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....
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 ...
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).
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
Random matrix improved subspace clustering
Couillet, Romain
2017-03-06
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.
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...
Innovation Pursuit: A New Approach to Subspace Clustering
Rahmani, Mostafa; Atia, George K.
2017-12-01
In subspace clustering, a group of data points belonging to a union of subspaces are assigned membership to their respective subspaces. This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties. We present two frameworks in which the idea of innovation pursuit is used to distinguish the subspaces. Underlying the first framework is an iterative method that finds the subspaces consecutively by solving a series of simple linear optimization problems, each searching for a direction of innovation in the span of the data potentially orthogonal to all subspaces except for the one to be identified in one step of the algorithm. A detailed mathematical analysis is provided establishing sufficient conditions for iPursuit to correctly cluster the data. The proposed approach can provably yield exact clustering even when the subspaces have significant intersections. It is shown that the complexity of the iterative approach scales only linearly in the number of data points and subspaces, and quadratically in the dimension of the subspaces. The second framework integrates iPursuit with spectral clustering to yield a new variant of spectral-clustering-based algorithms. The numerical simulations with both real and synthetic data demonstrate that iPursuit can often outperform the state-of-the-art subspace clustering algorithms, more so for subspaces with significant intersections, and that it significantly improves the state-of-the-art result for subspace-segmentation-based face clustering.
N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering
Directory of Open Access Journals (Sweden)
Farman Ullah
2014-01-01
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.
Robust auto-weighted multi-view subspace clustering with common subspace representation matrix.
Directory of Open Access Journals (Sweden)
Wenzhang Zhuge
Full Text Available In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research. Previous multi-view subspace methods aim to learn multiple subspace representation matrices simultaneously and these learning task for different views are treated equally. After obtaining representation matrices, they stack up the learned representation matrices as the common underlying subspace structure. However, for many problems, the importance of sources and the importance of features in one source both can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel method called Robust Auto-weighted Multi-view Subspace Clustering (RAMSC. In our method, the weight for both the sources and features can be learned automatically via utilizing a novel trick and introducing a sparse norm. More importantly, the objective of our method is a common representation matrix which directly reflects the common underlying subspace structure. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergency. Extensive experimental results on five benchmark multi-view datasets well demonstrate that the proposed method consistently outperforms the state-of-the-art methods.
Robust auto-weighted multi-view subspace clustering with common subspace representation matrix
National Research Council Canada - National Science Library
Wenzhang Zhuge; Chenping Hou; Yuanyuan Jiao; Jia Yue; Hong Tao; Dongyun Yi
2017-01-01
.... However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data...
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 co...... and create a common baseline for future developments and comparable evaluations in the field. For repeatability, all implementations, data sets and evaluation measures are available on our website....
A Geometric Analysis of Subspace Clustering with Outliers
Soltanolkotabi, Mahdi
2011-01-01
This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named {\\em sparse subspace clustering} (SSC) \\cite{Elhamifar09}, which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretica...
Linear-time subspace clustering via bipartite graph modeling.
Adler, Amir; Elad, Michael; Hel-Or, Yacov
2015-10-01
We present a linear-time subspace clustering approach that combines sparse representations and bipartite graph modeling. The signals are modeled as drawn from a union of low-dimensional subspaces, and each signal is represented by a sparse combination of basis elements, termed atoms, which form the columns of a dictionary matrix. The sparse representation coefficients are arranged in a sparse affinity matrix, which defines a bipartite graph of two disjoint sets: 1) atoms and 2) signals. Subspace clustering is obtained by applying low-complexity spectral bipartite graph clustering that exploits the small number of atoms for complexity reduction. The complexity of the proposed approach is linear in the number of signals, thus it can rapidly cluster very large data collections. Performance evaluation of face clustering and temporal video segmentation demonstrates comparable clustering accuracies to state-of-the-art at a significantly lower computational load.
DENSITY CONSCIOUS SUBSPACE CLUSTERING USING ITL DATA STRUCTURE
Directory of Open Access Journals (Sweden)
C. Palanisamy
2011-01-01
Full Text Available Most of the subspace clustering algorithms uses monotonicity property to generate higher dimensional subspaces. But this property is not applicable here since different subspace cardinalities have varying densities i.e., if a k-dimensional unit is dense, any (k-1 dimensional projection of this unit may not be dense. So in DENCOS a mechanism to compute upper bounds of region densities to constrain the search of dense regions is devised, where the regions whose density upper bounds are lower than the density thresholds will be pruned away in identifying the dense regions. They compute the region density upper bounds by utilizing a data structure, DFP-tree to store the summarized information of the dense regions. DFP-Tree employs FP-Growth algorithm and builds an FP-Tree based on the prefix tree concept and uses it during the entire subspace identification process. This method performs repeated horizontal traversals of the data to generate relevant subspaces which is time consuming. To reduce the time complexity, we employ ITL data structure to build Density Conscious ITL (DITL tree to be used in the entire subspace identification process. ITL reduces the cost by scanning the database only once, by significantly reducing the horizontal traversals of the database. The algorithm is evaluated through experiments on a collection of benchmark data sets datasets. Experimental results have shown favorable performance compared with other popular clustering algorithms.
LogDet Rank Minimization with Application to Subspace Clustering.
Kang, Zhao; Peng, Chong; Cheng, Jie; Cheng, Qiang
2015-01-01
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.
Generating Multiple Alternative Clusterings Via Globally Optimal Subspaces
DEFF Research Database (Denmark)
Dang, Xuan-Hong; Bailey, James
2014-01-01
Clustering analysis is important for exploring complex datasets. Alternative clustering analysis is an emerging subfield involving techniques for the generation of multiple different clusterings, allowing the data to be viewed from different perspectives. We present two new algorithms...... for alternative clustering generation. A distinctive feature of our algorithms is their principled formulation of an objective function, facilitating the discovery of a subspace satisfying natural quality and orthogonality criteria. The first algorithm is a regularization of the Principal Components analysis...... method, whereas the second is a regularization of graph-based dimension reduction. In both cases, we demonstrate a globally optimum subspace solution can be computed. Experimental evaluation shows our techniques are able to equal or outperform a range of existing methods....
Genome classification by gene distribution: An overlapping subspace clustering approach
Directory of Open Access Journals (Sweden)
Halgamuge Saman K
2008-04-01
Full Text Available Abstract Background Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical example. One recent approach that addresses this problem is the unsupervised clustering of genomes based on gene order and genome position, which helps to reveal species relationships that may not be apparent from traditional phylogenetic methods. Results We propose the use of an overlapping subspace clustering algorithm for such genome classification problems. The advantage of subspace clustering over traditional clustering is that it can associate clusters with gene arrangement patterns, preserving genomic information in the clusters produced. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome, such as those acquired from different species via horizontal gene transfers. The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm; we extended a generic subspace clustering algorithm called HARP to incorporate overlapping capability. The proposed algorithm was assessed and applied on bacteriophage genomes. The phage grouping results are consistent overall with the Phage Proteomic Tree and showed common genomic characteristics among the TP901-like, Sfi21-like and sk1-like phage groups. Among 441 phage genomes, we identified four significantly conserved distribution patterns structured by the terminase, portal, integrase, holin and lysin genes. We also observed a subgroup of Sfi21-like phages comprising a distinctive divergent genome organization and identified nine new phage members to the Sfi21-like genus: Staphylococcus 71, phiPVL108, Listeria A118, 2389, Lactobacillus phi AT3, A2
Scalable rendering on PC clusters
Energy Technology Data Exchange (ETDEWEB)
WYLIE,BRIAN N.; LEWIS,VASILY; SHIRLEY,DAVID NOYES; PAVLAKOS,CONSTANTINE
2000-04-25
This case study presents initial results from research targeted at the development of cost-effective scalable visualization and rendering technologies. The implementations of two 3D graphics libraries based on the popular sort-last and sort-middle parallel rendering techniques are discussed. An important goal of these implementations is to provide scalable rendering capability for extremely large datasets (>> 5 million polygons). Applications can use these libraries for either run-time visualization, by linking to an existing parallel simulation, or for traditional post-processing by linking to an interactive display program. The use of parallel, hardware-accelerated rendering on commodity hardware is leveraged to achieve high performance. Current performance results show that, using current hardware (a small 16-node cluster), they can utilize up to 85% of the aggregate graphics performance and achieve rendering rates in excess of 20 million polygons/second using OpenGL{reg_sign} with lighting, Gouraud shading, and individually specified triangles (not t-stripped).
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.
N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.
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.
Peng, Xi; Tang, Huajin; Zhang, Lei; Yi, Zhang; Xiao, Shijie
2016-12-01
Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph, which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and l2 -norm-based representation, and have achieved the state-of-the-art performance. However, these methods have suffered from the following two limitations. First, the time complexities of these methods are at least proportional to the cube of the data size, which make those methods inefficient for solving the large-scale problems. Second, they cannot cope with the out-of-sample data that are not used to construct the similarity graph. To cluster each out-of-sample datum, the methods have to recalculate the similarity graph and the cluster membership of the whole data set. In this paper, we propose a unified framework that makes the representation-based subspace clustering algorithms feasible to cluster both the out-of-sample and the large-scale data. Under our framework, the large-scale problem is tackled by converting it as the out-of-sample problem in the manner of sampling, clustering, coding, and classifying. Furthermore, we give an estimation for the error bounds by treating each subspace as a point in a hyperspace. Extensive experimental results on various benchmark data sets show that our methods outperform several recently proposed scalable methods in clustering a large-scale data set.
wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests
Directory of Open Access Journals (Sweden)
He Zhao
2017-03-01
Full Text Available We describe a parallel implementation in R of the weighted subspace random forest algorithm (Xu, Huang, Williams, Wang, and Ye 2012 available as the wsrf package. A novel variable weighting method is used for variable subspace selection in place of the traditional approach of random variable sampling. This new approach is particularly useful in building models for high dimensional data - often consisting of thousands of variables. Parallel computation is used to take advantage of multi-core machines and clusters of machines to build random forest models from high dimensional data in considerably shorter times. A series of experiments presented in this paper demonstrates that wsrf is faster than existing packages whilst retaining and often improving on the classification performance, particularly for high dimensional data.
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
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....
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...
Sparse subspace clustering for data with missing entries and high-rank matrix completion.
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.
Directory of Open Access Journals (Sweden)
Y. Xu
2016-06-01
Full Text Available This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM. The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.
Xu, Y.; Tuttas, S.; Heogner, L.; Stilla, U.
2016-06-01
This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.
Scalable Cluster-based Routing in Large Wireless Sensor Networks
Jiandong Li; Xuelian Cai; Jin Yang; Lina Zhu
2012-01-01
Large control overhead is the leading factor limiting the scalability of wireless sensor networks (WSNs). Clustering network nodes is an efficient solution, and Passive Clustering (PC) is one of the most efficient clustering methods. In this letter, we propose an improved PC-based route building scheme, named Route Reply (RREP) Broadcast with Passive Clustering (in short RBPC). Through broadcasting RREP packets on an expanding ring to build route, sensor nodes cache their route to the sink no...
Fan, Jicong; Tian, Zhaoyang; Zhao, Mingbo; Chow, Tommy W S
2018-02-02
The scalability of low-rank representation (LRR) to large-scale data is still a major research issue, because it is extremely time-consuming to solve singular value decomposition (SVD) in each optimization iteration especially for large matrices. Several methods were proposed to speed up LRR, but they are still computationally heavy, and the overall representation results were also found degenerated. In this paper, a novel method, called accelerated LRR (ALRR) is proposed for large-scale data. The proposed accelerated method integrates matrix factorization with nuclear-norm minimization to find a low-rank representation. In our proposed method, the large square matrix of representation coefficients is transformed into a significantly smaller square matrix, on which SVD can be efficiently implemented. The size of the transformed matrix is not related to the number of data points and the optimization of ALRR is linear with the number of data points. The proposed ALRR is convex, accurate, robust, and efficient for large-scale data. In this paper, ALRR is compared with state-of-the-art in subspace clustering and semi-supervised classification on real image datasets. The obtained results verify the effectiveness and superiority of the proposed ALRR method. Copyright © 2018 Elsevier Ltd. All rights reserved.
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.
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.
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.
Scalable Parallel Density-based Clustering and Applications
Patwary, Mostofa Ali
2014-04-01
Recently, density-based clustering algorithms (DBSCAN and OPTICS) have gotten significant attention of the scientific community due to their unique capability of discovering arbitrary shaped clusters and eliminating noise data. These algorithms have several applications, which require high performance computing, including finding halos and subhalos (clusters) from massive cosmology data in astrophysics, analyzing satellite images, X-ray crystallography, and anomaly detection. However, parallelization of these algorithms are extremely challenging as they exhibit inherent sequential data access order, unbalanced workload resulting in low parallel efficiency. To break the data access sequentiality and to achieve high parallelism, we develop new parallel algorithms, both for DBSCAN and OPTICS, designed using graph algorithmic techniques. For example, our parallel DBSCAN algorithm exploits the similarities between DBSCAN and computing connected components. Using datasets containing up to a billion floating point numbers, we show that our parallel density-based clustering algorithms significantly outperform the existing algorithms, achieving speedups up to 27.5 on 40 cores on shared memory architecture and speedups up to 5,765 using 8,192 cores on distributed memory architecture. In our experiments, we found that while achieving the scalability, our algorithms produce clustering results with comparable quality to the classical algorithms.
Radjavi, Heydar
2003-01-01
This broad survey spans a wealth of studies on invariant subspaces, focusing on operators on separable Hilbert space. Largely self-contained, it requires only a working knowledge of measure theory, complex analysis, and elementary functional analysis. Subjects include normal operators, analytic functions of operators, shift operators, examples of invariant subspace lattices, compact operators, and the existence of invariant and hyperinvariant subspaces. Additional chapters cover certain results on von Neumann algebras, transitive operator algebras, algebras associated with invariant subspaces,
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 ava...
Applications of a new subspace clustering algorithm (COSA) in medical systems biology
Damian, D.; Orešič, M.; Verheij, E.; Meulman, J.; Friedman, J.; Adourian, A.; Morel, N.; Smilde, A.; Greef, J.
2007-01-01
A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1-25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical
XGet: a highly scalable and efficient file transfer tool for clusters
Energy Technology Data Exchange (ETDEWEB)
Greenberg, Hugh [Los Alamos National Laboratory; Ionkov, Latchesar [Los Alamos National Laboratory; Minnich, Ronald [SNL
2008-01-01
As clusters rapidly grow in size, transferring files between nodes can no longer be solved by the traditional transfer utilities due to their inherent lack of scalability. In this paper, we describe a new file transfer utility called XGet, which was designed to address the scalability problem of standard tools. We compared XGet against four transfer tools: Bittorrent, Rsync, TFTP, and Udpcast and our results show that XGet's performance is superior to the these utilities in many cases.
Scalable Prediction of Energy Consumption using Incremental Time Series Clustering
Energy Technology Data Exchange (ETDEWEB)
Simmhan, Yogesh; Noor, Muhammad Usman
2013-10-09
Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.
DEFF Research Database (Denmark)
Knudsen, Torben
2002-01-01
Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....
Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections
Directory of Open Access Journals (Sweden)
Vahid Emamian
2003-03-01
Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.
A scalable and practical one-pass clustering algorithm for recommender system
Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali
2015-12-01
KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.
The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware
Energy Technology Data Exchange (ETDEWEB)
Tierney, Brian L; Vallentin, Matthias; Sommer, Robin; Lee, Jason; Leres, Craig; Paxson, Vern; Tierney, Brian
2007-09-19
In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.
CA-tree: a hierarchical structure for efficient and scalable coassociation-based cluster ensembles.
Wang, Tsaipei
2011-06-01
Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N. In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy.
Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob
2013-01-01
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.
Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob
2013-01-01
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992
Unsupervised spike sorting based on discriminative subspace learning.
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.
The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces.
Wang, Bing; Mueller, Klaus
2018-02-01
Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface while using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, keep their bearings, and return to interesting subspaces and views. Both trackball and trail map are each embedded into a word cloud of attribute labels which aid in navigation. We demonstrate our system via several use cases in a diverse set of application areas-cluster analysis and refinement, information discovery, and supervised training of classifiers. We also report on a user study that evaluates the usability of the various interactions our system provides.
Rastogi, Richa; Londhe, Ashutosh; Srivastava, Abhishek; Sirasala, Kirannmayi M.; Khonde, Kiran
2017-03-01
In this article, a new scalable 3D Kirchhoff depth migration algorithm is presented on state of the art multicore CPU based cluster. Parallelization of 3D Kirchhoff depth migration is challenging due to its high demand of compute time, memory, storage and I/O along with the need of their effective management. The most resource intensive modules of the algorithm are traveltime calculations and migration summation which exhibit an inherent trade off between compute time and other resources. The parallelization strategy of the algorithm largely depends on the storage of calculated traveltimes and its feeding mechanism to the migration process. The presented work is an extension of our previous work, wherein a 3D Kirchhoff depth migration application for multicore CPU based parallel system had been developed. Recently, we have worked on improving parallel performance of this application by re-designing the parallelization approach. The new algorithm is capable to efficiently migrate both prestack and poststack 3D data. It exhibits flexibility for migrating large number of traces within the available node memory and with minimal requirement of storage, I/O and inter-node communication. The resultant application is tested using 3D Overthrust data on PARAM Yuva II, which is a Xeon E5-2670 based multicore CPU cluster with 16 cores/node and 64 GB shared memory. Parallel performance of the algorithm is studied using different numerical experiments and the scalability results show striking improvement over its previous version. An impressive 49.05X speedup with 76.64% efficiency is achieved for 3D prestack data and 32.00X speedup with 50.00% efficiency for 3D poststack data, using 64 nodes. The results also demonstrate the effectiveness and robustness of the improved algorithm with high scalability and efficiency on a multicore CPU cluster.
Subspace methods for pattern recognition in intelligent environment
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.
Enhancing Scalability and Efficiency of the TOUGH2_MP for LinuxClusters
Energy Technology Data Exchange (ETDEWEB)
Zhang, Keni; Wu, Yu-Shu
2006-04-17
TOUGH2{_}MP, the parallel version TOUGH2 code, has been enhanced by implementing more efficient communication schemes. This enhancement is achieved through reducing the amount of small-size messages and the volume of large messages. The message exchange speed is further improved by using non-blocking communications for both linear and nonlinear iterations. In addition, we have modified the AZTEC parallel linear-equation solver to nonblocking communication. Through the improvement of code structuring and bug fixing, the new version code is now more stable, while demonstrating similar or even better nonlinear iteration converging speed than the original TOUGH2 code. As a result, the new version of TOUGH2{_}MP is improved significantly in its efficiency. In this paper, the scalability and efficiency of the parallel code are demonstrated by solving two large-scale problems. The testing results indicate that speedup of the code may depend on both problem size and complexity. In general, the code has excellent scalability in memory requirement as well as computing time.
Swarm v2: highly-scalable and high-resolution amplicon clustering
Directory of Open Access Journals (Sweden)
Frédéric Mahé
2015-12-01
Full Text Available Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs, free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d, followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1 a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2 the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.
DEFF Research Database (Denmark)
Vissing, S.; Hededal, O.
An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m-dimensional sub......An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m......-dimensional subspace in order to establish and solve a symmetric generalized eigenvalue problem in the subspace. The algorithm is described in pseudo code and implemented in the C programming language for lower triangular matrices A and B. The implementation includes procedures for selecting initial iteration vectors...
Scalable fault tolerant algorithms for linear-scaling coupled-cluster electronic structure methods.
Energy Technology Data Exchange (ETDEWEB)
Leininger, Matthew L.; Nielsen, Ida Marie B.; Janssen, Curtis L.
2004-10-01
By means of coupled-cluster theory, molecular properties can be computed with an accuracy often exceeding that of experiment. The high-degree polynomial scaling of the coupled-cluster method, however, remains a major obstacle in the accurate theoretical treatment of mainstream chemical problems, despite tremendous progress in computer architectures. Although it has long been recognized that this super-linear scaling is non-physical, the development of efficient reduced-scaling algorithms for massively parallel computers has not been realized. We here present a locally correlated, reduced-scaling, massively parallel coupled-cluster algorithm. A sparse data representation for handling distributed, sparse multidimensional arrays has been implemented along with a set of generalized contraction routines capable of handling such arrays. The parallel implementation entails a coarse-grained parallelization, reducing interprocessor communication and distributing the largest data arrays but replicating as many arrays as possible without introducing memory bottlenecks. The performance of the algorithm is illustrated by several series of runs for glycine chains using a Linux cluster with an InfiniBand interconnect.
BlueSNP: R package for highly scalable genome-wide association studies using Hadoop clusters.
Huang, Hailiang; Tata, Sandeep; Prill, Robert J
2013-01-01
Computational workloads for genome-wide association studies (GWAS) are growing in scale and complexity outpacing the capabilities of single-threaded software designed for personal computers. The BlueSNP R package implements GWAS statistical tests in the R programming language and executes the calculations across computer clusters configured with Apache Hadoop, a de facto standard framework for distributed data processing using the MapReduce formalism. BlueSNP makes computationally intensive analyses, such as estimating empirical p-values via data permutation, and searching for expression quantitative trait loci over thousands of genes, feasible for large genotype-phenotype datasets. http://github.com/ibm-bioinformatics/bluesnp
Understanding Stochastic Subspace Identification
DEFF Research Database (Denmark)
Brincker, Rune; Andersen, Palle
2006-01-01
The data driven Stochastic Subspace Identification techniques is considered to be the most powerful class of the known identification techniques for natural input modal analysis in the time domain. However, the techniques involves several steps of "mysterious mathematics" that is difficult...... to follow and to understand for people with a classical background in structural dynamics. Also the connection to the classical correlation driven time domain techniques is not well established. The purpose of this paper is to explain the different steps in the SSI techniques of importance for modal...
Forecasting Using Random Subspace Methods
T. Boot (Tom); D. Nibbering (Didier)
2016-01-01
textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two
Subspace Detectors: Efficient Implementation
Energy Technology Data Exchange (ETDEWEB)
Harris, D B; Paik, T
2006-07-26
The optimum detector for a known signal in white Gaussian background noise is the matched filter, also known as a correlation detector [Van Trees, 1968]. Correlation detectors offer exquisite sensitivity (high probability of detection at a fixed false alarm rate), but require perfect knowledge of the signal. The sensitivity of correlation detectors is increased by the availability of multichannel data, something common in seismic applications due to the prevalence of three-component stations and arrays. When the signal is imperfectly known, an extension of the correlation detector, the subspace detector, may be able to capture much of the performance of a matched filter [Harris, 2006]. In order to apply a subspace detector, the signal to be detected must be known to lie in a signal subspace of dimension d {ge} 1, which is defined by a set of d linearly-independent basis waveforms. The basis is constructed to span the range of signals anticipated to be emitted by a source of interest. Correlation detectors operate by computing a running correlation coefficient between a template waveform (the signal to be detected) and the data from a window sliding continuously along a data stream. The template waveform and the continuous data stream may be multichannel, as would be true for a three-component seismic station or an array. In such cases, the appropriate correlation operation computes the individual correlations channel-for-channel and sums the result (Figure 1). Both the waveform matching that occurs when a target signal is present and the cross-channel stacking provide processing gain. For a three-component station processing gain occurs from matching the time-history of the signals and their polarization structure. The projection operation that is at the heart of the subspace detector can be expensive to compute if implemented in a straightforward manner, i.e. with direct-form convolutions. The purpose of this report is to indicate how the projection can be
Energy Technology Data Exchange (ETDEWEB)
Peng, Bo [William R. Wiley Environmental; Kowalski, Karol [William R. Wiley Environmental
2017-08-11
The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.
Peng, Bo; Kowalski, Karol
2017-09-12
The representation and storage of two-electron integral tensors are vital in large-scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this work, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of the atomic basis set, Nb, ranging from ∼100 up to ∼2,000, the observed numerical scaling of our implementation shows [Formula: see text] versus [Formula: see text] cost of performing single CD on the two-electron integral tensor in most of the other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic orbital (AO) two-electron integral tensor from [Formula: see text] to [Formula: see text] with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled cluster formalism employing single and double excitations (CCSD) on several benchmark systems including the C60 molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10-4 to 10-3 to give acceptable compromise between efficiency and accuracy.
Shape analysis with subspace symmetries
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).
2011-01-01
present performance statistics to explain the scalability behavior. Keywords-atmospheric models, time intergrators , MPI, scal- ability, performance; I...solution vector q = (ρ′,uT , θ′), Eq. (1) is written in condensed form as ∂q ∂t = S(q) (2) Report Documentation Page Form ApprovedOMB No. 0704-0188...Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions
Subspace Methods for Eigenvalue Problems
Hochstenbach, Michiel Erik
2003-01-01
This thesis treats a number of aspects of subspace methods for various eigenvalue problems. Vibrations and their corresponding eigenvalues (or frequencies) arise in science, engineering, and daily life. Matrix eigenvalue problems come from a large number of areas, such as chemistry,
Explaining outliers by subspace separability
DEFF Research Database (Denmark)
Micenková, Barbora; Ng, Raymond T.; Dang, Xuan-Hong
2013-01-01
for the outlier. These explanations are expressed in the form of subspaces in which the given outlier shows separability from the inliers. In this manner, our proposed method complements existing outlier detection algorithms by providing additional information about the outliers. Our method is designed to work...
Directory of Open Access Journals (Sweden)
Rocco Langone
2016-05-01
Full Text Available Spectral clustering methods allow datasets to be partitioned into clusters by mapping the input datapoints into the space spanned by the eigenvectors of the Laplacian matrix. In this article, we make use of the incomplete Cholesky decomposition (ICD to construct an approximation of the graph Laplacian and reduce the size of the related eigenvalue problem from N to m, with m ≪ N . In particular, we introduce a new stopping criterion based on normalized mutual information between consecutive partitions, which terminates the ICD when the change in the cluster assignments is below a given threshold. Compared with existing ICD-based spectral clustering approaches, the proposed method allows the reduction of the number m of selected pivots (i.e., to obtain a sparser model and at the same time, to maintain high clustering quality. The method scales linearly with respect to the number of input datapoints N and has low memory requirements, because only matrices of size N × m and m × m are calculated (in contrast to standard spectral clustering, where the construction of the full N × N similarity matrix is needed. Furthermore, we show that the number of clusters can be reliably selected based on the gap heuristics computed using just a small matrix R of size m × m instead of the entire graph Laplacian. The effectiveness of the proposed algorithm is tested on several datasets.
Subspace Arrangement Codes and Cryptosystems
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
Geometry aware Stationary Subspace Analysis
2016-11-22
JMLR: Workshop and Conference Proceedings 63:430–444, 2016 ACML 2016 Geometry -aware Stationary Subspace Analysis Inbal Horev inbal@ms.k.u-tokyo.ac.jp... geometry of the SPD matrix manifold and the invariance properties of its metrics. Most notably we show that these invariances alleviate the need to...Horev, F. Yger & M. Sugiyama. Geometry -aware SSA many theoretical and practical aspects have been addressed (see Sugiyama and Kawanabe (2012) for an in
Krüger, Jens J.
2014-01-01
In computer science in general and in particular the field of high performance computing and supercomputing the term scalable plays an important role. It indicates that a piece of hardware, a concept, an algorithm, or an entire system scales with the size of the problem, i.e., it can not only be used in a very specific setting but it\\'s applicable for a wide range of problems. From small scenarios to possibly very large settings. In this spirit, there exist a number of fixed areas of research on scalability. There are works on scalable algorithms, scalable architectures but what are scalable devices? In the context of this chapter, we are interested in a whole range of display devices, ranging from small scale hardware such as tablet computers, pads, smart-phones etc. up to large tiled display walls. What interests us mostly is not so much the hardware setup but mostly the visualization algorithms behind these display systems that scale from your average smart phone up to the largest gigapixel display walls.
Frequent Pattern Mining Algorithms for Data Clustering
DEFF Research Database (Denmark)
Zimek, Arthur; Assent, Ira; Vreeken, Jilles
2014-01-01
that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data......Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say....... In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining...
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.
