity. A random tensor is an ensemble of tensor states. An average over the ensemble is carried out when computing any physical quantities. The random tensor exhibits a phenomenon known as ‘concentration of measure’, which states that for any bipartition the average value of entanglement entropy of its reduced density matrix is asymptotically the maximal possible as the local dimensions go to infinity. We show that this phenomenon is also true when the average is over the SU(2) invariant subspace instead of the entire space for rank-n tensors in general. It is shown in our earlier work Li et al (2017 New J. Phys. 19 063029) that the subleading correction of the entanglement entropy has a mild logarithmic divergence when n  =  4. In this paper, we show that for n  >  4 the subleading correction is not divergent but a finite number. In some special situation, the number could be even smaller than 1/2, which is the subleading correction of random state over the entire Hilbert space of tensors.

Quantum Gate Operations in Decoherence-Free Subspace with Superconducting Charge Qubits inside a Cavity

International Nuclear Information System (INIS)

Yi-Min, Wang; Yan-Li, Zhou; Lin-Mei, Liang; Cheng-Zu, Li

2009-01-01

We propose a feasible scheme to achieve universal quantum gate operations in decoherence-free subspace with superconducting charge qubits placed in a microwave cavity. Single-logic-qubit gates can be realized with cavity assisted interaction, which possesses the advantages of unconventional geometric gate operation. The two-logic-qubit controlled-phase gate between subsystems can be constructed with the help of a variable electrostatic transformer. The collective decoherence can be successfully avoided in our well-designed system. Moreover, GHZ state for logical qubits can also be easily produced in this system

Self-compassion training for binge eating disorder: a pilot randomized controlled trial.

Science.gov (United States)

Kelly, Allison C; Carter, Jacqueline C

2015-09-01

The present pilot study sought to compare a compassion-focused therapy (CFT)-based self-help intervention for binge eating disorder (BED) to a behaviourally based intervention. Forty-one individuals with BED were randomly assigned to 3 weeks of food planning plus self-compassion exercises; food planning plus behavioural strategies; or a wait-list control condition. Participants completed weekly measures of binge eating and self-compassion; pre- and post-intervention measures of eating disorder pathology and depressive symptoms; and a baseline measure assessing fear of self-compassion. Results showed that: (1) perceived credibility, expectancy, and compliance did not differ between the two interventions; (2) both interventions reduced weekly binge days more than the control condition; (3) the self-compassion intervention reduced global eating disorder pathology, eating concerns, and weight concerns more than the other conditions; (4) the self-compassion intervention increased self-compassion more than the other conditions; and (5) participants low in fear of self-compassion derived significantly more benefits from the self-compassion intervention than those high in fear of self-compassion. Findings offer preliminary support for the usefulness of CFT-based interventions for BED sufferers. Results also suggest that for individuals to benefit from self-compassion training, assessing and lowering fear of self-compassion will be crucial. Individuals with BED perceive self-compassion training self-help interventions, derived from CFT, to be as credible and as likely to help as behaviourally based interventions. The cultivation of self-compassion may be an effective approach for reducing binge eating, and eating, and weight concerns in individuals with BED. Teaching individuals with BED CFT-based self-help exercises may increase their self-compassion levels over a short period of time. It may be important for clinicians to assess and target clients' fear of self

Cross-cultural adaptation and validation of the Condom Self-Efficacy Scale: application to Brazilian adolescents and young adults

Directory of Open Access Journals (Sweden)

Carla Suellen Pires de Sousa

2018-01-01

Full Text Available ABSTRACT Objective: translate and adapt the Condom Self-Efficacy Scale to Portuguese in the Brazilian context. The scale originated in the United States and measures self-efficacy in condom use. Method: methodological study in two phases: translation, cross-cultural adaptation and verification of psychometric properties. The translation and adaptation process involved four translators, one mediator of the synthesis and five health professionals. The content validity was verified using the Content Validation Index, based on 22 experts’ judgments. Forty subjects participated in the pretest, who contributed to the understanding of the scale items. The scale was applied to 209 students between 13 and 26 years of age from a school affiliated with the state-owned educational network. The reliability was analyzed by means of Cronbach’s alpha. Results: the Portuguese version of the scale obtained a Cronbach’s alpha coefficient of 0.85 and the total mean score was 68.1 points. A statistically significant relation was found between the total scale and the variables not having children (p= 0.038, condom use (p= 0.008 and condom use with fixed partner (p=0.036. Conclusion: the Brazilian version of the Condom Self-Efficacy Scale is a valid and reliable tool to verify the self-efficacy in condom use among adolescents and young adults.