Energy Technology Data Exchange (ETDEWEB)
Retico, A. [Istituto Nazionale di Fisica Nucleare, Largo Pontecorvo 3, 56127 Pisa (Italy)]. E-mail: alessandra.retico@df.unipi.it; Delogu, P. [Istituto Nazionale di Fisica Nucleare, Largo Pontecorvo 3, 56127 Pisa (Italy): Dipartimento di Fisica dell' Universita di Pisa, Largo Pontecorvo 3, 56127 Pisa (Italy); Fantacci, M.E. [Istituto Nazionale di Fisica Nucleare, Largo Pontecorvo 3, 56127 Pisa (Italy): Dipartimento di Fisica dell' Universita di Pisa, Largo Pontecorvo 3, 56127 Pisa (Italy); Preite Martinez, A. [Dipartimento di Fisica dell' Universita di Pisa, Largo Pontecorvo 3, 56127 Pisa (Italy); Stefanini, A. [Istituto Nazionale di Fisica Nucleare, Largo Pontecorvo 3, 56127 Pisa (Italy): Dipartimento di Fisica dell' Universita di Pisa, Largo Pontecorvo 3, 56127 Pisa (Italy); Tata, A. [Istituto Nazionale di Fisica Nucleare, Largo Pontecorvo 3, 56127 Pisa (Italy)
2006-12-20
A computer-aided detection (CADe) system for microcalcification cluster identification in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and artificial neural networks. It is able to identify microcalcifications in different kinds of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of mammographic data. We report the FROC analyses of the CADe system performances on three different dataset of mammograms, i.e. images of the CALMA INFN-founded database collected in the Italian National screening program, the MIAS database and the so-far collected MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im have been obtained on the CALMA, MIAS and MammoGrid database, respectively.
Energy Technology Data Exchange (ETDEWEB)
Kowalski, Karol; Krishnamoorthy, Sriram; Olson, Ryan M.; Tipparaju, Vinod; Apra, Edoardo
2011-11-30
The development of reliable tools for excited-state simulations is emerging as an extremely powerful computational chemistry tool for understanding complex processes in the broad class of light harvesting systems and optoelectronic devices. Over the last years we have been developing equation of motion coupled cluster (EOMCC) methods capable of tackling these problems. In this paper we discuss the parallel performance of EOMCC codes which provide accurate description of the excited-state correlation effects. Two aspects are discuss in details: (1) a new algorithm for the iterative EOMCC methods based on the novel task scheduling algorithms, and (2) parallel algorithms for the non-iterative methods describing the effect of triply excited configurations. We demonstrate that the most computationally intensive non-iterative part can take advantage of 210,000 cores of the Cray XT5 system at OLCF. In particular, we demonstrate the importance of non-iterative many-body methods for achieving experimental level of accuracy for several porphyrin-based system.
Simplex ACE: a constrained subspace detector
Ziemann, Amanda; Theiler, James
2017-08-01
In hyperspectral target detection, one must contend with variability in both target materials and background clutter. While most algorithms focus on the background clutter, there are some materials for which there is substantial variability in the signatures of the target. When multiple signatures can be used to describe a target material, subspace detectors are often the detection algorithm of choice. However, as the number of variable target spectra increases, so does the size of the target subspace spanned by these spectra, which in turn increases the number of false alarms. Here, we propose a modification to this approach, wherein the target subspace is instead a constrained subspace, or a simplex without the sum-to-one constraint. We derive the simplex adaptive matched filter (simplex AMF) and the simplex adaptive cosine estimator (simplex ACE), which are constrained basis adaptations of the traditional subspace AMF and subspace ACE detectors. We present results using simplex AMF and simplex ACE for variable targets, and compare their performances against their subspace counterparts. Our primary interest is in the simplex ACE detector, and as such, the experiments herein seek to evaluate the robustness of simplex ACE, with simplex AMF included for comparison. Results are shown on hyperspectral images using both implanted and ground-truthed targets, and demonstrate the robustness of simplex ACE to target variability.
Stochastic Subspace Method for Experimental Modal Analysis
Directory of Open Access Journals (Sweden)
Liu Dazhi
2016-01-01
Full Text Available The formula of stochastic subspace identification method is deduced in details and the program is written out. The two methods are verified by a vibration test on a 5-floor rigid frame model. In this test the gauss white noise generated from a shaker table to simulate the ambient vibration on the model, and the response signals are measured. Next, the response data of experiment are processed by auto-cross spectrum density method and stochastic subspace identification method respectively, the two methods are verified by comparing with the theory result. and bearing out the superiority of stochastic subspace identification method compared to auto-cross spectrum density method.
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...... the estimation accuracy for the sparse channel, while achieving the same performance as the conventional subspace method when the channel is dense. Moreover, the proposed method enables us to estimate the channel response with unknown channel order if the channel is sparse enough....
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
Numerical considerations in computing invariant subspaces
Energy Technology Data Exchange (ETDEWEB)
Dongarra, J.J. (Tennessee Univ., Knoxville, TN (USA). Dept. of Computer Science Oak Ridge National Lab., TN (USA)); Hammarling, S. (Numerical Algorithms Group Ltd., Oxford (UK)); Wilkinson, J.H. (Oak Ridge National Lab., TN (USA))
1990-11-01
This paper describes two methods for computing the invariant subspace of a matrix. The first involves using transformations to interchange the eigenvalues; the second involves direct computation of the vectors. 10 refs.
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......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....
Scalable Robust Principal Component Analysis Using Grassmann Averages.
Hauberg, Sren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael J
2016-11-01
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average ( GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average ( TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.
Ramanujan subspace pursuit for signal periodic decomposition
Deng, Shi-Wen; Han, Ji-Qing
2017-06-01
The period estimation and periodic decomposition of a signal represent long-standing problems in the field of signal processing and biomolecular sequence analysis. To address such problems, we introduce the Ramanujan subspace pursuit (RSP) based on the Ramanujan subspace. As a greedy iterative algorithm, the RSP can uniquely decompose any signal into a sum of exactly periodic components by selecting and removing the most dominant periodic component from the residual signal in each iteration. In the RSP, a novel periodicity metric is derived based on the energy of the exactly periodic component obtained by orthogonally projecting the residual signal into the Ramanujan subspace. The metric is then used to select the most dominant periodic component in each iteration. To reduce the computational cost of the RSP, we also propose the fast RSP (FRSP) based on the relationship between the periodic subspace and the Ramanujan subspace and based on the maximum likelihood estimation of the energy of the periodic component in the periodic subspace. The fast RSP has a lower computational cost and can decompose a signal of length N into a sum of K exactly periodic components in O (KNlogN) . In short, the main contributions of this paper are threefold: First, we present the RSP algorithm for decomposing a signal into its periodic components and theoretically prove the convergence of the algorithm based on the Ramanujan subspaces. Second, we present the FRSP algorithm, which is used to reduce the computational cost. Finally, we derive a periodic metric to measure the periodicity of the hidden periodic components of a signal. In addition, our results show that the RSP outperforms current algorithms for period estimation.
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.
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.
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...
On the Subspace Projected Approximate Matrix method
Brandts, J.H.; Reis da Silva, R.
2015-01-01
We provide a comparative study of the Subspace Projected Approximate Matrix method, abbreviated SPAM, which is a fairly recent iterative method of computing a few eigenvalues of a Hermitian matrix A. It falls in the category of inner-outer iteration methods and aims to reduce the costs of
Convertible Subspaces of Hessenberg-Type Matrices
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Henrique F. da Cruz
2017-12-01
Full Text Available We describe subspaces of generalized Hessenberg matrices where the determinant is convertible into the permanent by affixing ± signs. An explicit characterization of convertible Hessenberg-type matrices is presented. We conclude that convertible matrices with the maximum number of nonzero entries can be reduced to a basic set.
Interference subspace rejection in wideband CDMA:
DEFF Research Database (Denmark)
Hansen, Henrik; Affes, Sofiene; Mermelstein, Paul
2001-01-01
This paper extends our study on a multi-user receiver structure for base-station receivers with antenna arrays in multicellular systems. The receiver employs a beamforming structure with constraints that nulls the signal component in appropriate interference subspaces. Here we introduce a new mod...
Chen, Dan; Guo, Lin-yuan; Wang, Chen-hao; Ke, Xi-zheng
2017-07-01
Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.
On a subspace of dual Zariski topology
Ćeken, Seçil
2017-07-01
Let R be a commutative ring with identity and S pecs(M) (resp. Min(M)) denote the set of all second (resp. minimal) submodules of a non-zero R-module M. In this paper, we investigate several properties of the subspace topology on Min(M) induced by the dual Zariski on S pecs(M) and determine some cases in which Min(M) is a max-spectral space.
Application of Earthquake Subspace Detectors at Kilauea and Mauna Loa Volcanoes, Hawai`i
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.
Computing approximate (symmetric block) rational Krylov subspaces without explicit inversion
Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf
2013-01-01
It has been shown, see TW623, that approximate extended Krylov subspaces can be computed —under certain assumptions— without any explicit inversion or system solves. Instead the necessary products A-1v are obtained in an implicit way retrieved from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which contain besides poles at infinite and zero also finite non-zero poles. Also an adaption of the algorithm to the block and the symmetric ...
Mosmann, Tim R; Naim, Iftekhar; Rebhahn, Jonathan; Datta, Suprakash; Cavenaugh, James S; Weaver, Jason M; Sharma, Gaurav
2014-05-01
A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc.
Mosmann, Tim R; Naim, Iftekhar; Rebhahn, Jonathan; Datta, Suprakash; Cavenaugh, James S; Weaver, Jason M; Sharma, Gaurav
2014-01-01
A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc. PMID:24532172
Optimizing Cubature for Efficient Integration of Subspace Deformations.
An, Steven S; Kim, Theodore; James, Doug L
2009-12-01
We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional quadrature) optimized for efficient integration of force densities associated with particular subspace deformations, particular materials, and particular geometric domains. We support generic subspace deformation kinematics, and nonlinear hyperelastic materials. For an r-dimensional deformation subspace with O(r) cubature points, our method is able to evaluate subspace forces at O(r(2)) cost. We also describe composite cubature rules for runtime error estimation. Results are provided for various subspace deformation models, several hyperelastic materials (St.Venant-Kirchhoff, Mooney-Rivlin, Arruda-Boyce), and multimodal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration. CR CATEGORIES: I.6.8 [Simulation and Modeling]: Types of Simulation-Animation, I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Physically based modeling G.1.4 [Mathematics of Computing]: Numerical Analysis-Quadrature and Numerical Differentiation.
Minimal Krylov Subspaces for Dimension Reduction
2013-01-01
67]. This algorithm and analysis address item 4. G. We develop Algorithm 13, a synthesis of random projections and Krylov subspaces for inverse 9 1...of ‖Â(k)K ‖, by using the identities ‖A‖F = tr(AT A) and tr(A) = ∑n i=1 aii with a block decomposition of AT A. The bounds for random projections can...3,710,420 non-zero elements. We rescaled the NPL data with term-frequency and inverse document frequency. The spectrum of the matrix AT A for the Enron
Partial interference subspace rejection in CDMA systems
DEFF Research Database (Denmark)
Hansen, Henrik; Affes, Sofiene; Mewelstein, Paul
2001-01-01
Previously presented interference subspace rejection (ISR) proposed a family of new efficient multiuser detectors for CDMA. We reconsider in this paper the modes of ISR using decision feedback (DF). DF modes share similarities with parallel interference cancellation (PIC) but attempt to cancel...... interference by nulling rather than subtraction. However like the PIC they are prone to wrong tentative decisions. We propose a modification to DF modes that performs partial ISR instead of complete interference cancellation. When tentative decisions are correct, interference is therefore not perfectly...
Tangent spaces to metric spaces and to their subspaces
Dovgoshey, O.
2009-01-01
We investigate a tangent space at a point of a general metric space and metric space valued derivatives. The conditions under which two different subspace of a metric space have isometric tangent spaces in a common point of these subspaces are completely determinated.
Scalable Engineering of Quantum Optical Information Processing Architectures (SEQUOIA)
2016-12-13
scalable architecture for LOQC and cluster state quantum computing (Ballistic or non-ballistic) - With parametric nonlinearities (Kerr, chi-2...Scalable Engineering of Quantum Optical Information-Processing Architectures (SEQUOIA) 5a. CONTRACT NUMBER W31-P4Q-15-C-0045 5b. GRANT NUMBER 5c...Technologies 13 December 2016 “Scalable Engineering of Quantum Optical Information-Processing Architectures (SEQUOIA)” Final R&D Status Report
Yuan, Xiaoru; Ren, Donghao; Wang, Zuchao; Guo, Cong
2013-12-01
For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions.
Robust adaptive subspace detection in impulsive noise
Ben Atitallah, Ismail
2016-09-13
This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.
Scalable Frequent Subgraph Mining
Abdelhamid, Ehab
2017-06-19
A graph is a data structure that contains a set of nodes and a set of edges connecting these nodes. Nodes represent objects while edges model relationships among these objects. Graphs are used in various domains due to their ability to model complex relations among several objects. Given an input graph, the Frequent Subgraph Mining (FSM) task finds all subgraphs with frequencies exceeding a given threshold. FSM is crucial for graph analysis, and it is an essential building block in a variety of applications, such as graph clustering and indexing. FSM is computationally expensive, and its existing solutions are extremely slow. Consequently, these solutions are incapable of mining modern large graphs. This slowness is caused by the underlying approaches of these solutions which require finding and storing an excessive amount of subgraph matches. This dissertation proposes a scalable solution for FSM that avoids the limitations of previous work. This solution is composed of four components. The first component is a single-threaded technique which, for each candidate subgraph, needs to find only a minimal number of matches. The second component is a scalable parallel FSM technique that utilizes a novel two-phase approach. The first phase quickly builds an approximate search space, which is then used by the second phase to optimize and balance the workload of the FSM task. The third component focuses on accelerating frequency evaluation, which is a critical step in FSM. To do so, a machine learning model is employed to predict the type of each graph node, and accordingly, an optimized method is selected to evaluate that node. The fourth component focuses on mining dynamic graphs, such as social networks. To this end, an incremental index is maintained during the dynamic updates. Only this index is processed and updated for the majority of graph updates. Consequently, search space is significantly pruned and efficiency is improved. The empirical evaluation shows that the
An alternative subspace approach to EEG dipole source localization
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
Energy Technology Data Exchange (ETDEWEB)
Xu Xiaoliang [KC Science and Technologies Inc., Naperville, IL 60565 (United States); Xu, Bobby [Illinois Mathematics and Science Academy, Aurora, IL 60506 (United States); He Bin [Department of Bioengineering, University of Illinois, Chicago, IL 60607 (United States)
2004-01-21
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.
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.
Attanasio, Orazio P; Fernández, Camila; Fitzsimons, Emla O A; Grantham-McGregor, Sally M; Meghir, Costas; Rubio-Codina, Marta
2014-09-29
To assess the effectiveness of an integrated early child development intervention, combining stimulation and micronutrient supplementation and delivered on a large scale in Colombia, for children's development, growth, and hemoglobin levels. Cluster randomized controlled trial, using a 2 × 2 factorial design, with municipalities assigned to one of four groups: psychosocial stimulation, micronutrient supplementation, combined intervention, or control. 96 municipalities in Colombia, located across eight of its 32 departments. 1420 children aged 12-24 months and their primary carers. Psychosocial stimulation (weekly home visits with play demonstrations), micronutrient sprinkles given daily, and both combined. All delivered by female community leaders for 18 months. Cognitive, receptive and expressive language, and fine and gross motor scores on the Bayley scales of infant development-III; height, weight, and hemoglobin levels measured at the baseline and end of intervention. Stimulation improved cognitive scores (adjusted for age, sex, testers, and baseline levels of outcomes) by 0.26 of a standard deviation (P=0.002). Stimulation also increased receptive language by 0.22 of a standard deviation (P=0.032). Micronutrient supplementation had no significant effect on any outcome and there was no interaction between the interventions. No intervention affected height, weight, or hemoglobin levels. Using the infrastructure of a national welfare program we implemented the integrated early child development intervention on a large scale and showed its potential for improving children's cognitive development. We found no effect of supplementation on developmental or health outcomes. Moreover, supplementation did not interact with stimulation. The implementation model for delivering stimulation suggests that it may serve as a promising blueprint for future policy on early childhood development.Trial registration Current Controlled trials ISRCTN18991160. © Attanasio et al 2014.
Indian Academy of Sciences (India)
electron transfer chains involved in a number of biologi- cal systems including respiration and photosynthesis.1. The most common iron–sulphur clusters found as active centres in iron–sulphur proteins are [Fe2S2], [Fe3S4] and [Fe4S4], in which Fe(III) ions are coordinated to cysteines from the peptide and are linked to each ...
Seismic noise attenuation using an online subspace tracking algorithm
Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang
2018-02-01
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 descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.
A Spectrum Sensing Scheme Based on Subspace Filtering
Mu, Junsheng; Yang, Wei; Jing, Xiaojun; Huang, Hai
2017-10-01
Spectrum sensing (SS) has attracted much concern of researchers due to its significant contribution on the spectral efficiency. Energy Detection (ED) has been a critical method for Spectrum Sensing in Cognitive Radio Networks (CRNS) due to its low complexity and simple implement. However, noise uncertainty in ED greatly degrades the detection performance, especially under a low Signal-to-Noise Ratio (SNR). To remove noise uncertainty as much as possible, a scheme based on subspace decomposition is proposed for SS, where the received signal is decomposed into two parts: noise subspace and signal-plus-noise subspace. Then the closed-form solution of the detection and false alarm probabilities is given on the basis of the signal-plus-noise subspace in Rayleigh fading channel. The energy of the remainders after removal of noise subspace and noise contribution in signal-plus-noise subspace is used to decide whether the primary user (PU) exists by a comparison with a redesigned threshold. Eventually, some simulations based on MATLAB platform is made to validate the proposed method.
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.
Directory of Open Access Journals (Sweden)
Lin Xinfan
2013-03-01
Full Text Available Although the battery surface temperature is commonly measured, the core temperature of a cell may be much higher hence more critical than the surface temperature. The core temperature of a battery, though usually unmeasured in commercial applications, can be estimated by an observer, based on a lumped-parameter battery thermal model and the measurement of the current and the surface temperature. Even with a closed loop observer based on the measured surface temperature, the accuracy of the core temperature estimation depends on the model parameters. For such purpose, an online parameterization methodology and an adaptive observer are designed for a cylindrical battery. The single cell thermal model is then scaled up to create a battery cluster model to investigate the temperature pattern of the cluster. The modeled thermal interconnections between cells include cell to cell heat conduction and convection to the surrounding coolant flow. An observability analysis is performed on the cluster before designing a closed loop observer for the pack. Based on the analysis, guidelines for determining the minimum number of required sensors and their exact locations are derived that guarantee the observability of all temperature states. Bien que la température de surface d’une batterie soit généralement mesurée, la température interne d’une cellule peut être beaucoup plus élevée donc plus critique que la température de surface. La température interne d’une batterie, pourtant normalement non mesurée dans les applications commerciales, peut être évaluée par un observateur, sur la base d’un modèle thermique de batterie à constantes localisées et à partir de la mesure du courant et de la température de surface. Même avec un observateur en boucle fermée basé sur la température de surface mesurée, la précision de l’estimation de la température interne dépend des constantes du modèle. Dans cette optique, une méthodologie de
A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging.
Li, Yudu; Lam, Fan; Clifford, Bryan; Liang, Zhi-Pei
2017-10-01
To provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors. A novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union of subspaces. Based on this model, the spectral quantification can be solved in two steps: 1) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors; and 2) parameter estimation for the union-of-subspaces model incorporating spatial priors. The proposed method has been evaluated using both simulated and experimental data, producing impressive results. The proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints. The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.
A Scalable Algorithm for Clustering Sequential Data
2001-08-16
can be computed e cient using a variety of sequential pattern discovery algorithms AS SA Zak JKK HPMA Projecting in to the Feature...matrices from protein blocks Proc Natl Academy Science HPMA J Han J Pei B MortazaviAsl Q Chen U Dayal and M
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
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.
Subspace dynamic mode decomposition for stochastic Koopman analysis
Takeishi, Naoya; Kawahara, Yoshinobu; Yairi, Takehisa
2017-09-01
The analysis of nonlinear dynamical systems based on the Koopman operator is attracting attention in various applications. Dynamic mode decomposition (DMD) is a data-driven algorithm for Koopman spectral analysis, and several variants with a wide range of applications have been proposed. However, popular implementations of DMD suffer from observation noise on random dynamical systems and generate inaccurate estimation of the spectra of the stochastic Koopman operator. In this paper, we propose subspace DMD as an algorithm for the Koopman analysis of random dynamical systems with observation noise. Subspace DMD first computes the orthogonal projection of future snapshots to the space of past snapshots and then estimates the spectra of a linear model, and its output converges to the spectra of the stochastic Koopman operator under standard assumptions. We investigate the empirical performance of subspace DMD with several dynamical systems and show its utility for the Koopman analysis of random dynamical systems.
Krylov-subspace acceleration of time periodic waveform relaxation
Energy Technology Data Exchange (ETDEWEB)
Lumsdaine, A. [Univ. of Notre Dame, IN (United States)
1994-12-31
In this paper the author uses Krylov-subspace techniques to accelerate the convergence of waveform relaxation applied to solving systems of first order time periodic ordinary differential equations. He considers the problem in the frequency domain and presents frequency dependent waveform GMRES (FDWGMRES), a member of a new class of frequency dependent Krylov-subspace techniques. FDWGMRES exhibits many desirable properties, including finite termination independent of the number of timesteps and, for certain problems, a convergence rate which is bounded from above by the convergence rate of GMRES applied to the static matrix problem corresponding to the linear time-invariant ODE.
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.
Slagell, Adam J.; Bonilla, Rafael
2004-01-01
This report surveys different PKI technologies such as PKIX and SPKI and the issues of PKI that affect scalability. Much focus is spent on certificate revocation methodologies and status verification systems such as CRLs, Delta-CRLs, CRS, Certificate Revocation Trees, Windowed Certificate Revocation, OCSP, SCVP and DVCS.
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
DEFF Research Database (Denmark)
Muller, Emmanuel; Assent, Ira; Iglesias, Patricia
2012-01-01
. And the same object might appear perfectly regular in other subspaces. One can think of subspaces as multiple views on one database. Traditional methods consider only one view (the full attribute space). Thus, they miss complex outliers that are hidden in multiple subspaces. In this work, we propose Outrank...
Counting Subspaces of a Finite Vector Space – 2
Indian Academy of Sciences (India)
Counting Subspaces of a Finite Vector Space – 2. Amritanshu Prasad. Keywords. Gaussian binomial coefficients, finite vector spaces. Amritanshu Prasad has been at the Institute of. Mathematical Sciences,. Chennai since 2003. His mathematical interests include representation theory, harmonic analysis, and combinatorics ...
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.
New characterizations of fusion frames (frames of subspaces)
Indian Academy of Sciences (India)
bounded surjective operator. Keywords. Frame; fusion frame (frames of subspaces); exact fusion frame; Bessel fusion sequence; orthonormal fusion basis; pseudo-inverses. 1. Introduction. A frame is a redundant set of vectors in a Hilbert space H with the property that provide usually non-unique representations of vectors in ...
Computational Complexity of Subspace Detectors and Matched Field Processing
Energy Technology Data Exchange (ETDEWEB)
Harris, D B
2010-12-01
Subspace detectors implement a correlation type calculation on a continuous (network or array) data stream [Harris, 2006]. The difference between subspace detectors and correlators is that the former projects the data in a sliding observation window onto a basis of template waveforms that may have a dimension (d) greater than one, and the latter projects the data onto a single waveform template. A standard correlation detector can be considered to be a degenerate (d=1) form of a subspace detector. Figure 1 below shows a block diagram for the standard formulation of a subspace detector. The detector consists of multiple multichannel correlators operating on a continuous data stream. The correlation operations are performed with FFTs in an overlap-add approach that allows the stream to be processed in uniform, consecutive, contiguous blocks. Figure 1 is slightly misleading for a calculation of computational complexity, as it is possible, when treating all channels with the same weighting (as shown in the figure), to perform the indicated summations in the multichannel correlators before the inverse FFTs and to get by with a single inverse FFT and overlap add calculation per multichannel correlator. In what follows, we make this simplification.
Estimation of hysteretic damping of structures by stochastic subspace identification
DEFF Research Database (Denmark)
Bajric, Anela; Høgsberg, Jan Becker
2018-01-01
identification method suitable for random response of dynamic systems with hysteretic damping. The method applies the concept of Stochastic Subspace Identification (SSI) to estimate the model parameters of a dynamic system with hysteretic damping. The restoring force is represented by the Bouc-Wen model...
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 estimato...
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 ...
Online Categorical Subspace Learning for Sketching Big Data with Misses
Shen, Yanning; Mardani, Morteza; Giannakis, Georgios B.
2017-08-01
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorization-minimization to sketch high-dimensional categorical data `on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.
Scalable Resolution Display Walls
Leigh, Jason
2013-01-01
This article will describe the progress since 2000 on research and development in 2-D and 3-D scalable resolution display walls that are built from tiling individual lower resolution flat panel displays. The article will describe approaches and trends in display hardware construction, middleware architecture, and user-interaction design. The article will also highlight examples of use cases and the benefits the technology has brought to their respective disciplines. © 1963-2012 IEEE.
Subspace-based interference removal methods for a multichannel biomagnetic sensor array
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.
Declarative and Scalable Selection for Map Visualizations
DEFF Research Database (Denmark)
Kefaloukos, Pimin Konstantin Balic
supports the PostgreSQL dialect of SQL. The prototype implementation is a compiler that translates CVL into SQL and stored procedures. (c) TileHeat is a framework and basic algorithm for partial materialization of hot tile sets for scalable map distribution. The framework predicts future map workloads......, there are indications that the method is scalable for databases that contain millions of records, especially if the target language of the compiler is substituted by a cluster-ready variant of SQL. While several realistic use cases for maps have been implemented in CVL, additional non-geographic data visualization uses...... goal. The results for Tileheat show that the prediction method offers a substantial improvement over the current method used by the Danish Geodata Agency. Thus, a large amount of computations can potentially be saved by this public institution, who is responsible for the distribution of government...
Quantum repeaters based on trapped ions with decoherence-free subspace encoding
Zwerger, M.; Lanyon, B. P.; Northup, T. E.; Muschik, C. A.; Dür, W.; Sangouard, N.
2017-12-01
Quantum repeaters provide an efficient solution to distribute Bell pairs over arbitrarily long distances. While scalable architectures are demanding regarding the number of qubits that need to be controlled, here we present a quantum repeater scheme aiming to extend the range of present day quantum communications that could be implemented in the near future with trapped ions in cavities. We focus on an architecture where ion-photon entangled states are created locally and subsequently processed with linear optics to create elementary links of ion-ion entangled states. These links are then used to distribute entangled pairs over long distances using successive entanglement swapping operations performed using deterministic ion-ion gates. We show how this architecture can be implemented while encoding the qubits in a decoherence-free subspace to protect them against collective dephasing. This results in a protocol that can be used to violate a Bell inequality over distances of about 800 km assuming state-of-the-art parameters. We discuss how this could be improved to several thousand kilometres in future setups.
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.
Generalized Broadband Beamforming Using a Modal Subspace Decomposition
Directory of Open Access Journals (Sweden)
Rodney A. Kennedy
2007-01-01
Full Text Available We propose a new broadband beamformer design technique which produces an optimal receiver beam pattern for any set of field measurements in space and time. The modal subspace decomposition (MSD technique is based on projecting a desired pattern into the subspace of patterns achievable by a particular set of space-time sampling positions. This projection is the optimal achievable pattern in the sense that it minimizes the mean-squared error (MSE between the desired and actual patterns. The main advantage of the technique is versatility as it can be applied to both sparse and dense arrays, nonuniform and asynchronous time sampling, and dynamic arrays where sensors can move throughout space. It can also be applied to any beam pattern type, including frequency-invariant and spot pattern designs. A simple extension to the technique is presented for oversampled arrays, which allows high-resolution beamforming whilst carefully controlling input energy and error sensitivity.
Face recognition based on LDA in manifold subspace
Directory of Open Access Journals (Sweden)
Hung Phuoc Truong
2016-05-01
Full Text Available Although LDA has many successes in dimensionality reduction and data separation, it also has disadvantages, especially the small sample size problem in training data because the "within-class scatter" matrix may not be accurately estimated. Moreover, this algorithm can only operate correctly with labeled data in supervised learning. In practice, data collection is very huge and labeling data requires high-cost, thus the combination of a part of labeled data and unlabeled data for this algorithm in Manifold subspace is a novelty research. This paper reports a study that propose a semi-supervised method called DSLM, which aims at overcoming all these limitations. The proposed method ensures that the discriminative information of labeled data and the intrinsic geometric structure of data are mapped to new optimal subspace. Results are obtained from the experiments and compared to several related methods showing the effectiveness of our proposed method.
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.
Scalable Reliable SD Erlang Design
Chechina, Natalia; Trinder, Phil; Ghaffari, Amir; Green, Rickard; Lundin, Kenneth; Virding, Robert
2014-01-01
This technical report presents the design of Scalable Distributed (SD) Erlang: a set of language-level changes that aims to enable Distributed Erlang to scale for server applications on commodity hardware with at most 100,000 cores. We cover a number of aspects, specifically anticipated architecture, anticipated failures, scalable data structures, and scalable computation. Other two components that guided us in the design of SD Erlang are design principles and typical Erlang applications. The...
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
important role in quantum teleportation and communication [3]. The following theorem due to Horodecki and ... Immediate. 2. DEFINITION 1.2. A non-zero subspace S ⊂ H is said to be completely entangled if S contains no non-zero product vector of the form u1 ⊗ u2 ⊗···⊗ uk with ui ∈ Hi for each i. Denote by E the collection ...
Proximinal subspaces of finite codimension in direct sum spaces
Indian Academy of Sciences (India)
Springer Verlag Heidelberg #4 2048 1996 Dec 15 10:16:45
subspace of finite codimension in c0-direct sum of Banach spaces. Keywords. Proximinality and strong proximinality. 0. Notation and preliminaries. Let X be a normed linear space and A be a closed subset of X. We say A is proximinal in. X if for each x ∈ X there exists an element a ∈ A such that. ∥. ∥x − a. ∥. ∥ = d(x, A).
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.
Scalable resource management in high performance computers.
Energy Technology Data Exchange (ETDEWEB)
Frachtenberg, E. (Eitan); Petrini, F. (Fabrizio); Fernandez Peinador, J. (Juan); Coll, S. (Salvador)
2002-01-01
Clusters of workstations have emerged as an important platform for building cost-effective, scalable and highly-available computers. Although many hardware solutions are available today, the largest challenge in making large-scale clusters usable lies in the system software. In this paper we present STORM, a resource management tool designed to provide scalability, low overhead and the flexibility necessary to efficiently support and analyze a wide range of job scheduling algorithms. STORM achieves these feats by closely integrating the management daemons with the low-level features that are common in state-of-the-art high-performance system area networks. The architecture of STORM is based on three main technical innovations. First, a sizable part of the scheduler runs in the thread processor located on the network interface. Second, we use hardware collectives that are highly scalable both for implementing control heartbeats and to distribute the binary of a parallel job in near-constant time, irrespective of job and machine sizes. Third, we use an I/O bypass protocol that allows fast data movements from the file system to the communication buffers in the network interface and vice versa. The experimental results show that STORM can launch a job with a binary of 12MB on a 64 processor/32 node cluster in less than 0.25 sec on an empty network, in less than 0.45 sec when all the processors are busy computing other jobs, and in less than 0.65 sec when the network is flooded with a background traffic. This paper provides experimental and analytical evidence that these results scale to a much larger number of nodes. To the best of our knowledge, STORM is at least two orders of magnitude faster than existing production schedulers in launching jobs, performing resource management tasks and gang scheduling.
Comparison Study of Subspace Identification Methods Applied to Flexible Structures
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.
Zhu, Xiaofeng; Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2016-03-01
The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.
Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles
Directory of Open Access Journals (Sweden)
Liying Yang
2016-01-01
Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.
A scalable distributed RRT for motion planning
Jacobs, Sam Ade
2013-05-01
Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine. © 2013 IEEE.
Iterative Discovery of Multiple AlternativeClustering Views.
Donglin Niu; Dy, Jennifer G; Jordan, Michael I
2014-07-01
Complex data can be grouped and interpreted in many different ways. Most existing clustering algorithms, however, only find one clustering solution, and provide little guidance to data analysts who may not be satisfied with that single clustering and may wish to explore alternatives. We introduce a novel approach that provides several clustering solutions to the user for the purposes of exploratory data analysis. Our approach additionally captures the notion that alternative clusterings may reside in different subspaces (or views). We present an algorithm that simultaneously finds these subspaces and the corresponding clusterings. The algorithm is based on an optimization procedure that incorporates terms for cluster quality and novelty relative to previously discovered clustering solutions. We present a range of experiments that compare our approach to alternatives and explore the connections between simultaneous and iterative modes of discovery of multiple clusterings.
Some properties of frames of subspaces obtained by operator theory methods
Ruiz, Mariano A.; Stojanoff, Demetrio
2008-07-01
We study the relationship among operators, orthonormal basis of subspaces and frames of subspaces (also called fusion frames) for a separable Hilbert space H. We get sufficient conditions on an orthonormal basis of subspaces of a Hilbert space K and a surjective T[set membership, variant]L(K,H) in order that {T(Ei)}i[set membership, variant]I is a frame of subspaces with respect to a computable sequence of weights. We also obtain generalizations of results in [J.A. Antezana, G. Corach, M. Ruiz, D. Stojanoff, Oblique projections and frames, Proc. Amer. Math. Soc. 134 (2006) 1031-1037], which relate frames of subspaces (including the computation of their weights) and oblique projections. The notion of refinement of a fusion frame is defined and used to obtain results about the excess of such frames. We study the set of admissible weights for a generating sequence of subspaces. Several examples are given.
Scalable photoreactor for hydrogen production
Takanabe, Kazuhiro
2017-04-06
Provided herein are scalable photoreactors that can include a membrane-free water- splitting electrolyzer and systems that can include a plurality of membrane-free water- splitting electrolyzers. Also provided herein are methods of using the scalable photoreactors provided herein.
Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition
Ma, Xi; Wu, Zhiyong; Jia, Jia; Xu, Mingxing; Meng, Helen; Cai, Lianhong
2016-01-01
Feature subspace selection is an important part in speech emotion recognition. Most of the studies are devoted to finding a feature subspace for representing all emotions. However, some studies have indicated that the features associated with different emotions are not exactly the same. Hence, traditional methods may fail to distinguish some of the emotions with just one global feature subspace. In this work, we propose a new divide and conquer idea to solve the problem. First, the feature su...
A subspace approach based on embedded prewhitening for voice activity detection.
Kim, Dong Kook; Chang, Joon-Hyuk
2011-11-01
This paper presents a subspace approach for voice activity detection (VAD). The proposed approach is based on an embedded prewhitening scheme for the simultaneous diagonalization of the clean speech and noise covariance matrices to provide a decision rule based on likelihood ratio test in signal subspace domain. Experimental results show that the proposed subspace-based VAD algorithm outperforms the method using a Gaussian model in a conventional discrete Fourier transform domain at the low signal-to-noise conditions.
Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf
2014-01-01
It has been shown that approximate extended Krylov subspaces can be computed, under certain assumptions, without any explicit inversion or system solves. Instead, the vectors spanning the extended Krylov space are retrieved in an implicit way, via unitary similarity transformations, from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which aside from poles at infinity and zero, also contain finite non-zero poles. Furthermore, the algorith...
Lie n-derivations on 7-subspace lattice algebras
Indian Academy of Sciences (India)
Abstract. Let L be a J -subspace lattice on a Banach space X over the real or complex field F with dim X ≥ 3 and let n ≥ 2 be an integer. Suppose thatdim K ≠ 2 for every K ∈ J ( L ) and L : A l g L → A l g L is a linear map. Itis shown that L satisfies ∑ i = 1 n p n ( A 1 , ⋯ , A i − 1 , L ( A i ) , A i + 1 , ⋯ , A n ) = 0 whenever p n ( A ...
Accurate Excited State Geometries within Reduced Subspace TDDFT/TDA.
Robinson, David
2014-12-09
A method for the calculation of TDDFT/TDA excited state geometries within a reduced subspace of Kohn-Sham orbitals has been implemented and tested. Accurate geometries are found for all of the fluorophore-like molecules tested, with at most all valence occupied orbitals and half of the virtual orbitals included but for some molecules even fewer orbitals. Efficiency gains of between 15 and 30% are found for essentially the same level of accuracy as a standard TDDFT/TDA excited state geometry optimization calculation.
Scalable Nanomanufacturing—A Review
Directory of Open Access Journals (Sweden)
Khershed Cooper
2017-01-01
Full Text Available This article describes the field of scalable nanomanufacturing, its importance and need, its research activities and achievements. The National Science Foundation is taking a leading role in fostering basic research in scalable nanomanufacturing (SNM. From this effort several novel nanomanufacturing approaches have been proposed, studied and demonstrated, including scalable nanopatterning. This paper will discuss SNM research areas in materials, processes and applications, scale-up methods with project examples, and manufacturing challenges that need to be addressed to move nanotechnology discoveries closer to the marketplace.
Enforcing Resource Sharing Agreements Among Distributed Server Clusters
National Research Council Canada - National Science Library
Zhao, Tao; Karamcheti, Vijay
2001-01-01
Future scalable, high throughput, and high performance applications are likely to execute on platforms constructed by clustering multiple autonomous distributed servers, with resource access governed...
A Subspace Method for Dynamical Estimation of Evoked Potentials
Directory of Open Access Journals (Sweden)
Stefanos D. Georgiadis
2007-01-01
Full Text Available It is a challenge in evoked potential (EP analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing. We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements.
Beamforming using subspace estimation from a diagonally averaged sample covariance.
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.
Scalable Gravity Offload System Project
National Aeronautics and Space Administration — A scalable gravity offload device simulates reduced gravity for the testing of various surface system elements such as mobile robots, excavators, habitats, and...
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…
Technical Report: Scalable Parallel Algorithms for High Dimensional Numerical Integration
Energy Technology Data Exchange (ETDEWEB)
Masalma, Yahya [Universidad del Turabo; Jiao, Yu [ORNL
2010-10-01
We implemented a scalable parallel quasi-Monte Carlo numerical high-dimensional integration for tera-scale data points. The implemented algorithm uses the Sobol s quasi-sequences to generate random samples. Sobol s sequence was used to avoid clustering effects in the generated random samples and to produce low-discrepancy random samples which cover the entire integration domain. The performance of the algorithm was tested. Obtained results prove the scalability and accuracy of the implemented algorithms. The implemented algorithm could be used in different applications where a huge data volume is generated and numerical integration is required. We suggest using the hyprid MPI and OpenMP programming model to improve the performance of the algorithms. If the mixed model is used, attention should be paid to the scalability and accuracy.
Scalable fast multipole accelerated vortex methods
Hu, Qi
2014-05-01
The fast multipole method (FMM) is often used to accelerate the calculation of particle interactions in particle-based methods to simulate incompressible flows. To evaluate the most time-consuming kernels - the Biot-Savart equation and stretching term of the vorticity equation, we mathematically reformulated it so that only two Laplace scalar potentials are used instead of six. This automatically ensuring divergence-free far-field computation. Based on this formulation, we developed a new FMM-based vortex method on heterogeneous architectures, which distributed the work between multicore CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm uses new data structures which can dynamically manage inter-node communication and load balance efficiently, with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching calculation for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s.
Hund, Michael; Böhm, Dominic; Sturm, Werner; Sedlmair, Michael; Schreck, Tobias; Ullrich, Torsten; Keim, Daniel A; Majnaric, Ljiljana; Holzinger, Andreas
2016-12-01
Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.
Discriminative Non-Linear Stationary Subspace Analysis for Video Classification.
Baktashmotlagh, Mahsa; Harandi, Mehrtash; Lovell, Brian C; Salzmann, Mathieu
2014-12-01
Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce non-linear stationary subspace analysis: a method that overcomes this issue by explicitly separating the stationary parts of the video signal (i.e., the parts shared across all videos in one class), from its non-stationary parts (i.e., the parts specific to individual videos). Our method also encourages the new representation to be discriminative, thus accounting for the underlying classification problem. We demonstrate the effectiveness of our approach on dynamic texture recognition, scene classification and action recognition.
Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble
Directory of Open Access Journals (Sweden)
Pham Tuan D
2011-04-01
Full Text Available Abstract Background Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM, which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Results Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The
Linear Subspace Ranking Hashing for Cross-Modal Retrieval.
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.
Scalability of DL_POLY on High Performance Computing Platform
Directory of Open Access Journals (Sweden)
Mabule Samuel Mabakane
2017-12-01
Full Text Available This paper presents a case study on the scalability of several versions of the molecular dynamics code (DL_POLY performed on South Africa‘s Centre for High Performance Computing e1350 IBM Linux cluster, Sun system and Lengau supercomputers. Within this study different problem sizes were designed and the same chosen systems were employed in order to test the performance of DL_POLY using weak and strong scalability. It was found that the speed-up results for the small systems were better than large systems on both Ethernet and Infiniband network. However, simulations of large systems in DL_POLY performed well using Infiniband network on Lengau cluster as compared to e1350 and Sun supercomputer.
Shape Detection from Raw LiDAR Data with Subspace Modeling.
Wang, Jun; Xu, Kai
2017-09-01
LiDAR scanning has become a prevalent technique for digitalizing large-scale outdoor scenes. However, the raw LiDAR data often contain imperfections, e.g., missing large regions, anisotropy of sampling density, and contamination of noise and outliers, which are the major obstacles that hinder its more ambitious and higher level applications in digital city modeling. Observing that 3D urban scenes can be locally described with several low dimensional subspaces, we propose to locally classify the neighborhoods of the scans to model the substructures of the scenes. The key enabler is the adaptive kernel-scale scoring, filtering and clustering of substructures, making it possible to recover the local structures at all points simultaneously, even in the presence of severe data imperfections. Integrating the local analyses leads to robust shape detection from raw LiDAR data. On this basis, we develop several urban scene applications and verify them on a number of LiDAR scans with various complexities and styles, which demonstrates the effectiveness and robustness of our methods.
Cui, Yiqian; Shi, Junyou; Wang, Zili
2018-01-01
The centers and radii of radial basis functions (RBFs) greatly affect the approximation capability of RBF networks (RBFNs). Traditional statistics-based approaches are widely used, but they may lack adaptivity to different data structures. Quantum clustering (QC), derived from quantum mechanics and the Schrödinger equation, demonstrates excellent capability in finding the structure and conformity toward data distribution. In this paper, a novel neural networks model called quantum local potential function networks (QLPFNs) is proposed. The QLPFN inherits the outstanding properties of QC by constructing the waves and the potential functions, and the level of data concentration can be discovered to obtain the inherent structures of the given data set. The local potential functions form the basic components of the QLPFN structure, which are automatically generated from the subsets of training data following specific subspace division procedures. Therefore, the QLPFN model in fact incorporates the level of data concentration as a computation technique, which is different from the classical RBFN model that exhibits radial symmetry toward specific centers. Some application examples are given in this paper to show the effectiveness of the QLPFN model.
Adaptive low-rank subspace learning with online optimization for robust visual tracking.
Liu, Risheng; Wang, Di; Han, Yuzhuo; Fan, Xin; Luo, Zhongxuan
2017-04-01
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data. To address above limitations, this paper develops a novel low-rank subspace learning with adaptive penalization (LSAP) framework for subspace based robust visual tracking. Different from previous work, which often simply decomposes observations as low-rank features and sparse errors, LSAP simultaneously learns the subspace basis, low-rank coefficients and column sparse errors to formulate appearance subspace. Within LSAP framework, we introduce a Hadamard production based regularization to incorporate rich generative/discriminative structure constraints to adaptively penalize the coefficients for subspace learning. It is shown that such adaptive penalization can significantly improve the robustness of LSAP on severely corrupted dataset. To utilize LSAP for online visual tracking, we also develop an efficient incremental optimization scheme for nuclear norm and column sparse norm minimizations. Experiments on 50 challenging video sequences demonstrate that our tracker outperforms other state-of-the-art methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Scalable algorithms for contact problems
Dostál, Zdeněk; Sadowská, Marie; Vondrák, Vít
2016-01-01
This book presents a comprehensive and self-contained treatment of the authors’ newly developed scalable algorithms for the solutions of multibody contact problems of linear elasticity. The brand new feature of these algorithms is theoretically supported numerical scalability and parallel scalability demonstrated on problems discretized by billions of degrees of freedom. The theory supports solving multibody frictionless contact problems, contact problems with possibly orthotropic Tresca’s friction, and transient contact problems. It covers BEM discretization, jumping coefficients, floating bodies, mortar non-penetration conditions, etc. The exposition is divided into four parts, the first of which reviews appropriate facets of linear algebra, optimization, and analysis. The most important algorithms and optimality results are presented in the third part of the volume. The presentation is complete, including continuous formulation, discretization, decomposition, optimality results, and numerical experimen...
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.
Geometric subspace updates with applications to online adaptive nonlinear model reduction
DEFF Research Database (Denmark)
Zimmermann, Ralf; Peherstorfer, Benjamin; Willcox, Karen
2017-01-01
the least-squares problem that underlies the approximation problem of interest such that the associated least-squares residual vanishes exactly. The method builds on a Riemmannian optimization procedure on the Grassmann manifold of low-dimensional subspaces, namely the Grassmannian Rank-One Subspace...... Estimation (GROUSE). We establish for GROUSE a closed-form expression for the residual function along the geodesic descent direction. Specific applications of subspace adaptation are discussed in the context of image processing and model reduction of nonlinear partial differential equation systems....
A fuzzy logic based clustering strategy for improving vehicular ad ...
Indian Academy of Sciences (India)
Abstract. This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of ...
Scalable shared-memory multiprocessing
Lenoski, Daniel E
1995-01-01
Dr. Lenoski and Dr. Weber have experience with leading-edge research and practical issues involved in implementing large-scale parallel systems. They were key contributors to the architecture and design of the DASH multiprocessor. Currently, they are involved with commercializing scalable shared-memory technology.
Scalability study of solid xenon
Energy Technology Data Exchange (ETDEWEB)
Yoo, J.; Cease, H.; Jaskierny, W. F.; Markley, D.; Pahlka, R. B.; Balakishiyeva, D.; Saab, T.; Filipenko, M.
2015-04-01
We report a demonstration of the scalability of optically transparent xenon in the solid phase for use as a particle detector above a kilogram scale. We employed a cryostat cooled by liquid nitrogen combined with a xenon purification and chiller system. A modified {\\it Bridgeman's technique} reproduces a large scale optically transparent solid xenon.
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...
Subspace-Based Holistic Registration for Low-Resolution Facial Images
National Research Council Canada - National Science Library
Boom, B.J; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J
2010-01-01
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...
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...
Lattices Generated by Orbits of Subspaces under Finite Singular Orthogonal Groups II
Directory of Open Access Journals (Sweden)
You Gao
2012-01-01
Full Text Available Let q(2ν+δ+l be a (2ν+δ+l-dimensional vector space over the finite field q. In this paper we assume that q is a finite field of odd characteristic, and O2ν+δ+l, Δ(q the singular orthogonal groups of degree 2ν+δ+l over q. Let ℳ be any orbit of subspaces under O2ν+δ+l, Δ(q. Denote by ℒ the set of subspaces which are intersections of subspaces in ℳ, where we make the convention that the intersection of an empty set of subspaces of q(2ν+δ+l is assumed to be q(2ν+δ+l. By ordering ℒ by ordinary or reverse inclusion, two lattices are obtained. This paper studies the questions when these lattices ℒ are geometric lattices.
2017-09-27
Krylov subspace. dimension reduction, Krylov subspaces, truncated SVD, iterative methods , matrix regularization 32 Alex Breuer 410-278...tasks in domains ranging from dimension reduction using principal component analysis (PCA),1 to eigenfaces2 in machine learning , to latent semantic...indexing (LSI)3 in in- formation retrieval, matrix regularization,4,5 and sundry methods in signal process- ing.6,7 It is the core operation of data
Entanglement dynamics of coupled qubits and a semi-decoherence free subspace
Energy Technology Data Exchange (ETDEWEB)
Campagnano, Gabriele [II Institut fuer Theoretische Physik, Universitaet Stuttgart (Germany); Hamma, Alioscia, E-mail: ahamma@perimeterinstitute.c [Perimeter Institute for Theoretical Physics, 31 Caroline St. N, N2L 2Y5, Waterloo ON (Canada); Massachusetts Institute of Technology, Research Laboratory of Electronics, 77 Massachusetts Ave., Cambridge, MA 02139 (United States); Weiss, Ulrich [II Institut fuer Theoretische Physik, Universitaet Stuttgart (Germany)
2010-01-04
We study the entanglement dynamics and relaxation properties of a system of two interacting qubits in the cases of (I) two independent bosonic baths and (II) one common bath. We find that in the case (II) the existence of a decoherence-free subspace (DFS) makes entanglement dynamics very rich. We show that when the system is initially in a state with a component in the DFS the relaxation time is surprisingly long, showing the existence of semi-decoherence free subspaces.
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.
MODAL TRACKING of A Structural Device: A Subspace Identification Approach
Energy Technology Data Exchange (ETDEWEB)
Candy, J. V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Franco, S. N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ruggiero, E. L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Emmons, M. C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lopez, I. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stoops, L. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-03-20
Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation of the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.
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.
Generalized shift-invariant systems and frames for subspaces
DEFF Research Database (Denmark)
Christensen, Ole; Eldar, Y.C.
2005-01-01
)(j is an element of J,k is an element of Z) are Bessel sequences, we are interested in expansions [GRAPHICS] Our main result gives an equivalent condition for this to hold in a more general setting than described here, where translation by k is an element of Z(d) is replaced by translation via the action......Let T-k denote translation by k is an element of Z(d). Given countable collections of functions {phi(j)}(j is an element of J), {(phi) over bar (j)}(j is an element of J) subset of L-2(R-d) and assuming that {T(k)phi(j)}(j is an element of J,k is an element of Z)(d) and {T-k(phi) over bar (j)} (d...... of a matrix. As special cases of our result we find conditions for shift-invariant systems, Gabor systems, and wavelet systems to generate a subspace frame with a corresponding dual having the same structure....
A multifaceted independent performance analysis of facial subspace recognition algorithms.
Bajwa, Usama Ijaz; Taj, Imtiaz Ahmad; Anwar, Muhammad Waqas; Wang, Xuan
2013-01-01
Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)(2)PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.
Optimal Design of Large Dimensional Adaptive Subspace Detectors
Ben Atitallah, Ismail
2016-05-27
This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.
Design and Implementation of Ceph: A Scalable Distributed File System
Energy Technology Data Exchange (ETDEWEB)
Weil, S A; Brandt, S A; Miller, E L; Long, D E; Maltzahn, C
2006-04-19
File system designers continue to look to new architectures to improve scalability. Object-based storage diverges from server-based (e.g. NFS) and SAN-based storage systems by coupling processors and memory with disk drives, delegating low-level allocation to object storage devices (OSDs) and decoupling I/O (read/write) from metadata (file open/close) operations. Even recent object-based systems inherit decades-old architectural choices going back to early UNIX file systems, however, limiting their ability to effectively scale to hundreds of petabytes. We present Ceph, a distributed file system that provides excellent performance and reliability with unprecedented scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable OSDs. We leverage OSD intelligence to distribute data replication, failure detection and recovery with semi-autonomous OSDs running a specialized local object storage file system (EBOFS). Finally, Ceph is built around a dynamic distributed metadata management cluster that provides extremely efficient metadata management that seamlessly adapts to a wide range of general purpose and scientific computing file system workloads. We present performance measurements under a variety of workloads that show superior I/O performance and scalable metadata management (more than a quarter million metadata ops/sec).
Quality scalable video data stream
Wiegand, T.; Kirchhoffer, H.; Schwarz, H
2008-01-01
An apparatus for generating a quality-scalable video data stream (36) is described which comprises means (42) for coding a video signal (18) using block-wise transformation to obtain transform blocks (146, 148) of transformation coefficient values for a picture (140) of the video signal, a predetermined scan order (154, 156, 164, 166) with possible scan positions being defined among the transformation coefficient values within the transform blocks so that in each transform block, for each pos...
Constrained Multi-View Video Face Clustering.
Cao, Xiaochun; Zhang, Changqing; Zhou, Chengju; Fu, Huazhu; Foroosh, Hassan
2015-11-01
In this paper, we focus on face clustering in videos. To promote the performance of video clustering by multiple intrinsic cues, i.e., pairwise constraints and multiple views, we propose a constrained multi-view video face clustering method under a unified graph-based model. First, unlike most existing video face clustering methods which only employ these constraints in the clustering step, we strengthen the pairwise constraints through the whole video face clustering framework, both in sparse subspace representation and spectral clustering. In the constrained sparse subspace representation, the sparse representation is forced to explore unknown relationships. In the constrained spectral clustering, the constraints are used to guide for learning more reasonable new representations. Second, our method considers both the video face pairwise constraints as well as the multi-view consistence simultaneously. In particular, the graph regularization enforces the pairwise constraints to be respected and the co-regularization penalizes the disagreement among different graphs of multiple views. Experiments on three real-world video benchmark data sets demonstrate the significant improvements of our method over the state-of-the-art methods.
Ferdosi, Bilkis J.; Buddelmeijer, Hugo; Trager, Scott; Wilkinson, Michael H.F.; Roerdink, Jos B.T.M.
2010-01-01
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge.
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.
Scalable Techniques for Formal Verification
Ray, Sandip
2010-01-01
This book presents state-of-the-art approaches to formal verification techniques to seamlessly integrate different formal verification methods within a single logical foundation. It should benefit researchers and practitioners looking to get a broad overview of the spectrum of formal verification techniques, as well as approaches to combining such techniques within a single framework. Coverage includes a range of case studies showing how such combination is fruitful in developing a scalable verification methodology for industrial designs. This book outlines both theoretical and practical issue
Flexible scalable photonic manufacturing method
Skunes, Timothy A.; Case, Steven K.
2003-06-01
A process for flexible, scalable photonic manufacturing is described. Optical components are actively pre-aligned and secured to precision mounts. In a subsequent operation, the mounted optical components are passively placed onto a substrate known as an Optical Circuit Board (OCB). The passive placement may be either manual for low volume applications or with a pick-and-place robot for high volume applications. Mating registration features on the component mounts and the OCB facilitate accurate optical alignment. New photonic circuits may be created by changing the layout of the OCB. Predicted yield data from Monte Carlo tolerance simulations for two fiber optic photonic circuits is presented.
The Node Monitoring Component of a Scalable Systems Software Environment
Energy Technology Data Exchange (ETDEWEB)
Miller, Samuel James [Iowa State Univ., Ames, IA (United States)
2006-01-01
This research describes Fountain, a suite of programs used to monitor the resources of a cluster. A cluster is a collection of individual computers that are connected via a high speed communication network. They are traditionally used by users who desire more resources, such as processing power and memory, than any single computer can provide. A common drawback to effectively utilizing such a large-scale system is the management infrastructure, which often does not often scale well as the system grows. Large-scale parallel systems provide new research challenges in the area of systems software, the programs or tools that manage the system from boot-up to running a parallel job. The approach presented in this thesis utilizes a collection of separate components that communicate with each other to achieve a common goal. While systems software comprises a broad array of components, this thesis focuses on the design choices for a node monitoring component. We will describe Fountain, an implementation of the Scalable Systems Software (SSS) node monitor specification. It is targeted at aggregate node monitoring for clusters, focusing on both scalability and fault tolerance as its design goals. It leverages widely used technologies such as XML and HTTP to present an interface to other components in the SSS environment.
Scalable inference for stochastic block models
Peng, Chengbin
2017-12-08
Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference algorithms for such a model are increasingly limited due to their high time complexity and poor scalability. In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges. We also propose a parallel algorithm based on message passing. Our algorithm can overlap communication and computation, providing speedup without compromising accuracy as the number of processors grows. For example, to process a real-world graph with about 1.3 million nodes and 10 million edges, our algorithm requires about 6 seconds on 64 cores of a contemporary commodity Linux cluster. Experiments demonstrate that the algorithm can produce high quality results on both benchmark and real-world graphs. An example of finding more meaningful communities is illustrated consequently in comparison with a popular modularity maximization algorithm.
Estimation of direction of arrival of a moving target using subspace based approaches
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.
Signal Subspace Smoothing Technique for Time Delay Estimation Using MUSIC Algorithm.
Sun, Meng; Wang, Yide; Le Bastard, Cédric; Pan, Jingjing; Ding, Yuehua
2017-12-10
In civil engineering, Time Delay Estimation (TDE) is one of the most important tasks for the media structure and quality evaluation. In this paper, the MUSIC algorithm is applied to estimate the time delay. In practice, the backscattered echoes are highly correlated (even coherent). In order to apply the MUSIC algorithm, an adaptation of signal subspace smoothing is proposed to decorrelate the correlation between echoes. Unlike the conventional sub-band averaging techniques, we propose to directly use the signal subspace, which can take full advantage of the signal subspace and reduce the influence of noise. Moreover, the proposed method is adapted to deal with any radar pulse shape. The proposed method is tested on both numerical and experimental data. Both results show the effectiveness of the proposed method.
Highly Scalable Matching Pursuit Signal Decomposition Algorithm
National Aeronautics and Space Administration — In this research, we propose a variant of the classical Matching Pursuit Decomposition (MPD) algorithm with significantly improved scalability and computational...
Perceptual compressive sensing scalability in mobile video
Bivolarski, Lazar
2011-09-01
Scalability features embedded within the video sequences allows for streaming over heterogeneous networks to a variety of end devices. Compressive sensing techniques that will allow for lowering the complexity increase the robustness of the video scalability are reviewed. Human visual system models are often used in establishing perceptual metrics that would evaluate quality of video. Combining of perceptual and compressive sensing approach outlined from recent investigations. The performance and the complexity of different scalability techniques are evaluated. Application of perceptual models to evaluation of the quality of compressive sensing scalability is considered in the near perceptually lossless case and to the appropriate coding schemes is reviewed.
Subspace Method-based Blind SNR Estimation for Communication between Orbiters in Mars Exploration
Directory of Open Access Journals (Sweden)
Sun Ze-Zhou
2017-01-01
Full Text Available In Mars exploration the effective time of communication between orbiters is short, and the relative distance and gesture between them change fast. The signal to noise ratio (SNR estimation is required in receiver to change adaptively the data rate in the communication system. Therefore, SNR estimation is a key technique in adaptive data transmission. We propose a blind SNR estimation for communication between orbiters in Mars exploration via subspace method. The subspace method has better SNR estimation than some conventional SNR estimation algorithms. Numerical simulations demonstrate the effectiveness and improvement of the proposed algorithm.
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...
Application of the Krylov subspace method to the incompressible navier-stokes equations
Energy Technology Data Exchange (ETDEWEB)
Maeng, J.S. [Hanyang University, Seoul (Korea); Choi, I.K.; Lim, Y.W. [Hanyang University Graduate School, Seoul(Korea)
2000-07-01
The preconditioned Krylov subspace methods were applied to the incompressible Navier-Stoke's equations for convergence acceleration. Three of the Krylov subspace methods combined with the five of the preconditioners were tested to solve the lid-driven cavity flow problem. The MILU preconditioned CG method showed very fast and stable convergency. The combination of GMRES/MILU-CG solver for momentum and pressure correction equations was found less dependency on the number of the grid points among them. A guide line for stopping inner iterations for each equation is offered. (author). 18 refs., 4 figs., 6 tabs.
Application of the Krylov subspace method to the incompressible Navier-Stokes equations
Energy Technology Data Exchange (ETDEWEB)
Maeng, J.S. [Hangyang University, Seoul (Korea); Lim, Y.W.; Choi, I.K. [Graduate School, Hanyang University, Seoul (Korea)
1999-04-01
In this article, application of the Krylov subspace method was presented in solving a two dimensional driven-cavity flow. Three algorithms of the Krylov subspace method, CG, CGSTAB(Bi-CG Stabilized) and GMRES method were tested. The preconditioners considered in this study include Jacobi, SSOR, IC, ILU, MILU. The early stage of the inner iteration is carefully investigated in the pressure correction equation using ADI and conjugate gradient methods with preconditioner. The MILU preconditioned CG shows fast and stable convergency. The combination of GMRES/MILU solver for momentum and pressure correction equations was found the best choice in this study. (author). 14 refs., 4 figs., 5 tabs.
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.
Robust subspace estimation using low-rank optimization theory and applications
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
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....
DEFF Research Database (Denmark)
Tatu, Aditya Jayant; Lauze, Francois Bernard; Sommer, Stefan Horst
2010-01-01
This paper deals with restricting curve evolution to a finite and not necessarily flat space of curves, obtained as a subspace of the infinite dimensional space of planar curves endowed with the usual but weak parametrization invariant curve L 2-metric.We first show how to solve differential...... of a 3-sphere and then a series of examples on a highly non-linear subspace of the space of closed spline curves, where we have restricted mean curvature motion, Geodesic Active contours and compute geodesic between two curves....
Event metadata records as a testbed for scalable data mining
van Gemmeren, P.; Malon, D.
2010-04-01
At a data rate of 200 hertz, event metadata records ("TAGs," in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise "data mining," but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.
Event metadata records as a testbed for scalable data mining
Energy Technology Data Exchange (ETDEWEB)
Gemmeren, P van; Malon, D, E-mail: gemmeren@anl.go [Argonne National Laboratory, Argonne, Illinois 60439 (United States)
2010-04-01
At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.
Scalable Performance Measurement and Analysis
Energy Technology Data Exchange (ETDEWEB)
Gamblin, Todd [Univ. of North Carolina, Chapel Hill, NC (United States)
2009-01-01
Concurrency levels in large-scale, distributed-memory supercomputers are rising exponentially. Modern machines may contain 100,000 or more microprocessor cores, and the largest of these, IBM's Blue Gene/L, contains over 200,000 cores. Future systems are expected to support millions of concurrent tasks. In this dissertation, we focus on efficient techniques for measuring and analyzing the performance of applications running on very large parallel machines. Tuning the performance of large-scale applications can be a subtle and time-consuming task because application developers must measure and interpret data from many independent processes. While the volume of the raw data scales linearly with the number of tasks in the running system, the number of tasks is growing exponentially, and data for even small systems quickly becomes unmanageable. Transporting performance data from so many processes over a network can perturb application performance and make measurements inaccurate, and storing such data would require a prohibitive amount of space. Moreover, even if it were stored, analyzing the data would be extremely time-consuming. In this dissertation, we present novel methods for reducing performance data volume. The first draws on multi-scale wavelet techniques from signal processing to compress systemwide, time-varying load-balance data. The second uses statistical sampling to select a small subset of running processes to generate low-volume traces. A third approach combines sampling and wavelet compression to stratify performance data adaptively at run-time and to reduce further the cost of sampled tracing. We have integrated these approaches into Libra, a toolset for scalable load-balance analysis. We present Libra and show how it can be used to analyze data from large scientific applications scalably.
Cluster analysis of multiple planetary flow regimes
Mo, Kingtse; Ghil, Michael
1988-01-01
A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.
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...
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
In interactive physical simulation, contact forces are applied to prevent rigid bodies from penetrating and to control slipping between bodies. Accurate contact force determination is a computationally hard problem. Thus, in practice one trades accuracy for performance. This results in visual art......–Seidel method with a subspace minimization method. Our new method shows improved qualities and superior convergence properties for specific configurations....
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
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...
A block Krylov subspace time-exact solution method for linear ordinary differential equation systems
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
Vorst, H.A. van der; Ye, Q.
1999-01-01
In this paper, a strategy is proposed for alternative computations of the residual vectors in Krylov subspace methods, which improves the agreement of the computed residuals and the true residuals to the level of O(u)kAkkxk. Building on earlier ideas on residual replacement and on insights in
Prewhitening for Narrow-Band Noise in Subspace Methods for Noise Reduction
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2004-01-01
A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, typically when...
Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2005-01-01
A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...
A relaxed-certificate facial reduction algorithm based on subspace intersection
DEFF Research Database (Denmark)
Friberg, Henrik Alsing
2016-01-01
A “facial reduction”-like regularization algorithm is established for general conic optimization problems by relaxing requirements on the reduction certificates. This yields a rapid subspace reduction algorithm challenged only by representational issues of the regularized cone. A condition...
Subspace-Based Holistic Registration for Low-Resolution Facial Images
Boom, B.J.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.
2010-01-01
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
A block Krylov subspace time-exact solution method for linear ODE systems
Bochev, Mikhail A.
We propose a time-exact Krylov-subspace-based method for solving linear ODE (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
Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections
Hong Liang
2015-01-01
Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and $\\sigma$-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.
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...
Two stage DOA and Fundamental Frequency Estimation based on Subspace Techniques
DEFF Research Database (Denmark)
Zhou, Zhenhua; Christensen, Mads Græsbøll; So, Hing-Cheung
2012-01-01
In this paper, the problem of fundamental frequency and direction-of-arrival (DOA) estimation for multi-channel harmonic sinusoidal signal is addressed. The estimation procedure consists of two stages. Firstly, by making use of the subspace technique and Markov-based eigenanalysis, a multi- chann...
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 i...
Krylov subspace method for evaluating the self-energy matrices in electron transport calculations
DEFF Research Database (Denmark)
Sørensen, Hans Henrik Brandenborg; Hansen, Per Christian; Petersen, D. E.
2008-01-01
We present a Krylov subspace method for evaluating the self-energy matrices used in the Green's function formulation of electron transport in nanoscale devices. A procedure based on the Arnoldi method is employed to obtain solutions of the quadratic eigenvalue problem associated with the infinite...
Partially supervised speaker clustering.
Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S
2012-05-01
Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical
Hierarchical Sets: Analyzing Pangenome Structure through Scalable Set Visualizations
DEFF Research Database (Denmark)
Pedersen, Thomas Lin
2017-01-01
information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis. We present a new hierarchical clustering algorithm based on set arithmetics that optimizes the intersection sizes...... along the branches. The intersection and union sizes along the hierarchy are visualized using a composite dendrogram and icicle plot, which, in pangenome context, shows the evolution of pangenome and core size along the evolutionary hierarchy. Outlying elements, i.e. elements whose presence pattern do...... of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis. The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https...
Directory of Open Access Journals (Sweden)
Steven L Brunton
Full Text Available 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
Scalable privacy-preserving big data aggregation mechanism
Directory of Open Access Journals (Sweden)
Dapeng Wu
2016-08-01
Full Text Available As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.
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
Myria: Scalable Analytics as a Service
Howe, B.; Halperin, D.; Whitaker, A.
2014-12-01
At the UW eScience Institute, we're working to empower non-experts, especially in the sciences, to write and use data-parallel algorithms. To this end, we are building Myria, a web-based platform for scalable analytics and data-parallel programming. Myria's internal model of computation is the relational algebra extended with iteration, such that every program is inherently data-parallel, just as every query in a database is inherently data-parallel. But unlike databases, iteration is a first class concept, allowing us to express machine learning tasks, graph traversal tasks, and more. Programs can be expressed in a number of languages and can be executed on a number of execution environments, but we emphasize a particular language called MyriaL that supports both imperative and declarative styles and a particular execution engine called MyriaX that uses an in-memory column-oriented representation and asynchronous iteration. We deliver Myria over the web as a service, providing an editor, performance analysis tools, and catalog browsing features in a single environment. We find that this web-based "delivery vector" is critical in reaching non-experts: they are insulated from irrelevant effort technical work associated with installation, configuration, and resource management. The MyriaX backend, one of several execution runtimes we support, is a main-memory, column-oriented, RDBMS-on-the-worker system that supports cyclic data flows as a first-class citizen and has been shown to outperform competitive systems on 100-machine cluster sizes. I will describe the Myria system, give a demo, and present some new results in large-scale oceanographic microbiology.
Quality Scalability Aware Watermarking for Visual Content.
Bhowmik, Deepayan; Abhayaratne, Charith
2016-11-01
Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.
Grassmann Averages for Scalable Robust PCA
DEFF Research Database (Denmark)
Hauberg, Søren; Feragen, Aasa; Black, Michael J.
2014-01-01
As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase—“big data” implies “big outliers”. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbit......, making it scalable to “big noisy data.” We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie....
Fast and scalable inequality joins
Khayyat, Zuhair
2016-09-07
Inequality joins, which is to join relations with inequality conditions, are used in various applications. Optimizing joins has been the subject of intensive research ranging from efficient join algorithms such as sort-merge join, to the use of efficient indices such as (Formula presented.)-tree, (Formula presented.)-tree and Bitmap. However, inequality joins have received little attention and queries containing such joins are notably very slow. In this paper, we introduce fast inequality join algorithms based on sorted arrays and space-efficient bit-arrays. We further introduce a simple method to estimate the selectivity of inequality joins which is then used to optimize multiple predicate queries and multi-way joins. Moreover, we study an incremental inequality join algorithm to handle scenarios where data keeps changing. We have implemented a centralized version of these algorithms on top of PostgreSQL, a distributed version on top of Spark SQL, and an existing data cleaning system, Nadeef. By comparing our algorithms against well-known optimization techniques for inequality joins, we show our solution is more scalable and several orders of magnitude faster. © 2016 Springer-Verlag Berlin Heidelberg
Scalable encryption using alpha rooting
Wharton, Eric J.; Panetta, Karen A.; Agaian, Sos S.
2008-04-01
Full and partial encryption methods are important for subscription based content providers, such as internet and cable TV pay channels. Providers need to be able to protect their products while at the same time being able to provide demonstrations to attract new customers without giving away the full value of the content. If an algorithm were introduced which could provide any level of full or partial encryption in a fast and cost effective manner, the applications to real-time commercial implementation would be numerous. In this paper, we present a novel application of alpha rooting, using it to achieve fast and straightforward scalable encryption with a single algorithm. We further present use of the measure of enhancement, the Logarithmic AME, to select optimal parameters for the partial encryption. When parameters are selected using the measure, the output image achieves a balance between protecting the important data in the image while still containing a good overall representation of the image. We will show results for this encryption method on a number of images, using histograms to evaluate the effectiveness of the encryption.
Microarray data mining using landmark gene-guided clustering
Directory of Open Access Journals (Sweden)
Cho HyungJun
2008-02-01
Full Text Available Abstract Background Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset. Results By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters. Conclusion We have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes, known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code are freely available under the GPL license. The source code is available [see
Finite Element Modeling on Scalable Parallel Computers
Cwik, T.; Zuffada, C.; Jamnejad, V.; Katz, D.
1995-01-01
A coupled finite element-integral equation was developed to model fields scattered from inhomogenous, three-dimensional objects of arbitrary shape. This paper outlines how to implement the software on a scalable parallel processor.
Corfu: A Platform for Scalable Consistency
Wei, Michael
2017-01-01
Corfu is a platform for building systems which are extremely scalable, strongly consistent and robust. Unlike other systems which weaken guarantees to provide better performance, we have built Corfu with a resilient fabric tuned and engineered for scalability and strong consistency at its core: the Corfu shared log. On top of the Corfu log, we have built a layer of advanced data services which leverage the properties of the Corfu log. Today, Corfu is already replacing data platforms in commer...
Visual analytics in scalable visualization environments
Yamaoka, So
2011-01-01
Visual analytics is an interdisciplinary field that facilitates the analysis of the large volume of data through interactive visual interface. This dissertation focuses on the development of visual analytics techniques in scalable visualization environments. These scalable visualization environments offer a high-resolution, integrated virtual space, as well as a wide-open physical space that affords collaborative user interaction. At the same time, the sheer scale of these environments poses ...
Fully scalable video coding in multicast applications
Lerouge, Sam; De Sutter, Robbie; Lambert, Peter; Van de Walle, Rik
2004-01-01
The increasing diversity of the characteristics of the terminals and networks that are used to access multimedia content through the internet introduces new challenges for the distribution of multimedia data. Scalable video coding will be one of the elementary solutions in this domain. This type of coding allows to adapt an encoded video sequence to the limitations of the network or the receiving device by means of very basic operations. Algorithms for creating fully scalable video streams, in which multiple types of scalability are offered at the same time, are becoming mature. On the other hand, research on applications that use such bitstreams is only recently emerging. In this paper, we introduce a mathematical model for describing such bitstreams. In addition, we show how we can model applications that use scalable bitstreams by means of definitions that are built on top of this model. In particular, we chose to describe a multicast protocol that is targeted at scalable bitstreams. This way, we will demonstrate that it is possible to define an abstract model for scalable bitstreams, that can be used as a tool for reasoning about such bitstreams and related applications.
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.
Cumulant-Based Coherent Signal Subspace Method for Bearing and Range Estimation
Saidi, Zineb; Bourennane, Salah
2006-12-01
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.
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.
Active subspace approach to reliability and safety assessments of small satellite separation
Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Tuo, Zhouhui; Yao, Wen
2017-02-01
Ever-increasing launch of small satellites demands an effective and efficient computer-aided analysis approach to shorten the ground test cycle and save the economic cost. However, the multiple influencing factors hamper the efficiency and accuracy of separation reliability assessment. In this study, a novel evaluation approach based on active subspace identification and response surface construction is established and verified. The formulation of small satellite separation is firstly derived, including equations of motion, separation and gravity forces, and quantity of interest. The active subspace reduces the dimension of uncertain inputs with minimum precision loss and a 4th degree multivariate polynomial regression (MPR) using cross validation is hand-coded for the propagation and error analysis. A common spring separation of small satellites is employed to demonstrate the accuracy and efficiency of the approach, which exhibits its potential use in widely existing needs of satellite separation analysis.
Visual tracking based on the sparse representation of the PCA subspace
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.
A repeatable inverse kinematics algorithm with linear invariant subspaces for mobile manipulators.
Tchoń, Krzysztof; Jakubiak, Janusz
2005-10-01
On the basis of a geometric characterization of repeatability we present a repeatable extended Jacobian inverse kinematics algorithm for mobile manipulators. The algorithm's dynamics have linear invariant subspaces in the configuration space. A standard Ritz approximation of platform controls results in a band-limited version of this algorithm. Computer simulations involving an RTR manipulator mounted on a kinematic car-type mobile platform are used in order to illustrate repeatability and performance of the algorithm.
Energy Technology Data Exchange (ETDEWEB)
Fattebert, J
2008-07-29
We describe an iterative algorithm to solve electronic structure problems in Density Functional Theory. The approach is presented as a Subspace Accelerated Inexact Newton (SAIN) solver for the non-linear Kohn-Sham equations. It is related to a class of iterative algorithms known as RMM-DIIS in the electronic structure community. The method is illustrated with examples of real applications using a finite difference discretization and multigrid preconditioning.
Subspace-Based Holistic Registration for Low-Resolution Facial Images
Boom BJ; Spreeuwers LJ; Veldhuis RNJ
2010-01-01
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 t...
Sahadevan, R.; Prakash, P.
2017-01-01
We show how invariant subspace method can be extended to time fractional coupled nonlinear partial differential equations and construct their exact solutions. Effectiveness of the method has been illustrated through time fractional Hunter-Saxton equation, time fractional coupled nonlinear diffusion system, time fractional coupled Boussinesq equation and time fractional Whitman-Broer-Kaup system. Also we explain how maximal dimension of the time fractional coupled nonlinear partial differential equations can be estimated.
Hongyun Qin; Ruqiang Yan; Aiguo Song; Hong Zeng
2013-01-01
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked int...
Shahbazi Avarvand, Forooz; Ewald, Arne; Nolte, Guido
2012-01-01
To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method "RAP-MUSIC" to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.
Directory of Open Access Journals (Sweden)
Forooz Shahbazi Avarvand
2012-01-01
Full Text Available To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.
Subspace-based damage detection under changes in the ambient excitation statistics
Döhler, Michael; Mevel, Laurent; Hille, Falk
2014-03-01
In the last ten years, monitoring the integrity of the civil infrastructure has been an active research topic, including in connected areas as automatic control. It is common practice to perform damage detection by detecting changes in the modal parameters between a reference state and the current (possibly damaged) state from measured vibration data. Subspace methods enjoy some popularity in structural engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, a subspace-based fault detection residual has been recently proposed and applied successfully, where the estimation of the modal parameters in the possibly damaged state is avoided. However, most works assume that the unmeasured ambient excitation properties during measurements of the structure in the reference and possibly damaged condition stay constant, which is hardly satisfied by any application. This paper addresses the problem of robustness of such fault detection methods. It is explained why current algorithms from literature fail when the excitation covariance changes and how they can be modified. Then, an efficient and fast subspace-based damage detection test is derived that is robust to changes in the excitation covariance but also to numerical instabilities that can arise easily in the computations. Three numerical applications show the efficiency of the new approach to better detect and separate different levels of damage even using a relatively low sample length.
Scalable fast multipole methods for vortex element methods
Hu, Qi
2012-11-01
We use a particle-based method to simulate incompressible flows, where the Fast Multipole Method (FMM) is used to accelerate the calculation of particle interactions. The most time-consuming kernelsâ\\'the Biot-Savart equation and stretching term of the vorticity equationâ\\'are mathematically reformulated so that only two Laplace scalar potentials are used instead of six, while automatically ensuring divergence-free far-field computation. Based on this formulation, and on our previous work for a scalar heterogeneous FMM algorithm, we develop a new FMM-based vortex method capable of simulating general flows including turbulence on heterogeneous architectures, which distributes the work between multi-core CPUs and GPUs to best utilize the hardware resources and achieve excellent scalability. The algorithm also uses new data structures which can dynamically manage inter-node communication and load balance efficiently but with only a small parallel construction overhead. This algorithm can scale to large-sized clusters showing both strong and weak scalability. Careful error and timing trade-off analysis are also performed for the cutoff functions induced by the vortex particle method. Our implementation can perform one time step of the velocity+stretching for one billion particles on 32 nodes in 55.9 seconds, which yields 49.12 Tflop/s. © 2012 IEEE.
Wanted: Scalable Tracers for Diffusion Measurements
2015-01-01
Scalable tracers are potentially a useful tool to examine diffusion mechanisms and to predict diffusion coefficients, particularly for hindered diffusion in complex, heterogeneous, or crowded systems. Scalable tracers are defined as a series of tracers varying in size but with the same shape, structure, surface chemistry, deformability, and diffusion mechanism. Both chemical homology and constant dynamics are required. In particular, branching must not vary with size, and there must be no transition between ordinary diffusion and reptation. Measurements using scalable tracers yield the mean diffusion coefficient as a function of size alone; measurements using nonscalable tracers yield the variation due to differences in the other properties. Candidate scalable tracers are discussed for two-dimensional (2D) diffusion in membranes and three-dimensional diffusion in aqueous solutions. Correlations to predict the mean diffusion coefficient of globular biomolecules from molecular mass are reviewed briefly. Specific suggestions for the 3D case include the use of synthetic dendrimers or random hyperbranched polymers instead of dextran and the use of core–shell quantum dots. Another useful tool would be a series of scalable tracers varying in deformability alone, prepared by varying the density of crosslinking in a polymer to make say “reinforced Ficoll” or “reinforced hyperbranched polyglycerol.” PMID:25319586
Scalable L-infinite coding of meshes.
Munteanu, Adrian; Cernea, Dan C; Alecu, Alin; Cornelis, Jan; Schelkens, Peter
2010-01-01
The paper investigates the novel concept of local-error control in mesh geometry encoding. In contrast to traditional mesh-coding systems that use the mean-square error as target distortion metric, this paper proposes a new L-infinite mesh-coding approach, for which the target distortion metric is the L-infinite distortion. In this context, a novel wavelet-based L-infinite-constrained coding approach for meshes is proposed, which ensures that the maximum error between the vertex positions in the original and decoded meshes is lower than a given upper bound. Furthermore, the proposed system achieves scalability in L-infinite sense, that is, any decoding of the input stream will correspond to a perfectly predictable L-infinite distortion upper bound. An instantiation of the proposed L-infinite-coding approach is demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this context, the advantages of scalable L-infinite coding over L-2-oriented coding are experimentally demonstrated. One concludes that the proposed L-infinite mesh-coding approach guarantees an upper bound on the local error in the decoded mesh, it enables a fast real-time implementation of the rate allocation, and it preserves all the scalability features and animation capabilities of the employed scalable mesh codec.
Scalable computing for evolutionary genomics.
Prins, Pjotr; Belhachemi, Dominique; Möller, Steffen; Smant, Geert
2012-01-01
Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques. Unfortunately, parallel programming is difficult and requires special software design. The alternative, especially attractive for legacy software, is to introduce poor man's parallelization by running whole programs in parallel as separate processes, using job schedulers. Such pipelines are often deployed on bioinformatics computer clusters. Recent advances in PC virtualization have made it possible to run a full computer operating system, with all of its installed software, on top of another operating system, inside a "box," or virtual machine (VM). Such a VM can flexibly be deployed on multiple computers, in a local network, e.g., on existing desktop PCs, and even in the Cloud, to create a "virtual" computer cluster. Many bioinformatics applications in evolutionary biology can be run in parallel, running processes in one or more VMs. Here, we show how a ready-made bioinformatics VM image, named BioNode, effectively creates a computing cluster, and pipeline, in a few steps. This allows researchers to scale-up computations from their desktop, using available hardware, anytime it is required. BioNode is based on Debian Linux and can run on networked PCs and in the Cloud. Over 200 bioinformatics and statistical software packages, of interest to evolutionary biology, are included, such as PAML, Muscle, MAFFT, MrBayes, and BLAST. Most of these software packages are maintained through the Debian Med project. In addition, BioNode contains convenient configuration scripts for parallelizing bioinformatics software. Where Debian Med encourages packaging free and open source bioinformatics software through one central project
Photonic Architecture for Scalable Quantum Information Processing in Diamond
Directory of Open Access Journals (Sweden)
Kae Nemoto
2014-08-01
Full Text Available Physics and information are intimately connected, and the ultimate information processing devices will be those that harness the principles of quantum mechanics. Many physical systems have been identified as candidates for quantum information processing, but none of them are immune from errors. The challenge remains to find a path from the experiments of today to a reliable and scalable quantum computer. Here, we develop an architecture based on a simple module comprising an optical cavity containing a single negatively charged nitrogen vacancy center in diamond. Modules are connected by photons propagating in a fiber-optical network and collectively used to generate a topological cluster state, a robust substrate for quantum information processing. In principle, all processes in the architecture can be deterministic, but current limitations lead to processes that are probabilistic but heralded. We find that the architecture enables large-scale quantum information processing with existing technology.
Scalable Open Source Smart Grid Simulator (SGSim)
DEFF Research Database (Denmark)
Ebeid, Emad Samuel Malki; Jacobsen, Rune Hylsberg; Quaglia, Davide
2017-01-01
The future smart power grid will consist of an unlimited number of smart devices that communicate with control units to maintain the grid’s sustainability, efficiency, and balancing. In order to build and verify such controllers over a large grid, a scalable simulation environment is needed....... This paper presents an open source smart grid simulator (SGSim). The simulator is based on open source SystemC Network Simulation Library (SCNSL) and aims to model scalable smart grid applications. SGSim has been tested under different smart grid scenarios that contain hundreds of thousands of households...... and appliances. By using SGSim, different smart grid control strategies and protocols can be tested, validated and evaluated in a scalable environment....
Highly Scalable Multiplication for Distributed Sparse Multivariate Polynomials on Many-core Systems
Gastineau, Mickael; Laskar, Jacques
2013-01-01
We present a highly scalable algorithm for multiplying sparse multivariate polynomials represented in a distributed format. This algo- rithm targets not only the shared memory multicore computers, but also computers clusters or specialized hardware attached to a host computer, such as graphics processing units or many-core coprocessors. The scal- ability on the large number of cores is ensured by the lacks of synchro- nizations, locks and false-sharing during the main parallel step.
Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection
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Wenfen Liu
2017-01-01
Full Text Available Constrained spectral clustering (CSC method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight.
From Digital Disruption to Business Model Scalability
DEFF Research Database (Denmark)
Nielsen, Christian; Lund, Morten; Thomsen, Peter Poulsen
2017-01-01
a long time to replicate, business model scalability can be cornered into four dimensions. In many corporate restructuring exercises and Mergers and Acquisitions there is a tendency to look for synergies in the form of cost reductions, lean workflows and market segments. However, this state of mind......This article discusses the terms disruption, digital disruption, business models and business model scalability. It illustrates how managers should be using these terms for the benefit of their business by developing business models capable of achieving exponentially increasing returns to scale...
From Digital Disruption to Business Model Scalability
DEFF Research Database (Denmark)
Nielsen, Christian; Lund, Morten; Thomsen, Peter Poulsen
2017-01-01
as a response to digital disruption. A series of case studies illustrate that besides frequent existing messages in the business literature relating to the importance of creating agile businesses, both in growing and declining economies, as well as hard to copy value propositions or value propositions that take......This article discusses the terms disruption, digital disruption, business models and business model scalability. It illustrates how managers should be using these terms for the benefit of their business by developing business models capable of achieving exponentially increasing returns to scale...... will seldom lead to business model scalability capable of competing with digital disruption(s)....
Energy Aware Clustering Algorithms for Wireless Sensor Networks
Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian
2011-09-01
The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.
Content-Aware Scalability-Type Selection for Rate Adaptation of Scalable Video
Directory of Open Access Journals (Sweden)
Tekalp A Murat
2007-01-01
Full Text Available Scalable video coders provide different scaling options, such as temporal, spatial, and SNR scalabilities, where rate reduction by discarding enhancement layers of different scalability-type results in different kinds and/or levels of visual distortion depend on the content and bitrate. This dependency between scalability type, video content, and bitrate is not well investigated in the literature. To this effect, we first propose an objective function that quantifies flatness, blockiness, blurriness, and temporal jerkiness artifacts caused by rate reduction by spatial size, frame rate, and quantization parameter scaling. Next, the weights of this objective function are determined for different content (shot types and different bitrates using a training procedure with subjective evaluation. Finally, a method is proposed for choosing the best scaling type for each temporal segment that results in minimum visual distortion according to this objective function given the content type of temporal segments. Two subjective tests have been performed to validate the proposed procedure for content-aware selection of the best scalability type on soccer videos. Soccer videos scaled from 600 kbps to 100 kbps by the proposed content-aware selection of scalability type have been found visually superior to those that are scaled using a single scalability option over the whole sequence.
... Genetics Services Directory Cancer Prevention Overview Research Cancer Clusters On This Page What is a cancer cluster? ... the number of cancer cases in the suspected cluster Many reported clusters include too few cancer cases ...
Clustering in Hilbert space of a quantum optimization problem
Morampudi, S. C.; Hsu, B.; Sondhi, S. L.; Moessner, R.; Laumann, C. R.
2017-10-01
The solution space of many classical optimization problems breaks up into clusters which are extensively distant from one another in the Hamming metric. Here, we show that an analogous quantum clustering phenomenon takes place in the ground-state subspace of a certain quantum optimization problem. This involves extending the notion of clustering to Hilbert space, where the classical Hamming distance is not immediately useful. Quantum clusters correspond to macroscopically distinct subspaces of the full quantum ground-state space which grow with the system size. We explicitly demonstrate that such clusters arise in the solution space of random quantum satisfiability (3-QSAT) at its satisfiability transition. We estimate both the number of these clusters and their internal entropy. The former are given by the number of hard-core dimer coverings of the core of the interaction graph, while the latter is related to the underconstrained degrees of freedom not touched by the dimers. We additionally provide numerical evidence suggesting that the 3-QSAT satisfiability transition may coincide with the product satisfiability transition, which would imply the absence of an intermediate entangled satisfiable phase.
Scalable Detection and Isolation of Phishing
Moreira Moura, Giovane; Pras, Aiko
2009-01-01
This paper presents a proposal for scalable detection and isolation of phishing. The main ideas are to move the protection from end users towards the network provider and to employ the novel bad neighborhood concept, in order to detect and isolate both phishing e-mail senders and phishing web
Scalable Open Source Smart Grid Simulator (SGSim)
DEFF Research Database (Denmark)
Ebeid, Emad Samuel Malki; Jacobsen, Rune Hylsberg; Stefanni, Francesco
2017-01-01
. This paper presents an open source smart grid simulator (SGSim). The simulator is based on open source SystemC Network Simulation Library (SCNSL) and aims to model scalable smart grid applications. SGSim has been tested under different smart grid scenarios that contain hundreds of thousands of households...
Realization of a scalable airborne radar
Halsema, D. van; Jongh, R.V. de; Es, J. van; Otten, M.P.G.; Vermeulen, B.C.B.; Liempt, L.J. van
2008-01-01
Modern airborne ground surveillance radar systems are increasingly based on Active Electronically Scanned Array (AESA) antennas. Efficient use of array technology and the need for radar solutions for various airborne platforms, manned and unmanned, leads to the design of scalable radar systems. The
Scalable Domain Decomposed Monte Carlo Particle Transport
Energy Technology Data Exchange (ETDEWEB)
O' Brien, Matthew Joseph [Univ. of California, Davis, CA (United States)
2013-12-05
In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation.
Subjective comparison of temporal and quality scalability
DEFF Research Database (Denmark)
Korhonen, Jari; Reiter, Ulrich; You, Junyong
2011-01-01
and quality scalability. The practical experiments with low resolution video sequences show that in general, distortion is a more crucial factor for the perceived subjective quality than frame rate. However, the results also depend on the content. Moreover,, we discuss the role of other different influence...
Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis
Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan
2017-09-01
It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus
Subspace-based additive fuzzy systems for classification and dimension reduction
Jauch, Thomas W.
1997-10-01
In classification tasks the appearance of high dimensional feature vectors and small datasets is a common problem. It is well known that these two characteristics usually result in an oversized model with poor generalization power. In this contribution a new way to cope with such tasks is presented which is based on the assumption that in high dimensional problems almost all data points are located in a low dimensional subspace. A way is proposed to design a fuzzy system on a unified framework, and to use it to develop a new model for classification tasks. It is shown that the new model can be understood as an additive fuzzy system with parameter based basis functions. Different parts of the models are only defined in a subspace of the whole feature space. The subspaces are not defined a priori but are subject to an optimization procedure as all other parameters of the model. The new model has the capability to cope with high feature dimensions. The model has similarities to projection pursuit and to the mixture of experts architecture. The model is trained in a supervised manner via conjugate gradients and logistic regression, or backfitting and conjugate gradients to handle classification tasks. An efficient initialization procedure is also presented. In addition a technique based on oblique projections is presented which enlarges the capabilities of the model to use data with missing features. It is possible to use data with missing features in the training and in the classification phase. Based on the design of the model, it is possible to prune certain basis functions with an OLS (orthogonal least squares) based technique in order to reduce the model size. Results are presented on an artificial and an application example.
An adaptive subspace trust-region method for frequency-domain seismic full waveform inversion
Zhang, Huan; Li, Xiaofan; Song, Hanjie; Liu, Shaolin
2015-05-01
Full waveform inversion is currently considered as a promising seismic imaging method to obtain high-resolution and quantitative images of the subsurface. It is a nonlinear ill-posed inverse problem, the main difficulty of which that prevents the full waveform inversion from widespread applying to real data is the sensitivity to incorrect initial models and noisy data. Local optimization theories including Newton's method and gradient method always lead the convergence to local minima, while global optimization algorithms such as simulated annealing are computationally costly. To confront this issue, in this paper we investigate the possibility of applying the trust-region method to the full waveform inversion problem. Different from line search methods, trust-region methods force the new trial step within a certain neighborhood of the current iterate point. Theoretically, the trust-region methods are reliable and robust, and they have very strong convergence properties. The capability of this inversion technique is tested with the synthetic Marmousi velocity model and the SEG/EAGE Salt model. Numerical examples demonstrate that the adaptive subspace trust-region method can provide solutions closer to the global minima compared to the conventional Approximate Hessian approach and the L-BFGS method with a higher convergence rate. In addition, the match between the inverted model and the true model is still excellent even when the initial model deviates far from the true model. Inversion results with noisy data also exhibit the remarkable capability of the adaptive subspace trust-region method for low signal-to-noise data inversions. Promising numerical results suggest this adaptive subspace trust-region method is suitable for full waveform inversion, as it has stronger convergence and higher convergence rate.
Large Scale Simulations of the Euler Equations on GPU Clusters
Liebmann, Manfred
2010-08-01
The paper investigates the scalability of a parallel Euler solver, using the Vijayasundaram method, on a GPU cluster with 32 Nvidia Geforce GTX 295 boards. The aim of this research is to enable large scale fluid dynamics simulations with up to one billion elements. We investigate communication protocols for the GPU cluster to compensate for the slow Gigabit Ethernet network between the GPU compute nodes and to maintain overall efficiency. A diesel engine intake-port and a nozzle, meshed in different resolutions, give good real world examples for the scalability tests on the GPU cluster. © 2010 IEEE.
On Similarity and Reducing Subspaces of the n-Shift plus Certain Weighted Volterra Operator
Directory of Open Access Journals (Sweden)
Yucheng Li
2017-01-01
Full Text Available Let g(z be an n-degree polynomial (n≥2. Inspired by Sarason’s result, we introduce the operator T1 defined by the multiplication operator Mg plus the weighted Volterra operator Vg on the Bergman space. We show that the operator T1 is similar to Mg on some Hilbert space Sg2(D. Then for g(z=zn, by using matrix manipulations, the reducing subspaces of the corresponding operator T2 on the Bergman space are characterized.
Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition
Directory of Open Access Journals (Sweden)
yuan Shuai
2017-01-01
Full Text Available In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.
Georgievskii, D. V.
2017-07-01
The mechanical meaning and the relationships among material constants in an n-dimensional isotropic elastic medium are discussed. The restrictions of the constitutive relations (Hooke's law) to subspaces of lower dimension caused by the conditions that an m-dimensional strain state or an m-dimensional stress state (1 ≤ m written out for any m-dimensional restriction are expressed in terms of one another. These expressions in terms of the known constants, for example, of a three-dimensional medium, i.e., the classical elastic constants, enable us to judge the material properties of this medium immersed in a space of larger dimension.
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.
Consistency analysis of subspace identification methods based on a linear regression approach
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
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......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...
Dai, Kaoshan; Wang, Ying; Lu, Wensheng; Ren, Xiaosong; Huang, Zhenhua
2017-04-01
Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.
A scalable method for parallelizing sampling-based motion planning algorithms
Jacobs, Sam Ade
2012-05-01
This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.
Energy Technology Data Exchange (ETDEWEB)
Druskin, V.; Knizhnerman, L.
1994-12-31
The authors solve the Cauchy problem for an ODE system Au + {partial_derivative}u/{partial_derivative}t = 0, u{vert_bar}{sub t=0} = {var_phi}, where A is a square real nonnegative definite symmetric matrix of the order N, {var_phi} is a vector from R{sup N}. The stiffness matrix A is obtained due to semi-discretization of a parabolic equation or system with time-independent coefficients. The authors are particularly interested in large stiff 3-D problems for the scalar diffusion and vectorial Maxwell`s equations. First they consider an explicit method in which the solution on a whole time interval is projected on a Krylov subspace originated by A. Then they suggest another Krylov subspace with better approximating properties using powers of an implicit transition operator. These Krylov subspace methods generate optimal in a spectral sense polynomial approximations for the solution of the ODE, similar to CG for SLE.
Hierarchical sets: analyzing pangenome structure through scalable set visualizations.
Pedersen, Thomas Lin
2017-06-01
The increase in available microbial genome sequences has resulted in an increase in the size of the pangenomes being analyzed. Current pangenome visualizations are not intended for the pangenome sizes possible today and new approaches are necessary in order to convert the increase in available information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis. We present a new hierarchical clustering algorithm based on set arithmetics that optimizes the intersection sizes along the branches. The intersection and union sizes along the hierarchy are visualized using a composite dendrogram and icicle plot, which, in pangenome context, shows the evolution of pangenome and core size along the evolutionary hierarchy. Outlying elements, i.e. elements whose presence pattern do not correspond with the hierarchy, can be visualized using hierarchical edge bundles. When applied to pangenome data this plot shows putative horizontal gene transfers between the genomes and can highlight relationships between genomes that is not represented by the hierarchy. We illustrate the utility of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis. The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN ( https://cran.r-project.org/web/packages/hierarchicalSets ). thomasp85@gmail.com. Supplementary data are available at Bioinformatics online.
Scalable and cost-effective NGS genotyping in the cloud.
Souilmi, Yassine; Lancaster, Alex K; Jung, Jae-Yoon; Rizzo, Ettore; Hawkins, Jared B; Powles, Ryan; Amzazi, Saaïd; Ghazal, Hassan; Tonellato, Peter J; Wall, Dennis P
2015-10-15
While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars. We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.
A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.
Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang
2014-02-25
In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.
A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise
Directory of Open Access Journals (Sweden)
Xianpeng Wang
2014-02-01
Full Text Available In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD and the direction of arrival (DOA for bistatic multiple-input multiple-output (MIMO radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen’s method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.
ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces.
Ivannikov, Andriy; Kalyakin, Igor; Hämäläinen, Jarmo; Leppänen, Paavo H T; Ristaniemi, Tapani; Lyytinen, Heikki; Kärkkäinen, Tommi
2009-06-15
In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The interpretation of the results and the performance of the proposed method under conditions, when the basic assumptions are violated - e.g. the problem is underdetermined - are also discussed. Moreover, we study how the factors of the number of channels and trials used by the method influence the effectiveness of ERP/noise subspaces separation. In addition, we explore also the impact of different data resampling strategies on the performance of the considered algorithm. The results can help in determining the optimal parameters of the equipment/methods used to elicit and reliably estimate ERPs.
Energy Technology Data Exchange (ETDEWEB)
Abdel-Khalik, Hany S. [North Carolina State Univ., Raleigh, NC (United States); Zhang, Qiong [North Carolina State Univ., Raleigh, NC (United States)
2014-05-20
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.
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.
A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise
Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang
2014-01-01
In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method. PMID:24573313
High resolution through-the-wall radar image based on beamspace eigenstructure subspace methods
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.
Alexander, Andrew S; Nitz, Douglas A
2017-06-05
Traversal of a complicated route is often facilitated by considering it as a set of related sub-spaces. Such compartmentalization processes could occur within retrosplenial cortex, a structure whose neurons simultaneously encode position within routes and other spatial coordinate systems. Here, retrosplenial cortex neurons were recorded as rats traversed a track having recurrent structure at multiple scales. Consistent with a major role in compartmentalization of complex routes, individual retrosplenial cortex (RSC) neurons exhibited periodic activation patterns that repeated across route segments having the same shape. Concurrently, a larger population of RSC neurons exhibited single-cycle periodicity over the full route, effectively defining a framework for encoding of sub-route positions relative to the whole. The same population simultaneously provides a novel metric for distance from each route position to all others. Together, the findings implicate retrosplenial cortex in the extraction of path sub-spaces, the encoding of their spatial relationships to each other, and path integration. Copyright © 2017 Elsevier Ltd. All rights reserved.
2012-04-20
in GMRES by replacing the modied Gram-Schmidt orthogonalization process with Householder QR [25]. All these authors used the monomial basis, and... monomial basis, which motivated research into the use of other bases for the Krylov Subspace. Hindmarsh and Walker used a scaled (normalized) monomial ...subspace. In the monomial basis, θi and σi are always 0, and γi = 1. For Newton, γi = 1, σi = 0, and θi are chosen to be eigenvalue estimates (Ritz
Directory of Open Access Journals (Sweden)
Søren Holdt Jensen
2007-01-01
Full Text Available 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 analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing.
Choroszavin, Sergej A.
1999-01-01
Definition. Let J be a period-2 unitary operator (some people say J is reflection operator or reflection symmetry) and U be a linear operator. If U^*JU = J (resp. U^*JU >= J) then U is said to be J-isometry (resp. J-noncontraction). If U^*JU >= J and UJU^* >= J) then U is said to be J-binoncontraction). Theorem. If every J-isometry has nontrivial positive invariant subspace then every J-noncontraction has such a subspace. Theorem. If every J-binoncontractive J-isometry has maximal positive in...
La Cour, Brian R.; Ostrove, Corey I.
2017-01-01
This paper describes a novel approach to solving unstructured search problems using a classical, signal-based emulation of a quantum computer. The classical nature of the representation allows one to perform subspace projections in addition to the usual unitary gate operations. Although bandwidth requirements will limit the scale of problems that can be solved by this method, it can nevertheless provide a significant computational advantage for problems of limited size. In particular, we find that, for the same number of noisy oracle calls, the proposed subspace projection method provides a higher probability of success for finding a solution than does an single application of Grover's algorithm on the same device.
fastBMA: scalable network inference and transitive reduction.
Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee
2017-10-01
Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.
Directory of Open Access Journals (Sweden)
Johnson Thie
2004-03-01
Full Text Available We are concerned with the efficient transmission of scalable compressed data over lossy communication channels. Recent works have proposed several strategies for assigning optimal code redundancies to elements in a scalable data stream under the assumption that all elements are encoded onto a common group of network packets. When the size of the data to be encoded becomes large in comparison to the size of the network packets, such schemes require very long channel codes with high computational complexity. In networks with high loss, small packets are generally more desirable than long packets. This paper proposes a robust strategy for optimally assigning elements of the scalable data to clusters of packets, subject to constraints on packet size and code complexity. Given a packet cluster arrangement, the scheme then assigns optimal code redundancies to the source elements subject to a constraint on transmission length. Experimental results show that the proposed strategy can outperform previously proposed code redundancy assignment policies subject to the above-mentioned constraints, particularly at high channel loss rates.
Scalable Atomistic Simulation Algorithms for Materials Research
Directory of Open Access Journals (Sweden)
Aiichiro Nakano
2002-01-01
Full Text Available A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD simulations and quantum-mechanical (QM calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.
A Scalability Model for ECS's Data Server
Menasce, Daniel A.; Singhal, Mukesh
1998-01-01
This report presents in four chapters a model for the scalability analysis of the Data Server subsystem of the Earth Observing System Data and Information System (EOSDIS) Core System (ECS). The model analyzes if the planned architecture of the Data Server will support an increase in the workload with the possible upgrade and/or addition of processors, storage subsystems, and networks. The approaches in the report include a summary of the architecture of ECS's Data server as well as a high level description of the Ingest and Retrieval operations as they relate to ECS's Data Server. This description forms the basis for the development of the scalability model of the data server and the methodology used to solve it.
Stencil Lithography for Scalable Micro- and Nanomanufacturing
Directory of Open Access Journals (Sweden)
Ke Du
2017-04-01
Full Text Available In this paper, we review the current development of stencil lithography for scalable micro- and nanomanufacturing as a resistless and reusable patterning technique. We first introduce the motivation and advantages of stencil lithography for large-area micro- and nanopatterning. Then we review the progress of using rigid membranes such as SiNx and Si as stencil masks as well as stacking layers. We also review the current use of flexible membranes including a compliant SiNx membrane with springs, polyimide film, polydimethylsiloxane (PDMS layer, and photoresist-based membranes as stencil lithography masks to address problems such as blurring and non-planar surface patterning. Moreover, we discuss the dynamic stencil lithography technique, which significantly improves the patterning throughput and speed by moving the stencil over the target substrate during deposition. Lastly, we discuss the future advancement of stencil lithography for a resistless, reusable, scalable, and programmable nanolithography method.
SPRNG Scalable Parallel Random Number Generator LIbrary
Energy Technology Data Exchange (ETDEWEB)
2010-03-16
This revision corrects some errors in SPRNG 1. Users of newer SPRNG versions can obtain the corrected files and build their version with it. This version also improves the scalability of some of the application-based tests in the SPRNG test suite. It also includes an interface to a parallel Mersenne Twister, so that if users install the Mersenne Twister, then they can test this generator with the SPRNG test suite and also use some SPRNG features with that generator.
Bitcoin-NG: A Scalable Blockchain Protocol
Eyal, Ittay; Gencer, Adem Efe; Sirer, Emin Gun; Renesse, Robbert,
2015-01-01
Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoin-derived blockchain protocols have inherent scalability limits that trade-off between throughput and latency and withhold the realization of this potential. This paper presents Bitcoin-NG, a new blockchain protocol designed to scale. Based on Bitcoin's blockchain protocol, Bitcoin-NG is By...
Stencil Lithography for Scalable Micro- and Nanomanufacturing
Ke Du; Junjun Ding; Yuyang Liu; Ishan Wathuthanthri; Chang-Hwan Choi
2017-01-01
In this paper, we review the current development of stencil lithography for scalable micro- and nanomanufacturing as a resistless and reusable patterning technique. We first introduce the motivation and advantages of stencil lithography for large-area micro- and nanopatterning. Then we review the progress of using rigid membranes such as SiNx and Si as stencil masks as well as stacking layers. We also review the current use of flexible membranes including a compliant SiNx membrane with spring...
Scalable robotic biofabrication of tissue spheroids
Energy Technology Data Exchange (ETDEWEB)
Mehesz, A Nagy; Hajdu, Z; Visconti, R P; Markwald, R R; Mironov, V [Advanced Tissue Biofabrication Center, Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC (United States); Brown, J [Department of Mechanical Engineering, Clemson University, Clemson, SC (United States); Beaver, W [York Technical College, Rock Hill, SC (United States); Da Silva, J V L, E-mail: mironovv@musc.edu [Renato Archer Information Technology Center-CTI, Campinas (Brazil)
2011-06-15
Development of methods for scalable biofabrication of uniformly sized tissue spheroids is essential for tissue spheroid-based bioprinting of large size tissue and organ constructs. The most recent scalable technique for tissue spheroid fabrication employs a micromolded recessed template prepared in a non-adhesive hydrogel, wherein the cells loaded into the template self-assemble into tissue spheroids due to gravitational force. In this study, we present an improved version of this technique. A new mold was designed to enable generation of 61 microrecessions in each well of a 96-well plate. The microrecessions were seeded with cells using an EpMotion 5070 automated pipetting machine. After 48 h of incubation, tissue spheroids formed at the bottom of each microrecession. To assess the quality of constructs generated using this technology, 600 tissue spheroids made by this method were compared with 600 spheroids generated by the conventional hanging drop method. These analyses showed that tissue spheroids fabricated by the micromolded method are more uniform in diameter. Thus, use of micromolded recessions in a non-adhesive hydrogel, combined with automated cell seeding, is a reliable method for scalable robotic fabrication of uniform-sized tissue spheroids.
DISP: Optimizations towards Scalable MPI Startup
Energy Technology Data Exchange (ETDEWEB)
Fu, Huansong [Florida State University, Tallahassee; Pophale, Swaroop S [ORNL; Gorentla Venkata, Manjunath [ORNL; Yu, Weikuan [Florida State University, Tallahassee
2016-01-01
Despite the popularity of MPI for high performance computing, the startup of MPI programs faces a scalability challenge as both the execution time and memory consumption increase drastically at scale. We have examined this problem using the collective modules of Cheetah and Tuned in Open MPI as representative implementations. Previous improvements for collectives have focused on algorithmic advances and hardware off-load. In this paper, we examine the startup cost of the collective module within a communicator and explore various techniques to improve its efficiency and scalability. Accordingly, we have developed a new scalable startup scheme with three internal techniques, namely Delayed Initialization, Module Sharing and Prediction-based Topology Setup (DISP). Our DISP scheme greatly benefits the collective initialization of the Cheetah module. At the same time, it helps boost the performance of non-collective initialization in the Tuned module. We evaluate the performance of our implementation on Titan supercomputer at ORNL with up to 4096 processes. The results show that our delayed initialization can speed up the startup of Tuned and Cheetah by an average of 32.0% and 29.2%, respectively, our module sharing can reduce the memory consumption of Tuned and Cheetah by up to 24.1% and 83.5%, respectively, and our prediction-based topology setup can speed up the startup of Cheetah by up to 80%.
Numeric Analysis for Relationship-Aware Scalable Streaming Scheme
Directory of Open Access Journals (Sweden)
Heung Ki Lee
2014-01-01
Full Text Available Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss.
Blind Cooperative Routing for Scalable and Energy-Efficient Internet of Things
Bader, Ahmed
2016-02-26
Multihop networking is promoted in this paper for energy-efficient and highly-scalable Internet of Things (IoT). Recognizing concerns related to the scalability of classical multihop routing and medium access techniques, the use of blind cooperation in conjunction with multihop communications is advocated herewith. Blind cooperation however is actually shown to be inefficient unless power control is applied. Inefficiency in this paper is projected in terms of the transport rate normalized to energy consumption. To that end, an uncoordinated power control mechanism is proposed whereby each device in a blind cooperative cluster randomly adjusts its transmit power level. An upper bound is derived for the mean transmit power that must be observed at each device. Finally, the uncoordinated power control mechanism is demonstrated to consistently outperform the simple point-to-point routing case. © 2015 IEEE.
Scalable real space pseudopotential-density functional codes for materials applications
Chelikowsky, James R.; Lena, Charles; Schofield, Grady; Saad, Yousef; Deslippe, Jack; Yang, Chao
2015-03-01
Real-space pseudopotential density functional theory has proven to be an efficient method for computing the properties of matter in many different states and geometries, including liquids, wires, slabs and clusters with and without spin polarization. Fully self-consistent solutions have been routinely obtained for systems with thousands of atoms. However, there are still systems where quantum mechanical accuracy is desired, but scalability proves to be a hindrance, such as large biological molecules or complex interfaces. We will present an overview of our work on new algorithms, which offer improved scalability by implementing another layer of parallelism, and by optimizing communication and memory management. Support provided by the SciDAC program, Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences. Grant Numbers DE-SC0008877 (Austin) and DE-FG02-12ER4 (Berkeley).
A Scalable Epitope Tagging Approach for High Throughput ChIP-Seq Analysis.
Xiong, Xiong; Zhang, Yanxiao; Yan, Jian; Jain, Surbhi; Chee, Sora; Ren, Bing; Zhao, Huimin
2017-06-16
Eukaryotic transcriptional factors (TFs) typically recognize short genomic sequences alone or together with other proteins to modulate gene expression. Mapping of TF-DNA interactions in the genome is crucial for understanding the gene regulatory programs in cells. While chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is commonly used for this purpose, its application is severely limited by the availability of suitable antibodies for TFs. To overcome this limitation, we developed an efficient and scalable strategy named cmChIP-Seq that combines the clustered regularly interspaced short palindromic repeats (CRISPR) technology with microhomology mediated end joining (MMEJ) to genetically engineer a TF with an epitope tag. We demonstrated the utility of this tool by applying it to four TFs in a human colorectal cancer cell line. The highly scalable procedure makes this strategy ideal for ChIP-Seq analysis of TFs in diverse species and cell types.
Scalable and balanced dynamic hybrid data assimilation
Kauranne, Tuomo; Amour, Idrissa; Gunia, Martin; Kallio, Kari; Lepistö, Ahti; Koponen, Sampsa
2017-04-01
Scalability of complex weather forecasting suites is dependent on the technical tools available for implementing highly parallel computational kernels, but to an equally large extent also on the dependence patterns between various components of the suite, such as observation processing, data assimilation and the forecast model. Scalability is a particular challenge for 4D variational assimilation methods that necessarily couple the forecast model into the assimilation process and subject this combination to an inherently serial quasi-Newton minimization process. Ensemble based assimilation methods are naturally more parallel, but large models force ensemble sizes to be small and that results in poor assimilation accuracy, somewhat akin to shooting with a shotgun in a million-dimensional space. The Variational Ensemble Kalman Filter (VEnKF) is an ensemble method that can attain the accuracy of 4D variational data assimilation with a small ensemble size. It achieves this by processing a Gaussian approximation of the current error covariance distribution, instead of a set of ensemble members, analogously to the Extended Kalman Filter EKF. Ensemble members are re-sampled every time a new set of observations is processed from a new approximation of that Gaussian distribution which makes VEnKF a dynamic assimilation method. After this a smoothing step is applied that turns VEnKF into a dynamic Variational Ensemble Kalman Smoother VEnKS. In this smoothing step, the same process is iterated with frequent re-sampling of the ensemble but now using past iterations as surrogate observations until the end result is a smooth and balanced model trajectory. In principle, VEnKF could suffer from similar scalability issues as 4D-Var. However, this can be avoided by isolating the forecast model completely from the minimization process by implementing the latter as a wrapper code whose only link to the model is calling for many parallel and totally independent model runs, all of them
EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces
Ashari, Rehab Bahaaddin
Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only
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
Wavelet analysis the scalable structure of information
Resnikoff, Howard L
1998-01-01
The authors have been beguiled and entranced by mathematics all of their lives, and both believe it is the highest expression of pure thought and an essential component-one might say the quintessence-of nature. How else can one ex plain the remarkable effectiveness of mathematics in describing and predicting the physical world? The projection of the mathematical method onto the subspace of human endeav 1 ors has long been a source of societal progress and commercial technology. The invention of the electronic digital computer (not the mechanical digital computer of Babbage) has made the role of mathematics in civilization even more central by making mathematics active in the operation of products. The writing of this book was intertwined with the development of a start-up company, Aware, Inc. Aware was founded in 1987 by one of the authors (H.L.R.), and the second author (R.O.W.) put his shoulder to the wheel as a consultant soon after.
de Andrade, Paulo C P; Freire, José A
2004-04-22
We develop nonorthogonal projectors, called Löwdin projectors, to construct an effective donor-acceptor system composed of localized donor (D) and acceptor (A) states of a long-distance electron transfer problem. When these states have a nonvanishing overlap with the bridge states these projectors are non-Hermitian and there are various possible effective two-level systems that can be built. We show how these can be constructed directly from the Schrödinger or Dyson equation projected onto the D-A subspace of the Hilbert space and explore these equations to determine the connection between Hamiltonian and Green function partitioning. We illustrate the use of these effective two-level systems in estimating the electron transfer rate in the context of a simple electron transfer model. (c) 2004 American Institute of Physics
Cho, Soojin; Park, Jong-Woong; Sim, Sung-Han
2015-04-08
Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
Non-Convex Sparse and Low-Rank Based Robust Subspace Segmentation for Data Mining.
Cheng, Wenlong; Zhao, Mingbo; Xiong, Naixue; Chui, Kwok Tai
2017-07-15
Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l₁-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating lp-norm and Schatten p-norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms.
Application of a Subspace-Based Fault Detection Method to Industrial Structures
Mevel, L.; Hermans, L.; van der Auweraer, H.
1999-11-01
Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.
Efficient Structural System Reliability Updating with Subspace-Based Damage Detection Information
DEFF Research Database (Denmark)
Döhler, Michael; Thöns, Sebastian
and prognosis is hardly exploited nor treated in scientific literature up to now. In order to utilize the information provided by DDS for the structural performance, usually high computational efforts for the pre-determination of DDS reliability are required. In this paper, an approach for the DDS performance......Damage detection systems and algorithms (DDS and DDA) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. However, the potential of utilizing DDS information for the structural integrity assessment...... modelling is introduced building upon the non-destructive testing reliability which applies to structural systems and DDS containing a strategy to overcome the high computational efforts for the pre-determination of the DDS reliability. This approach takes basis in the subspace-based damage detection method...
Kerfriden, P; Schmidt, K M; Rabczuk, T; Bordas, S P A
We propose to identify process zones in heterogeneous materials by tailored statistical tools. The process zone is redefined as the part of the structure where the random process cannot be correctly approximated in a low-dimensional deterministic space. Such a low-dimensional space is obtained by a spectral analysis performed on pre-computed solution samples. A greedy algorithm is proposed to identify both process zone and low-dimensional representative subspace for the solution in the complementary region. In addition to the novelty of the tools proposed in this paper for the analysis of localised phenomena, we show that the reduced space generated by the method is a valid basis for the construction of a reduced order model.
A Sub-band Divided Ray Tracing Algorithm Using the DPS Subspace in UWB Indoor Scenarios
DEFF Research Database (Denmark)
Gan, Mingming; Xu, Zhinan; Hofer, Markus
2015-01-01
Sub-band divided ray tracing (SDRT) is one technique that has been extensively used to obtain the channel characteristics for ultra-wideband (UWB) radio wave propagation in realistic indoor environments. However, the computational complexity of SDRT scales directly with the number of sub......-bands. Although we have proposed a low-complexity SDRT algorithm for one terminal position [1], the computational complexity i s still extremely high when involving multiple mobile terminal positions. Moreover, some indoor positioning techniques require for high positioning accuracy data from measurements/simulations...... with a very fine spatial resolution. To cope with this, we propose an algorithm to reduce the computational complexity of SDRT for multiple mobile terminal positions. The algorithm uses a projection of all propagation paths on a subspace spanned by two-dimensional discrete prolate spheroidal (DPS) sequences...
Siddiqui, Bilal A.
2016-07-26
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
McMahon, Nicole D.; Aster, Richard C.; Yeck, William L.; McNamara, Daniel E.; Benz, Harley M.
2017-07-01
The 6 November 2011 Mw 5.7 earthquake near Prague, Oklahoma, is the second largest earthquake ever recorded in the state. A Mw 4.8 foreshock and the Mw 5.7 mainshock triggered a prolific aftershock sequence. Utilizing a subspace detection method, we increase by fivefold the number of precisely located events between 4 November and 5 December 2011. We find that while most aftershock energy is released in the crystalline basement, a significant number of the events occur in the overlying Arbuckle Group, indicating that active Meeker-Prague faulting extends into the sedimentary zone of wastewater disposal. Although the number of aftershocks in the Arbuckle Group is large, comprising 40% of the aftershock catalog, the moment contribution of Arbuckle Group earthquakes is much less than 1% of the total aftershock moment budget. Aftershock locations are sparse in patches that experienced large slip during the mainshock.
Gautier, Guillaume; Delwar Hossain Bhuyan, Md; D öhler, Michael; Mevel, Laurent
2017-04-01
Damage identification in mechanical systems under vibration excitation relates to the monitoring of the changes in the dynamical properties of the corresponding linear system, and thus reflects changes in modal parameters (frequencies, damping, mode shapes) and finally in the finite element model of the structure [1]. Damage localization can be performed using ambient vibration data collected from sensors in the reference and possibly damaged state and information from a finite element model (FEM). Two approaches are considered in this framework, the Stochastic Dynamic Damage Location Vector (SDDLV) approach [2, 3] and the Subspace Fitting (SF) approach [4, 5]. The SDDLV is based on finite element (FE) model of the structure and modal parameters estimated from measurements in both reference and damaged states. From the measurements, a load vector is computed in the kernel of the transfer matrix difference between both states and then applied to the FE model of the structure. This load vector leads to zero (or close to zero) stress over the damaged elements. A joint statistical evaluation has been proposed, where several stress estimates and their uncertainties are computed from multiple mode sets and different Laplace variables for robustness of the approach. SF approach is a finite element model updating. The approach makes use of subspace-based system identification, where an observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating a finite element model observability matrix with the estimated one. SF is applied to damage localization where damages are assumed to be modeled in terms of mean variations of element stiffness matrices. Localization algorithm is improved by taking into account the estimation uncertainties of the underlying finite element model parameters. Both localization algorithms are presented and their performance is illustrated and compared on simulated and experimental vibration
Gieles, M.
2006-01-01
Star clusters are observed in almost every galaxy. In this thesis we address several fundamental problems concerning the formation, evolution and disruption of star clusters. From observations of (young) star clusters in the interacting galaxy M51, we found that clusters are formed in complexes of
Jefferson, J.; Gilbert, J. M.; Maxwell, R. M.; Constantine, P. G.
2015-12-01
Complex hydrologic models are commonly used as computational tools to assess and quantify fluxes at the land surface and for forecasting and prediction purposes. When estimating water and energy fluxes from vegetated surfaces, the equations solved within these models require that multiple input parameters be specified. Some parameters characterize land cover properties while others are constants used to model physical processes like transpiration. As a result, it becomes important to understand the sensitivity of output flux estimates to uncertain input parameters. The active subspace method identifies the most important direction in the high-dimensional space of model inputs. Perturbations of input parameters in this direction influence output quantities more, on average, than perturbations in other directions. The components of the vector defining this direction quantify the sensitivity of the model output to the corresponding inputs. Discovering whether or not an active subspace exists is computationally efficient compared to several other sensitivity analysis methods. Here, we apply this method to evaluate the sensitivity of latent, sensible and ground heat fluxes from the ParFlow-Common Land Model (PF-CLM). Of the 19 input parameters used to specify properties of a grass covered surface, between three and six parameters are identified as important for heat flux estimates. Furthermore, the 19-dimenision input parameter space is reduced to one active variable and the relationship between the inputs and output fluxes for this case is described by a quadratic polynomial. The input parameter weights and the input-output relationship provide a powerful combination of information that can be used to understand land surface dynamics. Given the success of this proof-of-concept example, extension of this method to identify important parameters within the transpiration computation will be explored.
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.
caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data
Directory of Open Access Journals (Sweden)
Xuan Jianhua
2008-09-01
Full Text Available Abstract Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample
caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.
Zhu, Yitan; Li, Huai; Miller, David J; Wang, Zuyi; Xuan, Jianhua; Clarke, Robert; Hoffman, Eric P; Wang, Yue
2008-09-18
The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering
caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data
Zhu, Yitan; Li, Huai; Miller, David J; Wang, Zuyi; Xuan, Jianhua; Clarke, Robert; Hoffman, Eric P; Wang, Yue
2008-01-01
Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype
Combined Scalable Video Coding Method for Wireless Transmission
Directory of Open Access Journals (Sweden)
Achmad Affandi
2011-08-01
Full Text Available Mobile video streaming is one of multimedia services that has developed very rapidly. Recently, bandwidth utilization for wireless transmission is the main problem in the field of multimedia communications. In this research, we offer a combination of scalable methods as the most attractive solution to this problem. Scalable method for wireless communication should adapt to input video sequence. Standard ITU (International Telecommunication Union - Joint Scalable Video Model (JSVM is employed to produce combined scalable video coding (CSVC method that match the required quality of video streaming services for wireless transmission. The investigation in this paper shows that combined scalable technique outperforms the non-scalable one, in using bit rate capacity at certain layer.
Towards a Scalable, Biomimetic, Antibacterial Coating
Dickson, Mary Nora
Corneal afflictions are the second leading cause of blindness worldwide. When a corneal transplant is unavailable or contraindicated, an artificial cornea device is the only chance to save sight. Bacterial or fungal biofilm build up on artificial cornea devices can lead to serious complications including the need for systemic antibiotic treatment and even explantation. As a result, much emphasis has been placed on anti-adhesion chemical coatings and antibiotic leeching coatings. These methods are not long-lasting, and microorganisms can eventually circumvent these measures. Thus, I have developed a surface topographical antimicrobial coating. Various surface structures including rough surfaces, superhydrophobic surfaces, and the natural surfaces of insects' wings and sharks' skin are promising anti-biofilm candidates, however none meet the criteria necessary for implementation on the surface of an artificial cornea device. In this thesis I: 1) developed scalable fabrication protocols for a library of biomimetic nanostructure polymer surfaces 2) assessed the potential these for poly(methyl methacrylate) nanopillars to kill or prevent formation of biofilm by E. coli bacteria and species of Pseudomonas and Staphylococcus bacteria and improved upon a proposed mechanism for the rupture of Gram-negative bacterial cell walls 3) developed a scalable, commercially viable method for producing antibacterial nanopillars on a curved, PMMA artificial cornea device and 4) developed scalable fabrication protocols for implantation of antibacterial nanopatterned surfaces on the surfaces of thermoplastic polyurethane materials, commonly used in catheter tubings. This project constitutes a first step towards fabrication of the first entirely PMMA artificial cornea device. The major finding of this work is that by precisely controlling the topography of a polymer surface at the nano-scale, we can kill adherent bacteria and prevent biofilm formation of certain pathogenic bacteria
Programming Scala Scalability = Functional Programming + Objects
Wampler, Dean
2009-01-01
Learn how to be more productive with Scala, a new multi-paradigm language for the Java Virtual Machine (JVM) that integrates features of both object-oriented and functional programming. With this book, you'll discover why Scala is ideal for highly scalable, component-based applications that support concurrency and distribution. Programming Scala clearly explains the advantages of Scala as a JVM language. You'll learn how to leverage the wealth of Java class libraries to meet the practical needs of enterprise and Internet projects more easily. Packed with code examples, this book provides us
Scalable and Anonymous Group Communication with MTor
Directory of Open Access Journals (Sweden)
Lin Dong
2016-04-01
Full Text Available This paper presents MTor, a low-latency anonymous group communication system. We construct MTor as an extension to Tor, allowing the construction of multi-source multicast trees on top of the existing Tor infrastructure. MTor does not depend on an external service to broker the group communication, and avoids central points of failure and trust. MTor’s substantial bandwidth savings and graceful scalability enable new classes of anonymous applications that are currently too bandwidth-intensive to be viable through traditional unicast Tor communication-e.g., group file transfer, collaborative editing, streaming video, and real-time audio conferencing.
Scalable conditional induction variables (CIV) analysis
DEFF Research Database (Denmark)
Oancea, Cosmin Eugen; Rauchwerger, Lawrence
2015-01-01
representation. Our technique requires no modifications of our dependence tests, which is agnostic to the original shape of the subscripts, and is more powerful than previously reported dependence tests that rely on the pairwise disambiguation of read-write references. We have implemented the CIV analysis in our...... parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in....
Tip-Based Nanofabrication for Scalable Manufacturing
Directory of Open Access Journals (Sweden)
Huan Hu
2017-03-01
Full Text Available Tip-based nanofabrication (TBN is a family of emerging nanofabrication techniques that use a nanometer scale tip to fabricate nanostructures. In this review, we first introduce the history of the TBN and the technology development. We then briefly review various TBN techniques that use different physical or chemical mechanisms to fabricate features and discuss some of the state-of-the-art techniques. Subsequently, we focus on those TBN methods that have demonstrated potential to scale up the manufacturing throughput. Finally, we discuss several research directions that are essential for making TBN a scalable nano-manufacturing technology.
DEFF Research Database (Denmark)
Ackerman, Margareta; Ben-David, Shai; Branzei, Simina
2012-01-01
We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...
GRACOS: Scalable and Load Balanced P3M Cosmological N-body Code
Shirokov, Alexander; Bertschinger, Edmund
2010-10-01
The GRACOS (GRAvitational COSmology) code, a parallel implementation of the particle-particle/particle-mesh (P3M) algorithm for distributed memory clusters, uses a hybrid method for both computation and domain decomposition. Long-range forces are computed using a Fourier transform gravity solver on a regular mesh; the mesh is distributed across parallel processes using a static one-dimensional slab domain decomposition. Short-range forces are computed by direct summation of close pairs; particles are distributed using a dynamic domain decomposition based on a space-filling Hilbert curve. A nearly-optimal method was devised to dynamically repartition the particle distribution so as to maintain load balance even for extremely inhomogeneous mass distributions. Tests using 800(3) simulations on a 40-processor beowulf cluster showed good load balance and scalability up to 80 processes. There are limits on scalability imposed by communication and extreme clustering which may be removed by extending the algorithm to include adaptive mesh refinement.
Scalable Parallel Simulation Of Small-scale Structure In Cold Dark Matter
Shirokov, A V
2005-01-01
We present a parallel implementation of the particle- particle/particle-mesh (P3M) algorithm for distributed memory clusters. The 11p3m-hc code uses a hybrid method for both computation and domain decomposition. Long-range forces are computed using a Fourier transform gravity solver on a regular mesh; the mesh is distributed across parallel processes using a static one-dimensional slab domain decomposition. Short- range forces are computed by direct summation of close pairs; particles are distributed using a dynamic domain decomposition based on a space-filling Hilbert curve. A nearly-optimal method was devised to dynamically repartition the particle distribution so as to maintain load balance even for extremely inhomogeneous mass distributions. Tests using 8003 simulations on a 40-processor Beowulf cluster showed good load balance and scalability up to 80 processes. We discuss the limits on scalability imposed by communication and extreme clustering and suggest how they may be removed by extending our algor...
Big data integration: scalability and sustainability
Zhang, Zhang
2016-01-26
Integration of various types of omics data is critically indispensable for addressing most important and complex biological questions. In the era of big data, however, data integration becomes increasingly tedious, time-consuming and expensive, posing a significant obstacle to fully exploit the wealth of big biological data. Here we propose a scalable and sustainable architecture that integrates big omics data through community-contributed modules. Community modules are contributed and maintained by different committed groups and each module corresponds to a specific data type, deals with data collection, processing and visualization, and delivers data on-demand via web services. Based on this community-based architecture, we build Information Commons for Rice (IC4R; http://ic4r.org), a rice knowledgebase that integrates a variety of rice omics data from multiple community modules, including genome-wide expression profiles derived entirely from RNA-Seq data, resequencing-based genomic variations obtained from re-sequencing data of thousands of rice varieties, plant homologous genes covering multiple diverse plant species, post-translational modifications, rice-related literatures, and community annotations. Taken together, such architecture achieves integration of different types of data from multiple community-contributed modules and accordingly features scalable, sustainable and collaborative integration of big data as well as low costs for database update and maintenance, thus helpful for building IC4R into a comprehensive knowledgebase covering all aspects of rice data and beneficial for both basic and translational researches.
Using MPI to Implement Scalable Libraries
Lusk, Ewing
MPI is an instantiation of a general-purpose programming model, and high-performance implementations of the MPI standard have provided scalability for a wide range of applications. Ease of use was not an explicit goal of the MPI design process, which emphasized completeness, portability, and performance. Thus it is not surprising that MPI is occasionally criticized for being inconvenient to use and thus a drag on software developer productivity. One approach to the productivity issue is to use MPI to implement simpler programming models. Such models may limit the range of parallel algorithms that can be expressed, yet provide sufficient generality to benefit a significant number of applications, even from different domains.We illustrate this concept with the ADLB (Asynchronous, Dynamic Load-Balancing) library, which can be used to express manager/worker algorithms in such a way that their execution is scalable, even on the largestmachines. ADLB makes sophisticated use ofMPI functionality while providing an extremely simple API for the application programmer.We will describe it in the context of solving Sudoku puzzles and a nuclear physics Monte Carlo application currently running on tens of thousands of processors.
Using the scalable nonlinear equations solvers package
Energy Technology Data Exchange (ETDEWEB)
Gropp, W.D.; McInnes, L.C.; Smith, B.F.
1995-02-01
SNES (Scalable Nonlinear Equations Solvers) is a software package for the numerical solution of large-scale systems of nonlinear equations on both uniprocessors and parallel architectures. SNES also contains a component for the solution of unconstrained minimization problems, called SUMS (Scalable Unconstrained Minimization Solvers). Newton-like methods, which are known for their efficiency and robustness, constitute the core of the package. As part of the multilevel PETSc library, SNES incorporates many features and options from other parts of PETSc. In keeping with the spirit of the PETSc library, the nonlinear solution routines are data-structure-neutral, making them flexible and easily extensible. This users guide contains a detailed description of uniprocessor usage of SNES, with some added comments regarding multiprocessor usage. At this time the parallel version is undergoing refinement and extension, as we work toward a common interface for the uniprocessor and parallel cases. Thus, forthcoming versions of the software will contain additional features, and changes to parallel interface may result at any time. The new parallel version will employ the MPI (Message Passing Interface) standard for interprocessor communication. Since most of these details will be hidden, users will need to perform only minimal message-passing programming.
Towards Scalable Graph Computation on Mobile Devices
Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng
2015-01-01
Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach. PMID:25859564
Scalability Optimization of Seamless Positioning Service
Directory of Open Access Journals (Sweden)
Juraj Machaj
2016-01-01
Full Text Available Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.
An Open Infrastructure for Scalable, Reconfigurable Analysis
Energy Technology Data Exchange (ETDEWEB)
de Supinski, B R; Fowler, R; Gamblin, T; Mueller, F; Ratn, P; Schulz, M
2008-05-15
Petascale systems will have hundreds of thousands of processor cores so their applications must be massively parallel. Effective use of petascale systems will require efficient interprocess communication through memory hierarchies and complex network topologies. Tools to collect and analyze detailed data about this communication would facilitate its optimization. However, several factors complicate tool design. First, large-scale runs on petascale systems will be a precious commodity, so scalable tools must have almost no overhead. Second, the volume of performance data from petascale runs could easily overwhelm hand analysis and, thus, tools must collect only data that is relevant to diagnosing performance problems. Analysis must be done in-situ, when available processing power is proportional to the data. We describe a tool framework that overcomes these complications. Our approach allows application developers to combine existing techniques for measurement, analysis, and data aggregation to develop application-specific tools quickly. Dynamic configuration enables application developers to select exactly the measurements needed and generic components support scalable aggregation and analysis of this data with little additional effort.
Highly scalable Ab initio genomic motif identification
Marchand, Benoit
2011-01-01
We present results of scaling an ab initio motif family identification system, Dragon Motif Finder (DMF), to 65,536 processor cores of IBM Blue Gene/P. DMF seeks groups of mutually similar polynucleotide patterns within a set of genomic sequences and builds various motif families from them. Such information is of relevance to many problems in life sciences. Prior attempts to scale such ab initio motif-finding algorithms achieved limited success. We solve the scalability issues using a combination of mixed-mode MPI-OpenMP parallel programming, master-slave work assignment, multi-level workload distribution, multi-level MPI collectives, and serial optimizations. While the scalability of our algorithm was excellent (94% parallel efficiency on 65,536 cores relative to 256 cores on a modest-size problem), the final speedup with respect to the original serial code exceeded 250,000 when serial optimizations are included. This enabled us to carry out many large-scale ab initio motiffinding simulations in a few hours while the original serial code would have needed decades of execution time. Copyright 2011 ACM.
Katz, R
1992-11-01
Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.
Naeem, Raeece
2012-11-28
Summary: READSCAN is a highly scalable parallel program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets. READSCAN accurately classified human and viral sequences on a 20.1 million reads simulated dataset in <27 min using a small Beowulf compute cluster with 16 nodes (Supplementary Material). Availability: http://cbrc.kaust.edu.sa/readscan Contact: or raeece.naeem@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. 2012 The Author(s).
MR-Tree - A Scalable MapReduce Algorithm for Building Decision Trees
Directory of Open Access Journals (Sweden)
Vasile PURDILĂ
2014-03-01
Full Text Available Learning decision trees against very large amounts of data is not practical on single node computers due to the huge amount of calculations required by this process. Apache Hadoop is a large scale distributed computing platform that runs on commodity hardware clusters and can be used successfully for data mining task against very large datasets. This work presents a parallel decision tree learning algorithm expressed in MapReduce programming model that runs on Apache Hadoop platform and has a very good scalability with dataset size.
Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy
Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli
2014-03-01
One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3DMIP platform when a larger number of cores is available.
A scalable neuroinformatics data flow for electrophysiological signals using MapReduce
Jayapandian, Catherine; Wei, Annan; Ramesh, Priya; Zonjy, Bilal; Lhatoo, Samden D.; Loparo, Kenneth; Zhang, Guo-Qiang; Sahoo, Satya S.
2015-01-01
Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications. PMID:25852536
A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.
Jayapandian, Catherine; Wei, Annan; Ramesh, Priya; Zonjy, Bilal; Lhatoo, Samden D; Loparo, Kenneth; Zhang, Guo-Qiang; Sahoo, Satya S
2015-01-01
Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.
Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy.
Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli
2014-03-19
One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3D-MIP platform when a larger number of cores is available.
A Scalable Neuroinformatics Data Flow for Electrophysiological Signals using MapReduce
Directory of Open Access Journals (Sweden)
Catherine eJayapandian
2015-03-01
Full Text Available Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF, the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.
... re at risk of cluster headache. A family history. Having a parent or sibling who has had cluster headache might increase your risk. By Mayo Clinic Staff . Mayo Clinic Footer Legal Conditions and Terms ...
Network selection, Information filtering and Scalable computation
Ye, Changqing
-complete factorizations, possibly with a high percentage of missing values. This promotes additional sparsity beyond rank reduction. Computationally, we design methods based on a ``decomposition and combination'' strategy, to break large-scale optimization into many small subproblems to solve in a recursive and parallel manner. On this basis, we implement the proposed methods through multi-platform shared-memory parallel programming, and through Mahout, a library for scalable machine learning and data mining, for mapReduce computation. For example, our methods are scalable to a dataset consisting of three billions of observations on a single machine with sufficient memory, having good timings. Both theoretical and numerical investigations show that the proposed methods exhibit significant improvement in accuracy over state-of-the-art scalable methods.
The CMS online cluster: Setup, operation and maintenance of an evolving cluster
Energy Technology Data Exchange (ETDEWEB)
Coarasa, J.A.; et al.
2012-01-01
The CMS online cluster consists of more than 2700 computers running about 15000 application instances. These applications implement the necessary services to run the data acquisition of the CMS experiment. In this paper the IT solutions employed on the cluster are reviewed. Details are given on the adopted solutions which include the following topics: implementation of reduction and load balanced network and core IT services; deployment and configuration management infrastructure and its customization; a new monitoring infrastructure. Special emphasis will be put on the scalable approach allowing to increase the size of the cluster with no administration overhead. Finally, the lessons learnt from the two years of running will be presented.
The CMS Online Cluster: Setup, Operation and Maintenance of an Evolving Cluster
Coarasa, Jose Antonio; Behrens, Ulf; Bouffet, Olivier; Branson, James G; Bukowiec, Sebastian; Chaze, Olivier; Ciganek, Marek; Cittolin, Sergio; Deldicque, Christian; Dobson, Marc; Dupon, Aymeric; Erhan, Samim; Gigi, Dominique; Glege, Frank; Gomez-Reino, Robert; Hartl, Christian; Holzner, André; Masetti, Lorenzo; Meijers, Frans; Meschi, Emilio; Mommsen, Remigius K; Nunez-Barranco-Fernandez, Carlos; O'Dell, Vivian; Orsini, Luciano; Paus, Christoph; Petrucci, Andrea; Pieri, Marco; Polese, Giovanni; Racz, Attila; Raginel, Olivier; Sakulin, Hannes; Sani, Matteo; Schwick, Christoph; Simon, Michal; Spataru, Andrei Cristian; Stoeckli, Fabian; Sumorok, Konstanty
2012-01-01
The CMS online cluster consists of more than 2700 computers running about 15000 application instances. These applications implement the necessary services to run the data acquisition of the CMS experiment. In this paper the IT solutions employed on the cluster are reviewed. Details are given on the adopted solutions which include the following topics: implementation of a redundant and load balanced network and core IT services; deployment and configuration management infrastructure and its customization; a new monitoring infrastructure. Special emphasis will be put on the scalable approach allowing to increase the size of the cluster with no administration overhead. Finally, the lessons learnt from the two years of running will be presented.
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.
Bergseng, Marta Næss
1985-01-01
Cluster headache is the most severe primary headache with recurrent pain attacks described as worse than giving birth. The aim of this paper was to make an overview of current knowledge on cluster headache with a focus on pathophysiology and treatment. This paper presents hypotheses of cluster headache pathophysiology, current treatment options and possible future therapy approaches. For years, the hypothalamus was regarded as the key structure in cluster headache, but is now thought to be pa...
Energy Technology Data Exchange (ETDEWEB)
Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.
2004-05-26
We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.
Scalable Transactions for Web Applications in the Cloud
Zhou, W.; Pierre, G.E.O.; Chi, C.-H.
2009-01-01
Cloud Computing platforms provide scalability and high availability properties for web applications but they sacrifice data consistency at the same time. However, many applications cannot afford any data inconsistency. We present a scalable transaction manager for NoSQL cloud database services to
New Complexity Scalable MPEG Encoding Techniques for Mobile Applications
Directory of Open Access Journals (Sweden)
Stephan Mietens
2004-03-01
Full Text Available Complexity scalability offers the advantage of one-time design of video applications for a large product family, including mobile devices, without the need of redesigning the applications on the algorithmic level to meet the requirements of the different products. In this paper, we present complexity scalable MPEG encoding having core modules with modifications for scalability. The interdependencies of the scalable modules and the system performance are evaluated. Experimental results show scalability giving a smooth change in complexity and corresponding video quality. Scalability is basically achieved by varying the number of computed DCT coefficients and the number of evaluated motion vectors but other modules are designed such they scale with the previous parameters. In the experiments using the Ã‚Â“StefanÃ‚Â” sequence, the elapsed execution time of the scalable encoder, reflecting the computational complexity, can be gradually reduced to roughly 50% of its original execution time. The video quality scales between 20 dB and 48 dB PSNR with unity quantizer setting, and between 21.5 dB and 38.5 dB PSNR for different sequences targeting 1500 kbps. The implemented encoder and the scalability techniques can be successfully applied in mobile systems based on MPEG video compression.
Scalable DeNoise-and-Forward in Bidirectional Relay Networks
DEFF Research Database (Denmark)
Sørensen, Jesper Hemming; Krigslund, Rasmus; Popovski, Petar
2010-01-01
In this paper a scalable relaying scheme is proposed based on an existing concept called DeNoise-and-Forward, DNF. We call it Scalable DNF, S-DNF, and it targets the scenario with multiple communication flows through a single common relay. The idea of the scheme is to combine packets at the relay...
Building scalable apps with Redis and Node.js
Johanan, Joshua
2014-01-01
If the phrase scalability sounds alien to you, then this is an ideal book for you. You will not need much Node.js experience as each framework is demonstrated in a way that requires no previous knowledge of the framework. You will be building scalable Node.js applications in no time! Knowledge of JavaScript is required.
Pak, Chan-gi; Lung, Shun-fat
2009-01-01
Modern airplane design is a multidisciplinary task which combines several disciplines such as structures, aerodynamics, flight controls, and sometimes heat transfer. Historically, analytical and experimental investigations concerning the interaction of the elastic airframe with aerodynamic and in retia loads have been conducted during the design phase to determine the existence of aeroelastic instabilities, so called flutter .With the advent and increased usage of flight control systems, there is also a likelihood of instabilities caused by the interaction of the flight control system and the aeroelastic response of the airplane, known as aeroservoelastic instabilities. An in -house code MPASES (Ref. 1), modified from PASES (Ref. 2), is a general purpose digital computer program for the analysis of the closed-loop stability problem. This program used subroutines given in the International Mathematical and Statistical Library (IMSL) (Ref. 3) to compute all of the real and/or complex conjugate pairs of eigenvalues of the Hessenberg matrix. For high fidelity configuration, these aeroelastic system matrices are large and compute all eigenvalues will be time consuming. A subspace iteration method (Ref. 4) for complex eigenvalues problems with nonsymmetric matrices has been formulated and incorporated into the modified program for aeroservoelastic stability (MPASES code). Subspace iteration method only solve for the lowest p eigenvalues and corresponding eigenvectors for aeroelastic and aeroservoelastic analysis. In general, the selection of p is ranging from 10 for wing flutter analysis to 50 for an entire aircraft flutter analysis. The application of this newly incorporated code is an experiment known as the Aerostructures Test Wing (ATW) which was designed by the National Aeronautic and Space Administration (NASA) Dryden Flight Research Center, Edwards, California to research aeroelastic instabilities. Specifically, this experiment was used to study an instability
A note on the blind deconvolution of multiple sparse signals from unknown subspaces
Cosse, Augustin
2017-08-01
This note studies the recovery of multiple sparse signals, xn ∈ ℝL, n = 1, . . . , N, from the knowledge of their convolution with an unknown point spread function h ∈ ℝL. When the point spread function is known to be nonzero, |h[k]| > 0, this blind deconvolution problem can be relaxed into a linear, ill-posed inverse problem in the vector concatenating the unknown inputs xn together with the inverse of the filter, d ∈ ℝL where d[k] := 1/h[k]. When prior information is given on the input subspaces, the resulting overdetermined linear system can be solved efficiently via least squares (see Ling et al. 20161). When no information is given on those subspaces, and the inputs are only known to be sparse, it still remains possible to recover these inputs along with the filter by considering an additional l1 penalty. This note certifies exact recovery of both the unknown PSF and unknown sparse inputs, from the knowledge of their convolutions, as soon as the number of inputs N and the dimension of each input, L , satisfy L ≳ N and N ≳ T2max, up to log factors. Here Tmax = maxn{Tn} and Tn, n = 1, . . . , N denote the supports of the inputs xn. Our proof system combines the recent results on blind deconvolution via least squares to certify invertibility of the linear map encoding the convolutions, with the construction of a dual certificate following the structure first suggested in Candés et al. 2007.2 Unlike in these papers, however, it is not possible to rely on the norm ||(A*TAT)-1|| to certify recovery. We instead use a combination of the Schur Complement and Neumann series to compute an expression for the inverse (A*TAT)-1. Given this expression, it is possible to show that the poorly scaled blocks in (A*TAT)-1 are multiplied by the better scaled ones or vanish in the construction of the certificate. Recovery is certified with high probablility on the choice of the supports and distribution of the signs of each input xn on the support. The paper
Parashar, Sumeet
Most engineering design problems are complex and multidisciplinary in nature, and quite often require more than one objective (cost) function to be extremized simultaneously. For multi-objective optimization problems, there is not a single optimum solution, but a set of optimum solutions called the Pareto set. The primary goal of this research is to develop a heuristic solution strategy to enable multi-objective optimization of highly coupled multidisciplinary design applications, wherein each discipline is able to retain some degree of autonomous control during the process. To achieve this goal, this research extends the capability of the Multi-Objective Pareto Concurrent Subspace Optimization (MOPCSSO) method to generate large numbers of non-dominated solutions in each cycle, with subsequent update and refinement, thereby greatly increasing efficiency. While the conventional MOPCSSO approach is easily able to generate Pareto solutions, it will only generate one Pareto solution at a time. In order to generate the complete Pareto front, MOPCSSO requires multiple runs (translating into many system convergence cycles) using different initial staring points. In this research, a Genetic Algorithm-based heuristic solution strategy is developed for multi-objective problems in coupled multidisciplinary design. The Multi-Objective Genetic Algorithm Concurrent Subspace Optimization (MOGACSSO) method allows for the generation of relatively evenly distributed Pareto solutions in a faster and more efficient manner than repeated implementation of MOPCSSO. While achieving an optimum design, it is often also desirable that the optimum design be robust to uncontrolled parameter variations. In this research, the capability of the MOGACSSO method is also extended to generate Pareto points that are robust in terms of performance and feasibility, for given uncontrolled parameter variations. The Roust-MOGACSSO method developed in this research can generate a large number of designs
Energy Technology Data Exchange (ETDEWEB)
Yang, Won Sik [Chosun University, Department of Nuclear Engineering, Kwangju (Korea, Republic of); Joo, Han Gyu [Korea Atomic Energy Research Institute, Yusong, Taejon (Korea, Republic of)
1999-05-01
A computational method was developed for predicting the steady-state temperature field in an LMR core based on the simplified energy equation mixing model and the subchannel analysis method. The {theta}-method was employed for discretizing the energy equation in the axial direction, and the interassembly coupling was achieved by interassembly gap flow. For an implicit scheme, a Krylov subspace method based on the BiCGSTAB algorithm and the MILU preconditioning was employed to solve the resulting linear system at each axial level. Numerical test results suggest that at least an order of magnitude reduction in the computing time can be achieved with the implicit solution scheme employing the Krylov subspace method when compared to the explicit scheme.
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...
BASSET: Scalable Gateway Finder in Large Graphs
Energy Technology Data Exchange (ETDEWEB)
Tong, H; Papadimitriou, S; Faloutsos, C; Yu, P S; Eliassi-Rad, T
2010-11-03
Given a social network, who is the best person to introduce you to, say, Chris Ferguson, the poker champion? Or, given a network of people and skills, who is the best person to help you learn about, say, wavelets? The goal is to find a small group of 'gateways': persons who are close enough to us, as well as close enough to the target (person, or skill) or, in other words, are crucial in connecting us to the target. The main contributions are the following: (a) we show how to formulate this problem precisely; (b) we show that it is sub-modular and thus it can be solved near-optimally; (c) we give fast, scalable algorithms to find such gateways. Experiments on real data sets validate the effectiveness and efficiency of the proposed methods, achieving up to 6,000,000x speedup.
The Concept of Business Model Scalability
DEFF Research Database (Denmark)
Nielsen, Christian; Lund, Morten
2015-01-01
are leveraged in this value creation, delivery and realization exercise. Central to the mainstream understanding of business models is the value proposition towards the customer and the hypothesis generated is that if the firm delivers to the customer what he/she requires, then there is a good foundation......The power of business models lies in their ability to visualize and clarify how firms’ may configure their value creation processes. Among the key aspects of business model thinking are a focus on what the customer values, how this value is best delivered to the customer and how strategic partners...... for a long-term profitable business. However, the message conveyed in this article is that while providing a good value proposition may help the firm ‘get by’, the really successful businesses of today are those able to reach the sweet-spot of business model scalability. This article introduces and discusses...
Towards scalable Byzantine fault-tolerant replication
Zbierski, Maciej
2017-08-01
Byzantine fault-tolerant (BFT) replication is a powerful technique, enabling distributed systems to remain available and correct even in the presence of arbitrary faults. Unfortunately, existing BFT replication protocols are mostly load-unscalable, i.e. they fail to respond with adequate performance increase whenever new computational resources are introduced into the system. This article proposes a universal architecture facilitating the creation of load-scalable distributed services based on BFT replication. The suggested approach exploits parallel request processing to fully utilize the available resources, and uses a load balancer module to dynamically adapt to the properties of the observed client workload. The article additionally provides a discussion on selected deployment scenarios, and explains how the proposed architecture could be used to increase the dependability of contemporary large-scale distributed systems.
A graph algebra for scalable visual analytics.
Shaverdian, Anna A; Zhou, Hao; Michailidis, George; Jagadish, Hosagrahar V
2012-01-01
Visual analytics (VA), which combines analytical techniques with advanced visualization features, is fast becoming a standard tool for extracting information from graph data. Researchers have developed many tools for this purpose, suggesting a need for formal methods to guide these tools' creation. Increased data demands on computing requires redesigning VA tools to consider performance and reliability in the context of analysis of exascale datasets. Furthermore, visual analysts need a way to document their analyses for reuse and results justification. A VA graph framework encapsulated in a graph algebra helps address these needs. Its atomic operators include selection and aggregation. The framework employs a visual operator and supports dynamic attributes of data to enable scalable visual exploration of data.
Declarative and Scalable Selection for Map Visualizations
DEFF Research Database (Denmark)
Kefaloukos, Pimin Konstantin Balic
foreground layers is merited. (2) The typical map making professional has changed from a GIS specialist to a busy person with map making as a secondary skill. Today, thematic maps are produced by journalists, aid workers, amateur data enth siasts, and scientists alike. Therefore it is crucial...... that this diverse group of map makers is provided with easy-to-use and expressible thematic map design tools. Such tools should support customized selection of data for maps in scenarios where developer time is a scarce resource. (3) The Web provides access to massive data repositories for thematic maps...... based on an access log of recent requests. The results show that Glossy SQL og CVL can be used to compute cartographic selection by processing one or more complex queries in a relational database. The scalability of the approach has been verified up to half a million objects in the database. Furthermore...
Scalable and Media Aware Adaptive Video Streaming over Wireless Networks
Tizon, Nicolas; Pesquet-Popescu, Béatrice
2008-12-01
This paper proposes an advanced video streaming system based on scalable video coding in order to optimize resource utilization in wireless networks with retransmission mechanisms at radio protocol level. The key component of this system is a packet scheduling algorithm which operates on the different substreams of a main scalable video stream and which is implemented in a so-called media aware network element. The concerned type of transport channel is a dedicated channel subject to parameters (bitrate, loss rate) variations on the long run. Moreover, we propose a combined scalability approach in which common temporal and SNR scalability features can be used jointly with a partitioning of the image into regions of interest. Simulation results show that our approach provides substantial quality gain compared to classical packet transmission methods and they demonstrate how ROI coding combined with SNR scalability allows to improve again the visual quality.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Peng; Zhou, Ning; Abdollahi, Ali
2013-09-10
A Generalized Subspace-Least Mean Square (GSLMS) method is presented for accurate and robust estimation of oscillation modes from exponentially damped power system signals. The method is based on orthogonality of signal and noise eigenvectors of the signal autocorrelation matrix. Performance of the proposed method is evaluated using Monte Carlo simulation and compared with Prony method. Test results show that the GSLMS is highly resilient to noise and significantly dominates Prony method in tracking power system modes under noisy environments.
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
Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Zi, Yanyang; Yan, Ruqiang
2017-09-01
The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.
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.
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.
Scalability of Knowledge Transfer in Complex Systems of Emergent "living" Communities
Directory of Open Access Journals (Sweden)
Susu Nousala
2013-08-01
Full Text Available Communities are emergent, holistic living systems. Understanding the impact of social complex systems through spatial interactions via the lens of scalability requires the development of new methodological behavioural approaches. The evolution of social complex systems of cities and their regions can be investigated through the evolution of spatial structures. The clustering of entities within cities, regions and beyond presents behavioural elements for which methodological approaches need to be considered. The emergent aspect of complex entities by their very nature requires an understanding that can embrace unpredictability through emergence. Qualitative methodological approaches can be holistic with the ability to embrace bottom up and top down methods for analysis. Social complex systems develop structures by connecting "like minded" behaviour through scalability. How "mobile" these interactions are, is a concept that can be understood via "inter-organizational" and "interstructural" comparative approaches. How do we indeed convey this adequately or appropriately? Just as a geographical area may contain characteristics that can help to support the formation of an emergent industry cluster, similar behaviours occur through emergent characteristics of complex systems that underpin the sustainability of an organization. The idea that complex systems have tacit structures, capable of displaying emergent behaviour, is not a common concept. These tacit structures can in turn, impact the structural sustainability of physical entities. More often than not, there is a focus on how these concepts of complex systems work, but the "why" questions depends upon scalability. Until recently, social complex adaptive systems were largely over looked due to the tacit nature of these network structures.
Attracting subspaces in a hyper-spherical representation of autonomous dynamical systems
Ceccato, Alessandro; Nicolini, Paolo; Frezzato, Diego
2017-09-01
In this work, we focus on the possibility to recast the ordinary differential equations (ODEs) governing the evolution of deterministic autonomous dynamical systems (conservative or damped and generally non-linear) into a parameter-free universal format. We term such a representation "hyper-spherical" since the new variables are a "radial" norm having physical units of inverse-of-time and a normalized "state vector" with (possibly complex-valued) dimensionless components. Here we prove that while the system evolves in its physical space, the mirrored evolution in the hyper-spherical space is such that the state vector moves monotonically towards fixed "attracting subspaces" (one at a time). Correspondingly, the physical space can be split into "attractiveness regions." We present the general concepts and provide an example of how such a transformation of ODEs can be achieved for a class of mechanical-like systems where the physical variables are a set of configurational degrees of freedom and the associated velocities in a phase-space representation. A one-dimensional case model (motion in a bi-stable potential) is adopted to illustrate the procedure.
Energy Technology Data Exchange (ETDEWEB)
Platoni, K.; Lefkopoulos, D.; Grandjean, P.; Schlienger, M. [Tenon Hospital, Paris (France). Radiation Oncology Dept.
1999-11-01
A Linac sterotactic irradiation space is characterized by different angular separations of beams because of the geometry of the stereotactic irradiation. The regions of the stereotactic space characterized by low angular separations are one of the causes of ill-conditioning of the stereotactic irradiation inverse problem. The singular value decomposition (SVD) is a powerful mathematical analysis that permits the measurement of the ill-conditioning of the stereotactic irradiation problem. This study examines the ill-conditioning of the stereotactic irradiation space, provoked by the different angular separations of beams, using the SVD analysis. We subdivided the maximum irradiation space (MIS: (AA){sub AP} x (AA){sub RL}=180 x 180 ) into irradiation subspaces (ISSs), each characterized by its own angular separation. We studied the influence of ISSs on the SVD analysis and the evolution of the reconstruction quality of well defined three-dimensional dose matrices in each configuration. The more the ISS is characterized by low angular separation the more the condition number and the reconstruction inaccuracy are increased. Based on the above results we created two reduced irradiation spaces (RIS: (AA){sub AP} x (AA){sub RL}=180 x 140 and (AA){sub AP} x (AA){sub RL}=180 x 120 ) and compared the reconstruction quality of the RISs with respect to the MIS. The more an irradiation space is free of low angular separations the more the irradiation space contains useful singular components. (orig.)
Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter
Directory of Open Access Journals (Sweden)
Siva Karteek Bolisetti
2015-01-01
Full Text Available The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.
Ma, Junshui; Bayram, Sevinç; Tao, Peining; Svetnik, Vladimir
2011-03-15
After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion. Copyright © 2011 Elsevier B.V. All rights reserved.
Transferring multiqubit entanglement onto memory qubits in a decoherence-free subspace
He, Xiao-Ling; Yang, Chui-Ping
2017-03-01
Different from the previous works on generating entangled states, this work is focused on how to transfer the prepared entangled states onto memory qubits for protecting them against decoherence. We here consider a physical system consisting of n operation qubits and 2 n memory qubits placed in a cavity or coupled to a resonator. A method is presented for transferring n-qubit Greenberger-Horne-Zeilinger (GHZ) entangled states from the operation qubits (i.e., information processing cells) onto the memory qubits (i.e., information memory elements with long decoherence time). The transferred GHZ states are encoded in a decoherence-free subspace against collective dephasing and thus can be immune from decoherence induced by a dephasing environment. In addition, the state transfer procedure has nothing to do with the number of qubits, the operation time does not increase with the number of qubits, and no measurement is needed for the state transfer. This proposal can be applied to a wide range of hybrid qubits such as natural atoms and artificial atoms (e.g., various solid-state qubits).
Directory of Open Access Journals (Sweden)
Remus Oşan
2007-05-01
Full Text Available Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA, Principal Components Analysis (PCA and Artificial Neural Networks (ANN, and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD. Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.
Directory of Open Access Journals (Sweden)
Hansen Henrik
2004-01-01
Full Text Available We proposed recently a new technique for multiuser detection in CDMA networks, denoted by interference subspace rejection (ISR, and evaluated its performance on the uplink. This paper extends its application to the downlink (DL. On the DL, the information about the interference is sparse, for example, spreading factor (SF and modulation of interferers may not be known, which makes the task much more challenging. We present three new ISR variants which require no prior knowledge of interfering users. The new solutions are applicable to MIMO systems and can accommodate any modulation, coding, SF, and connection type. We propose a new code allocation scheme denoted by DACCA which significantly reduces the complexity of our solution at the receiving mobile. We present estimates of user capacities and data rates attainable under practically reasonable conditions regarding interferences identified and suppressed in a multicellular interference-limited system. We show that the system capacity increases linearly with the number of antennas despite the existence of interference. Our new DL multiuser receiver consistently provides an Erlang capacity gain of at least over the single-user detector.
Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei
2014-01-01
This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.
Removal of EOG artifacts from EEG recordings using stationary subspace analysis.
Zeng, Hong; Song, Aiguo
2014-01-01
An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Directory of Open Access Journals (Sweden)
Hongyun Qin
2013-11-01
Full Text Available Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Zeng, Hong; Song, Aiguo; Yan, Ruqiang; Qin, Hongyun
2013-11-01
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis
Directory of Open Access Journals (Sweden)
Hong Zeng
2014-01-01
Full Text Available An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter.
Bolisetti, Siva Karteek; Patwary, Mohammad; Ahmed, Khawza; Soliman, Abdel-Hamid; Abdel-Maguid, Mohamed
2015-01-01
The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT) detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.
De Filippis, G.; Noël, J. P.; Kerschen, G.; Soria, L.; Stephan, C.
2017-09-01
The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, first-generation framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the present paper, it is proposed as a first contribution to operate directly on the identified state-space matrices to carry out spurious pole removal. A modal-space decomposition of the state and output matrices is examined to discriminate genuine from numerical poles, prior to estimating the extended input and feedthrough matrices. The final state-space model thus contains physical information only and naturally leads to nonlinear coefficients free of spurious variations. Besides spurious variations due to nonphysical poles, vibration modes lying outside the frequency band of interest may also produce drifts of the nonlinear coefficients. The second contribution of the paper is to include residual terms, accounting for the existence of these modes. The proposed improved FNSI methodology is validated numerically and experimentally using a full-scale structure, the Morane-Saulnier Paris aircraft.
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
Gillis, T.; Winckelmans, G.; Chatelain, P.
2017-10-01
We formulate the penalization problem inside a vortex particle-mesh method as a linear system. This system has to be solved at every wall boundary condition enforcement within a time step. Furthermore, because the underlying problem is a Poisson problem, the solution of this linear system is computationally expensive. For its solution, we here use a recycling iterative solver, rBiCGStab, in order to reduce the number of iterations and therefore decrease the computational cost of the penalization step. For the recycled subspace, we use the orthonormalized previous solutions as only the right hand side changes from the solution at one time to the next. This method is validated against benchmark results: the impulsively started cylinder, with validation at low Reynolds number (Re = 550) and computational savings assessments at moderate Reynolds number (Re = 9500); then on a flat plate benchmark (Re = 1000). By improving the convergence behavior, the approach greatly reduces the computational cost of iterative penalization, at a moderate cost in memory overhead.
Directory of Open Access Journals (Sweden)
Wilfried B. Krätzig
2014-01-01
Full Text Available This paper applies recent research on structural damage description to earthquake-resistant design concepts. Based on the primary design aim of life safety, this work adopts the necessity of additional protection aims for property, installation, and equipment. This requires the definition of damage indicators, which are able to quantify the arising structural damage. As in present design, it applies nonlinear quasistatic (pushover concepts due to code provisions as simplified dynamic design tools. Substituting so nonlinear time-history analyses, seismic low-cycle fatigue of RC structures is approximated in similar manner. The treatment will be embedded into a finite element environment, and the tangential stiffness matrix KT in tangential subspaces then is identified as the most general entry for structural damage information. Its spectra of eigenvalues λi or natural frequencies ωi of the structure serve to derive damage indicators Di, applicable to quasistatic evaluation of seismic damage. Because det KT=0 denotes structural failure, such damage indicators range from virgin situation Di=0 to failure Di=1 and thus correspond with Fema proposals on performance-based seismic design. Finally, the developed concept is checked by reanalyses of two experimentally investigated RC frames.
Discriminative local subspaces in gene expression data for effective gene function prediction.
Puelma, Tomas; Gutiérrez, Rodrigo A; Soto, Alvaro
2012-09-01
Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning methods, such as Support Vector Machines (SVMs), have shown superior prediction accuracy. However, these methods lack the simple biological intuition provided by co-expression networks (CNs), limiting their practical usefulness. In this work, we present Discriminative Local Subspaces (DLS), a novel method that combines supervised machine learning and co-expression techniques with the goal of systematically predict genes involved in specific biological processes of interest. Unlike traditional CNs, DLS uses the knowledge available in Gene Ontology (GO) to generate informative training sets that guide the discovery of expression signatures: expression patterns that are discriminative for genes involved in the biological process of interest. By linking genes co-expressed with these signatures, DLS is able to construct a discriminative CN that links both, known and previously uncharacterized genes, for the selected biological process. This article focuses on the algorithm behind DLS and shows its predictive power using an Arabidopsis thaliana dataset and a representative set of 101 GO terms from the Biological Process Ontology. Our results show that DLS has a superior average accuracy than both SVMs and CNs. Thus, DLS is able to provide the prediction accuracy of supervised learning methods while maintaining the intuitive understanding of CNs. A MATLAB® implementation of DLS is available at http://virtualplant.bio.puc.cl/cgi-bin/Lab/tools.cgi.
Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods
Wang, C. L.; Funk, L. L.; Riedel, R. A.; Berry, K. D.
2017-05-01
3He gas based neutron Linear-Position-Sensitive Detectors (LPSDs) have been used for many neutron scattering instruments. Traditional Pulse-height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (NGD ratio) on the order of 105-106. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher Linear Discriminant Analysis (FLDA) and three Multivariate Analyses (MVAs) of the features were performed. The NGD ratios are improved by about 102-103 times compared with the traditional PHA method. Our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.
Reynders, Edwin; Maes, Kristof; Lombaert, Geert; De Roeck, Guido
2016-01-01
Identified modal characteristics are often used as a basis for the calibration and validation of dynamic structural models, for structural control, for structural health monitoring, etc. It is therefore important to know their accuracy. In this article, a method for estimating the (co)variance of modal characteristics that are identified with the stochastic subspace identification method is validated for two civil engineering structures. The first structure is a damaged prestressed concrete bridge for which acceleration and dynamic strain data were measured in 36 different setups. The second structure is a mid-rise building for which acceleration data were measured in 10 different setups. There is a good quantitative agreement between the predicted levels of uncertainty and the observed variability of the eigenfrequencies and damping ratios between the different setups. The method can therefore be used with confidence for quantifying the uncertainty of the identified modal characteristics, also when some or all of them are estimated from a single batch of vibration data. Furthermore, the method is seen to yield valuable insight in the variability of the estimation accuracy from mode to mode and from setup to setup: the more informative a setup is regarding an estimated modal characteristic, the smaller is the estimated variance.
Loizou, Nicolas
2017-12-27
In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.
Directory of Open Access Journals (Sweden)
Soojin Cho
2015-04-01
Full Text Available Wireless sensor networks (WSNs facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI technique is selected for system identification, and SSI-based decentralized system identification (SDSI is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
A Sub-Space Method to Detect Multiple Wireless Microphone Signals in TV Band White Space
Dhillon, Harpreet S; Datla, Dinesh; Benonis, Michael; Buehrer, R Michael; Reed, Jeffrey H
2011-01-01
The main hurdle in the realization of dynamic spectrum access (DSA) systems from physical layer perspective is the reliable sensing of low power licensed users. One such scenario shows up in the unlicensed use of TV bands where the TV Band Devices (TVBDs) are required to sense extremely low power wireless microphones (WMs). The lack of technical standard among various wireless manufacturers and the resemblance of certain WM signals to narrow-band interference signals, such as spurious emissions, further aggravate the problem. Due to these uncertainties, it is extremely difficult to abstract the features of WM signals and hence develop robust sensing algorithms. To partly counter these challenges, we develop a two-stage sub-space algorithm that detects multiple narrow-band analog frequency-modulated signals generated by WMs. The performance of the algorithm is verified by using experimentally captured low power WM signals with received power ranging from -100 to -105 dBm. The problem of differentiating between...
The Environmental Technology Innovation Clusters Program advises cluster organizations, encourages collaboration between clusters, tracks U.S. environmental technology clusters, and connects EPA programs to cluster needs.
Wagstaff, Kiri L.
2012-03-01
On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained
Joint fMRI analysis and subject clustering using sparse dictionary learning
Kim, Seung-Jun; Dontaraju, Krishna K.
2017-08-01
Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.
Oracle database performance and scalability a quantitative approach
Liu, Henry H
2011-01-01
A data-driven, fact-based, quantitative text on Oracle performance and scalability With database concepts and theories clearly explained in Oracle's context, readers quickly learn how to fully leverage Oracle's performance and scalability capabilities at every stage of designing and developing an Oracle-based enterprise application. The book is based on the author's more than ten years of experience working with Oracle, and is filled with dependable, tested, and proven performance optimization techniques. Oracle Database Performance and Scalability is divided into four parts that enable reader
A novel 3D scalable video compression algorithm
Somasundaram, Siva; Subbalakshmi, Koduvayur P.
2003-05-01
In this paper we propose a scalable video coding scheme that utilizes the embedded block coding with optimal truncation (EBCOT) compression algorithm. Three dimensional spatio-temporal decomposition of the video sequence succeeded by compression using the EBCOT generates a SNR and resolution scalable bit stream. The proposed video coding algorithm not only performs closer to the MPEG-4 video coding standard in compression efficiency but also provides better SNR and resolution scalability. Experimental results show that the performance of the proposed algorithm does better than the 3-D SPIHT (Set Partitioning in Hierarchial Trees) algorithm by 1.5dB.
Snakemake-a scalable bioinformatics workflow engine
J. Köster (Johannes); S. Rahmann (Sven)
2012-01-01
textabstractSnakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically
Scalable, remote administration of Windows NT.
Energy Technology Data Exchange (ETDEWEB)
Gomberg, M.; Stacey, C.; Sayre, J.
1999-06-08
In the UNIX community there is an overwhelming perception that NT is impossible to manage remotely and that NT administration doesn't scale. This was essentially true with earlier versions of the operating system. Even today, out of the box, NT is difficult to manage remotely. Many tools, however, now make remote management of NT not only possible, but under some circumstances very easy. In this paper we discuss how we at Argonne's Mathematics and Computer Science Division manage all our NT machines remotely from a single console, with minimum locally installed software overhead. We also present NetReg, which is a locally developed tool for scalable registry management. NetReg allows us to apply a registry change to a specified set of machines. It is a command line utility that can be run in either interactive or batch mode and is written in Perl for Win32, taking heavy advantage of the Win32::TieRegistry module.
Scalable conditional induction variables (CIV) analysis
Oancea, Cosmin E.
2015-02-01
Subscripts using induction variables that cannot be expressed as a formula in terms of the enclosing-loop indices appear in the low-level implementation of common programming abstractions such as Alter, or stack operations and pose significant challenges to automatic parallelization. Because the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow-sensitive technique that summarizes both such CIV-based and affine subscripts to program level, using the same representation. Our technique requires no modifications of our dependence tests, which is agnostic to the original shape of the subscripts, and is more powerful than previously reported dependence tests that rely on the pairwise disambiguation of read-write references. We have implemented the CIV analysis in our parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in.
Scalable Notch Antenna System for Multiport Applications
Directory of Open Access Journals (Sweden)
Abdurrahim Toktas
2016-01-01
Full Text Available A novel and compact scalable antenna system is designed for multiport applications. The basic design is built on a square patch with an electrical size of 0.82λ0×0.82λ0 (at 2.4 GHz on a dielectric substrate. The design consists of four symmetrical and orthogonal triangular notches with circular feeding slots at the corners of the common patch. The 4-port antenna can be simply rearranged to 8-port and 12-port systems. The operating band of the system can be tuned by scaling (S the size of the system while fixing the thickness of the substrate. The antenna system with S: 1/1 in size of 103.5×103.5 mm2 operates at the frequency band of 2.3–3.0 GHz. By scaling the antenna with S: 1/2.3, a system of 45×45 mm2 is achieved, and thus the operating band is tuned to 4.7–6.1 GHz with the same scattering characteristic. A parametric study is also conducted to investigate the effects of changing the notch dimensions. The performance of the antenna is verified in terms of the antenna characteristics as well as diversity and multiplexing parameters. The antenna system can be tuned by scaling so that it is applicable to the multiport WLAN, WIMAX, and LTE devices with port upgradability.
A Programmable, Scalable-Throughput Interleaver
Directory of Open Access Journals (Sweden)
Rijshouwer EJC
2010-01-01
Full Text Available The interleaver stages of digital communication standards show a surprisingly large variation in throughput, state sizes, and permutation functions. Furthermore, data rates for 4G standards such as LTE-Advanced will exceed typical baseband clock frequencies of handheld devices. Multistream operation for Software Defined Radio and iterative decoding algorithms will call for ever higher interleave data rates. Our interleave machine is built around 8 single-port SRAM banks and can be programmed to generate up to 8 addresses every clock cycle. The scalable architecture combines SIMD and VLIW concepts with an efficient resolution of bank conflicts. A wide range of cellular, connectivity, and broadcast interleavers have been mapped on this machine, with throughputs up to more than 0.5 Gsymbol/second. Although it was designed for channel interleaving, the application domain of the interleaver extends also to Turbo interleaving. The presented configuration of the architecture is designed as a part of a programmable outer receiver on a prototype board. It offers (near universal programmability to enable the implementation of new interleavers. The interleaver measures 2.09 m in 65 nm CMOS (including memories and proves functional on silicon.
SCTP as scalable video coding transport
Ortiz, Jordi; Graciá, Eduardo Martínez; Skarmeta, Antonio F.
2013-12-01
This study presents an evaluation of the Stream Transmission Control Protocol (SCTP) for the transport of the scalable video codec (SVC), proposed by MPEG as an extension to H.264/AVC. Both technologies fit together properly. On the one hand, SVC permits to split easily the bitstream into substreams carrying different video layers, each with different importance for the reconstruction of the complete video sequence at the receiver end. On the other hand, SCTP includes features, such as the multi-streaming and multi-homing capabilities, that permit to transport robustly and efficiently the SVC layers. Several transmission strategies supported on baseline SCTP and its concurrent multipath transfer (CMT) extension are compared with the classical solutions based on the Transmission Control Protocol (TCP) and the Realtime Transmission Protocol (RTP). Using ns-2 simulations, it is shown that CMT-SCTP outperforms TCP and RTP in error-prone networking environments. The comparison is established according to several performance measurements, including delay, throughput, packet loss, and peak signal-to-noise ratio of the received video.
Scalable Combinatorial Tools for Health Disparities Research
Directory of Open Access Journals (Sweden)
Michael A. Langston
2014-10-01
Full Text Available Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.
Scalability and interoperability within glideinWMS
Energy Technology Data Exchange (ETDEWEB)
Bradley, D.; /Wisconsin U., Madison; Sfiligoi, I.; /Fermilab; Padhi, S.; /UC, San Diego; Frey, J.; /Wisconsin U., Madison; Tannenbaum, T.; /Wisconsin U., Madison
2010-01-01
Physicists have access to thousands of CPUs in grid federations such as OSG and EGEE. With the start-up of the LHC, it is essential for individuals or groups of users to wrap together available resources from multiple sites across multiple grids under a higher user-controlled layer in order to provide a homogeneous pool of available resources. One such system is glideinWMS, which is based on the Condor batch system. A general discussion of glideinWMS can be found elsewhere. Here, we focus on recent advances in extending its reach: scalability and integration of heterogeneous compute elements. We demonstrate that the new developments exceed the design goal of over 10,000 simultaneous running jobs under a single Condor schedd, using strong security protocols across global networks, and sustaining a steady-state job completion rate of a few Hz. We also show interoperability across heterogeneous computing elements achieved using client-side methods. We discuss this technique and the challenges in direct access to NorduGrid and CREAM compute elements, in addition to Globus based systems.
Imaging of heart acoustic based on the sub-space methods using a microphone array.
Moghaddasi, Hanie; Almasganj, Farshad; Zoroufian, Arezoo
2017-07-01
Heart disease is one of the leading causes of death around the world. Phonocardiogram (PCG) is an important bio-signal which represents the acoustic activity of heart, typically without any spatiotemporal information of the involved acoustic sources. The aim of this study is to analyze the PCG by employing a microphone array by which the heart internal sound sources could be localized, too. In this paper, it is intended to propose a modality by which the locations of the active sources in the heart could also be investigated, during a cardiac cycle. In this way, a microphone array with six microphones is employed as the recording set up to be put on the human chest. In the following, the Group Delay MUSIC algorithm which is a sub-space based localization method is used to estimate the location of the heart sources in different phases of the PCG. We achieved to 0.14cm mean error for the sources of first heart sound (S1) simulator and 0.21cm mean error for the sources of second heart sound (S2) simulator with Group Delay MUSIC algorithm. The acoustical diagrams created for human subjects show distinct patterns in various phases of the cardiac cycles such as the first and second heart sounds. Moreover, the evaluated source locations for the heart valves are matched with the ones that are obtained via the 4-dimensional (4D) echocardiography applied, to a real human case. Imaging of heart acoustic map presents a new outlook to indicate the acoustic properties of cardiovascular system and disorders of valves and thereby, in the future, could be used as a new diagnostic tool. Copyright © 2017. Published by Elsevier B.V.
Shokravi, H.; Bakhary, NH
2017-11-01
Subspace System Identification (SSI) is considered as one of the most reliable tools for identification of system parameters. Performance of a SSI scheme is considerably affected by the structure of the associated identification algorithm. Weight matrix is a variable in SSI that is used to reduce the dimensionality of the state-space equation. Generally one of the weight matrices of Principle Component (PC), Unweighted Principle Component (UPC) and Canonical Variate Analysis (CVA) are used in the structure of a SSI algorithm. An increasing number of studies in the field of structural health monitoring are using SSI for damage identification. However, studies that evaluate the performance of the weight matrices particularly in association with accuracy, noise resistance, and time complexity properties are very limited. In this study, the accuracy, noise-robustness, and time-efficiency of the weight matrices are compared using different qualitative and quantitative metrics. Three evaluation metrics of pole analysis, fit values and elapsed time are used in the assessment process. A numerical model of a mass-spring-dashpot and operational data is used in this research paper. It is observed that the principal components obtained using PC algorithms are more robust against noise uncertainty and give more stable results for the pole distribution. Furthermore, higher estimation accuracy is achieved using UPC algorithm. CVA had the worst performance for pole analysis and time efficiency analysis. The superior performance of the UPC algorithm in the elapsed time is attributed to using unit weight matrices. The obtained results demonstrated that the process of reducing dimensionality in CVA and PC has not enhanced the time efficiency but yield an improved modal identification in PC.
Signal subspace change detection in averaged multi-look SAR imagery
Ranney, Kenneth; Soumekh, Mehrdad
2005-05-01
Modern Synthetic Aperture Radar (SAR) signal processing algorithms could retrieve accurate and subtle information regarding a scene that is being interrogated by an airborne radar system. An important reconnaissance problem that is being studied via the use of SAR systems and their sophisticated signal processing methods involves detecting changes in an imaged scene. In these problems, the user interrogates a scene with a SAR system at two different time points (e.g. different days); the resultant two SAR databases that we refer to as reference and test data, are used to determine where targets have entered or left the imaged scene between the two data acquisitions. For instance, X band SAR systems have the potential to become a potent tool to determine whether mines have been recently placed in an area. This paper describes an algorithm for detecting changes in averaged multi-look SAR imagery. Averaged multi-look SAR images are preferable to full aperture SAR reconstructions when the imaging algorithm is approximation based (e.g. polar format processing), or motion data are not accurate over a long full aperture. We study the application of a SAR detection method, known as Signal Subspace Processing, that is based on the principles of 2D adaptive filtering. We identify the change detection problem as a binary hypothesis-testing problem, and identify an error signal and its normalized version to determine whether i) there is no change in the imaged scene; or ii) a target has been added to the imaged scene. A statistical analysis of the error signal is provided to show its properties and merits. Results are provided for data collected by an X band SAR platform and processed to form non-coherently look-averaged SAR images.
Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.
Li, Ke; Pang, Kaiyue; Song, Yi-Zhe; Hospedales, Timothy M; Xiang, Tao; Zhang, Honggang
2017-08-25
We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address these challenges, we propose to bridge the image-sketch gap both at the high-level via parts and attributes, as well as at the low-level, via introducing a new domain alignment method. More specifically, (i) we contribute a dataset with 304 photos and 912 sketches, where each sketch and image is annotated with its semantic parts and associated part-level attributes. With the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and provide pose-aligned sketch-image comparisons. To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better align the domains. Finally (iv) these are combined in a matching framework integrating aligned low-level features, mid-level geometric structure and high-level semantic attributes. Extensive experiments conducted on our new dataset demonstrate effectiveness of the proposed method.
Razavipour, Fatemeh; Sameni, Reza
2015-07-15
Event related potentials (ERP) are time-locked electrical activities of the brain in direct response to a specific sensory, cognitive, or motor stimulus. ERP components, such as the P300 wave, which are involved in the process of decision-making, help scientists diagnose specific cognitive disabilities. In this study, we utilize the angles between multichannel electroencephalogram (EEG) subspaces in different frequency bands, as a similarity factor for studying the spatial coherency between ERP frequency responses. A matched filter is used to enhance the ERP from background EEG. While previous researches have focused on frequencies below 10 Hz, as the major frequency band of ERP, it is shown that by using the proposed method, significant ERP-related information can also be found in the 25-40 Hz band. These frequency bands are selected by calculating the correlation coefficient between P300 response segments and synthetic EEG, and ERP segments without P300 waves, and by rejecting the bands having the most association with background EEG and non-P300 components. The significance of the results is assessed by real EEG acquired in brain computer interface experiments versus synthetic EEG produced by existing methods in the literature, to assure that the results are not systematic side effects of the proposed framework. The overall results show that the equivalent dipoles corresponding to narrow-band events in the brain are spatially coherent within different (not necessarily adjacent) frequency bands. The results of this study can lead into novel perspectives in ERP studies. Copyright © 2015 Elsevier B.V. All rights reserved.
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. Approximation Clustering. Clustering within (1+ ε) of the optimum cost. ε is user defined tolerance. For metric spaces even approximating is. hard (below, say 30%). Euclidean k-median in fixed dimension can. be approximated in polynomial time.
ARC Code TI: Block-GP: Scalable Gaussian Process Regression
National Aeronautics and Space Administration — Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear...
Scalable pattern recognition algorithms applications in computational biology and bioinformatics
Maji, Pradipta
2014-01-01
Reviews the development of scalable pattern recognition algorithms for computational biology and bioinformatics Includes numerous examples and experimental results to support the theoretical concepts described Concludes each chapter with directions for future research and a comprehensive bibliography
Scalability of telecom cloud architectures for live-TV distribution
Asensio Carmona, Adrian; Contreras, Luis Miguel; Ruiz Ramírez, Marc; López Álvarez, Victor; Velasco Esteban, Luis Domingo
2015-01-01
A hierarchical distributed telecom cloud architecture for live-TV distribution exploiting flexgrid networking and SBVTs is proposed. Its scalability is compared to that of a centralized architecture. Cost savings as high as 32 % are shown. Peer Reviewed
Evaluating the Scalability of Enterprise JavaBeans Technology
Energy Technology Data Exchange (ETDEWEB)
Liu, Yan (Jenny); Gorton, Ian; Liu, Anna; Chen, Shiping; Paul A Strooper; Pornsiri Muenchaisri
2002-12-04
One of the major problems in building large-scale distributed systems is to anticipate the performance of the eventual solution before it has been built. This problem is especially germane to Internet-based e-business applications, where failure to provide high performance and scalability can lead to application and business failure. The fundamental software engineering problem is compounded by many factors, including individual application diversity, software architecture trade-offs, COTS component integration requirements, and differences in performance of various software and hardware infrastructures. In this paper, we describe the results of an empirical investigation into the scalability of a widely used distributed component technology, Enterprise JavaBeans (EJB). A benchmark application is developed and tested to measure the performance of a system as both the client load and component infrastructure are scaled up. A scalability metric from the literature is then applied to analyze the scalability of the EJB component infrastructure under two different architectural solutions.
Scalable RFCMOS Model for 90 nm Technology
Directory of Open Access Journals (Sweden)
Ah Fatt Tong
2011-01-01
Full Text Available This paper presents the formation of the parasitic components that exist in the RF MOSFET structure during its high-frequency operation. The parasitic components are extracted from the transistor's S-parameter measurement, and its geometry dependence is studied with respect to its layout structure. Physical geometry equations are proposed to represent these parasitic components, and by implementing them into the RF model, a scalable RFCMOS model, that is, valid up to 49.85 GHz is demonstrated. A new verification technique is proposed to verify the quality of the developed scalable RFCMOS model. The proposed technique can shorten the verification time of the scalable RFCMOS model and ensure that the coded scalable model file is error-free and thus more reliable to use.
Scalable-to-lossless transform domain distributed video coding
DEFF Research Database (Denmark)
Huang, Xin; Ukhanova, Ann; Veselov, Anton
2010-01-01
Distributed video coding (DVC) is a novel approach providing new features as low complexity encoding by mainly exploiting the source statistics at the decoder based on the availability of decoder side information. In this paper, scalable-tolossless DVC is presented based on extending a lossy...... TransformDomain Wyner-Ziv (TDWZ) distributed video codec with feedback.The lossless coding is obtained by using a reversible integer DCT.Experimental results show that the performance of the proposed scalable-to-lossless TDWZ video codec can outperform alternatives based on the JPEG 2000 standard. The TDWZ...... codec provides frame by frame encoding. Comparing the lossless coding efficiency, the proposed scalable-to-lossless TDWZ video codec can save up to 5%-13% bits compared to JPEG LS and H.264 Intra frame lossless coding and do so as a scalable-to-lossless coding....
Improving the Performance Scalability of the Community Atmosphere Model
Energy Technology Data Exchange (ETDEWEB)
Mirin, Arthur [Lawrence Livermore National Laboratory (LLNL); Worley, Patrick H [ORNL
2012-01-01
The Community Atmosphere Model (CAM), which serves as the atmosphere component of the Community Climate System Model (CCSM), is the most computationally expensive CCSM component in typical configurations. On current and next-generation leadership class computing systems, the performance of CAM is tied to its parallel scalability. Improving performance scalability in CAM has been a challenge, due largely to algorithmic restrictions necessitated by the polar singularities in its latitude-longitude computational grid. Nevertheless, through a combination of exploiting additional parallelism, implementing improved communication protocols, and eliminating scalability bottlenecks, we have been able to more than double the maximum throughput rate of CAM on production platforms. We describe these improvements and present results on the Cray XT5 and IBM BG/P. The approaches taken are not specific to CAM and may inform similar scalability enhancement activities for other codes.
Parallelism and Scalability in an Image Processing Application
DEFF Research Database (Denmark)
Rasmussen, Morten Sleth; Stuart, Matthias Bo; Karlsson, Sven
2008-01-01
parallel programs. This paper investigates parallelism and scalability of an embedded image processing application. The major challenges faced when parallelizing the application were to extract enough parallelism from the application and to reduce load imbalance. The application has limited immediately...
Scalable Multiple-Description Image Coding Based on Embedded Quantization
Directory of Open Access Journals (Sweden)
Moerman Ingrid
2007-01-01
Full Text Available Scalable multiple-description (MD coding allows for fine-grain rate adaptation as well as robust coding of the input source. In this paper, we present a new approach for scalable MD coding of images, which couples the multiresolution nature of the wavelet transform with the robustness and scalability features provided by embedded multiple-description scalar quantization (EMDSQ. Two coding systems are proposed that rely on quadtree coding to compress the side descriptions produced by EMDSQ. The proposed systems are capable of dynamically adapting the bitrate to the available bandwidth while providing robustness to data losses. Experiments performed under different simulated network conditions demonstrate the effectiveness of the proposed scalable MD approach for image streaming over error-prone channels.
Scalable Multiple-Description Image Coding Based on Embedded Quantization
Directory of Open Access Journals (Sweden)
Augustin I. Gavrilescu
2007-02-01
Full Text Available Scalable multiple-description (MD coding allows for fine-grain rate adaptation as well as robust coding of the input source. In this paper, we present a new approach for scalable MD coding of images, which couples the multiresolution nature of the wavelet transform with the robustness and scalability features provided by embedded multiple-description scalar quantization (EMDSQ. Two coding systems are proposed that rely on quadtree coding to compress the side descriptions produced by EMDSQ. The proposed systems are capable of dynamically adapting the bitrate to the available bandwidth while providing robustness to data losses. Experiments performed under different simulated network conditions demonstrate the effectiveness of the proposed scalable MD approach for image streaming over error-prone channels.
DEFF Research Database (Denmark)
Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha
2018-01-01
In this chapter, we investigate corporate social responsibility (CSR) in industrial clusters in the Indian context. We use the definition of CSR as given in the Indian Ministry of Corporate Affairs’ National Voluntary Guidelines (NVGs) for Business Responsibility: ‘the commitment of an enterprise...... sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country....... At the same time, several attempts have been made at promoting the adoption of CSR in MSMEs in Indian industrial clusters. In fact, India has proved to be a kind of laboratory for experimenting with different types of cluster-based CSR and is thus an interesting location in relation to the broader aim...
TriG: Next Generation Scalable Spaceborne GNSS Receiver
Tien, Jeffrey Y.; Okihiro, Brian Bachman; Esterhuizen, Stephan X.; Franklin, Garth W.; Meehan, Thomas K.; Munson, Timothy N.; Robison, David E.; Turbiner, Dmitry; Young, Lawrence E.
2012-01-01
TriG is the next generation NASA scalable space GNSS Science Receiver. It will track all GNSS and additional signals (i.e. GPS, GLONASS, Galileo, Compass and Doris). Scalable 3U architecture and fully software and firmware recofigurable, enabling optimization to meet specific mission requirements. TriG GNSS EM is currently undergoing testing and is expected to complete full performance testing later this year.
SDC: Scalable description coding for adaptive streaming media
Quinlan, Jason J.; Zahran, Ahmed H.; Sreenan, Cormac J.
2012-01-01
Video compression techniques enable adaptive media streaming over heterogeneous links to end-devices. Scalable Video Coding (SVC) and Multiple Description Coding (MDC) represent well-known techniques for video compression with distinct characteristics in terms of bandwidth efficiency and resiliency to packet loss. In this paper, we present Scalable Description Coding (SDC), a technique to compromise the tradeoff between bandwidth efficiency and error resiliency without sacrificing user-percei...
Woźniak, M.
2016-06-02
We study the features of a new mixed integration scheme dedicated to solving the non-stationary variational problems. The scheme is composed of the FEM approximation with respect to the space variable coupled with a 3-leveled time integration scheme with a linearized right-hand side operator. It was applied in solving the Cahn-Hilliard parabolic equation with a nonlinear, fourth-order elliptic part. The second order of the approximation along the time variable was proven. Moreover, the good scalability of the software based on this scheme was confirmed during simulations. We verify the proposed time integration scheme by monitoring the Ginzburg-Landau free energy. The numerical simulations are performed by using a parallel multi-frontal direct solver executed over STAMPEDE Linux cluster. Its scalability was compared to the results of the three direct solvers, including MUMPS, SuperLU and PaSTiX.
Joint Experimentation on Scalable Parallel Processors (JESPP)
2006-04-01
start, they can be observed joining the federation by executing RTI print commands within the parser of any local federates. Problems with...Myrinet / 2816 Linux Networx 8051 11264 7 Lr . Livermore National Lab U.S./2002 MCR Linux Cluster Xeon 2.4GHz, Quadrics / 2304 Linux Networx...Quadrics 7634 11060 8 Lr . Livermore National Lab U.S./2000 ASCI White, SP Power3 375 MHz / 8192 IBM 7304 12288 9 NERSC/LBNL U.S./2002 SP
Scalable persistent identifier systems for dynamic datasets
Golodoniuc, P.; Cox, S. J. D.; Klump, J. F.
2016-12-01
Reliable and persistent identification of objects, whether tangible or not, is essential in information management. Many Internet-based systems have been developed to identify digital data objects, e.g., PURL, LSID, Handle, ARK. These were largely designed for identification of static digital objects. The amount of data made available online has grown exponentially over the last two decades and fine-grained identification of dynamically generated data objects within large datasets using conventional systems (e.g., PURL) has become impractical. We have compared capabilities of various technological solutions to enable resolvability of data objects in dynamic datasets, and developed a dataset-centric approach to resolution of identifiers. This is particularly important in Semantic Linked Data environments where dynamic frequently changing data is delivered live via web services, so registration of individual data objects to obtain identifiers is impractical. We use identifier patterns and pattern hierarchies for identification of data objects, which allows relationships between identifiers to be expressed, and also provides means for resolving a single identifier into multiple forms (i.e. views or representations of an object). The latter can be implemented through (a) HTTP content negotiation, or (b) use of URI querystring parameters. The pattern and hierarchy approach has been implemented in the Linked Data API supporting the United Nations Spatial Data Infrastructure (UNSDI) initiative and later in the implementation of geoscientific data delivery for the Capricorn Distal Footprints project using International Geo Sample Numbers (IGSN). This enables flexible resolution of multi-view persistent identifiers and provides a scalable solution for large heterogeneous datasets.
Microscopic Characterization of Scalable Coherent Rydberg Superatoms
Directory of Open Access Journals (Sweden)
Johannes Zeiher
2015-08-01
Full Text Available Strong interactions can amplify quantum effects such that they become important on macroscopic scales. Controlling these coherently on a single-particle level is essential for the tailored preparation of strongly correlated quantum systems and opens up new prospects for quantum technologies. Rydberg atoms offer such strong interactions, which lead to extreme nonlinearities in laser-coupled atomic ensembles. As a result, multiple excitation of a micrometer-sized cloud can be blocked while the light-matter coupling becomes collectively enhanced. The resulting two-level system, often called a “superatom,” is a valuable resource for quantum information, providing a collective qubit. Here, we report on the preparation of 2 orders of magnitude scalable superatoms utilizing the large interaction strength provided by Rydberg atoms combined with precise control of an ensemble of ultracold atoms in an optical lattice. The latter is achieved with sub-shot-noise precision by local manipulation of a two-dimensional Mott insulator. We microscopically confirm the superatom picture by in situ detection of the Rydberg excitations and observe the characteristic square-root scaling of the optical coupling with the number of atoms. Enabled by the full control over the atomic sample, including the motional degrees of freedom, we infer the overlap of the produced many-body state with a W state from the observed Rabi oscillations and deduce the presence of entanglement. Finally, we investigate the breakdown of the superatom picture when two Rydberg excitations are present in the system, which leads to dephasing and a loss of coherence.
Microscopic Characterization of Scalable Coherent Rydberg Superatoms
Zeiher, Johannes; Schauß, Peter; Hild, Sebastian; Macrı, Tommaso; Bloch, Immanuel; Gross, Christian
2015-07-01
Strong interactions can amplify quantum effects such that they become important on macroscopic scales. Controlling these coherently on a single-particle level is essential for the tailored preparation of strongly correlated quantum systems and opens up new prospects for quantum technologies. Rydberg atoms offer such strong interactions, which lead to extreme nonlinearities in laser-coupled atomic ensembles. As a result, multiple excitation of a micrometer-sized cloud can be blocked while the light-matter coupling becomes collectively enhanced. The resulting two-level system, often called a "superatom," is a valuable resource for quantum information, providing a collective qubit. Here, we report on the preparation of 2 orders of magnitude scalable superatoms utilizing the large interaction strength provided by Rydberg atoms combined with precise control of an ensemble of ultracold atoms in an optical lattice. The latter is achieved with sub-shot-noise precision by local manipulation of a two-dimensional Mott insulator. We microscopically confirm the superatom picture by in situ detection of the Rydberg excitations and observe the characteristic square-root scaling of the optical coupling with the number of atoms. Enabled by the full control over the atomic sample, including the motional degrees of freedom, we infer the overlap of the produced many-body state with a W state from the observed Rabi oscillations and deduce the presence of entanglement. Finally, we investigate the breakdown of the superatom picture when two Rydberg excitations are present in the system, which leads to dephasing and a loss of coherence.
Physical principles for scalable neural recording.
Marblestone, Adam H; Zamft, Bradley M; Maguire, Yael G; Shapiro, Mikhail G; Cybulski, Thaddeus R; Glaser, Joshua I; Amodei, Dario; Stranges, P Benjamin; Kalhor, Reza; Dalrymple, David A; Seo, Dongjin; Alon, Elad; Maharbiz, Michel M; Carmena, Jose M; Rabaey, Jan M; Boyden, Edward S; Church, George M; Kording, Konrad P
2013-01-01
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power-bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and
Memory-Scalable GPU Spatial Hierarchy Construction.
Qiming Hou; Xin Sun; Kun Zhou; Lauterbach, C; Manocha, D
2011-04-01
Recent GPU algorithms for constructing spatial hierarchies have achieved promising performance for moderately complex models by using the breadth-first search (BFS) construction order. While being able to exploit the massive parallelism on the GPU, the BFS order also consumes excessive GPU memory, which becomes a serious issue for interactive applications involving very complex models with more than a few million triangles. In this paper, we propose to use the partial breadth-first search (PBFS) construction order to control memory consumption while maximizing performance. We apply the PBFS order to two hierarchy construction algorithms. The first algorithm is for kd-trees that automatically balances between the level of parallelism and intermediate memory usage. With PBFS, peak memory consumption during construction can be efficiently controlled without costly CPU-GPU data transfer. We also develop memory allocation strategies to effectively limit memory fragmentation. The resulting algorithm scales well with GPU memory and constructs kd-trees of models with millions of triangles at interactive rates on GPUs with 1 GB memory. Compared with existing algorithms, our algorithm is an order of magnitude more scalable for a given GPU memory bound. The second algorithm is for out-of-core bounding volume hierarchy (BVH) construction for very large scenes based on the PBFS construction order. At each iteration, all constructed nodes are dumped to the CPU memory, and the GPU memory is freed for the next iteration's use. In this way, the algorithm is able to build trees that are too large to be stored in the GPU memory. Experiments show that our algorithm can construct BVHs for scenes with up to 20 M triangles, several times larger than previous GPU algorithms.
Minku, Leandro L.
2017-10-06
Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.
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.
Wang, F.; Huang, Y.-Y.; Zhang, Z.-Y.; Zu, C.; Hou, P.-Y.; Yuan, X.-X.; Wang, W.-B.; Zhang, W.-G.; He, L.; Chang, X.-Y.; Duan, L.-M.
2017-10-01
We experimentally demonstrate room-temperature storage of quantum entanglement using two nuclear spins weakly coupled to the electronic spin carried by a single nitrogen-vacancy center in diamond. We realize universal quantum gate control over the three-qubit spin system and produce entangled states in the decoherence-free subspace of the two nuclear spins. By injecting arbitrary collective noise, we demonstrate that the decoherence-free entangled state has coherence time longer than that of other entangled states by an order of magnitude in our experiment.
Everitt, Brian S; Leese, Morven; Stahl, Daniel
2011-01-01
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons
Distributed controller clustering in software defined networks.
Directory of Open Access Journals (Sweden)
Ahmed Abdelaziz
Full Text Available Software Defined Networking (SDN is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN SDN and Open Network Operating System (ONOS controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.
Distributed controller clustering in software defined networks.
Abdelaziz, Ahmed; Fong, Ang Tan; Gani, Abdullah; Garba, Usman; Khan, Suleman; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond
2017-01-01
Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.
DEFF Research Database (Denmark)
Böcker, S.; Baumbach, Jan
2013-01-01
The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side. The prob......The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side....... The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications...
Singer, B. Sh.
2008-12-01
The paper presents a new code for modelling electromagnetic fields in complicated 3-D environments and provides examples of the code application. The code is based on an integral equation (IE) for the scattered electromagnetic field, presented in the form used by the Modified Iterative Dissipative Method (MIDM). This IE possesses contraction properties that allow it to be solved iteratively. As a result, for an arbitrary earth model and any source of the electromagnetic field, the sequence of approximations converges to the solution at any frequency. The system of linear equations that represents a finite-dimensional counterpart of the continuous IE is derived using a projection definition of the system matrix. According to this definition, the matrix is calculated by integrating the Green's function over the `source' and `receiver' cells of the numerical grid. Such a system preserves contraction properties of the continuous equation and can be solved using the same iterative technique. The condition number of the system matrix and, therefore, the convergence rate depends only on the physical properties of the model under consideration. In particular, these parameters remain independent of the numerical grid used for numerical simulation. Applied to the system of linear equations, the iterative perturbation approach generates a sequence of approximations, converging to the solution. The number of iterations is significantly reduced by finding the best possible approximant inside the Krylov subspace, which spans either all accumulated iterates or, if it is necessary to save the memory, only a limited number of the latest iterates. Optimization significantly reduces the number of iterates and weakens its dependence on the lateral contrast of the model. Unlike more traditional conjugate gradient approaches, the iterations are terminated when the approximate solution reaches the requested relative accuracy. The number of the required iterates, which for simple
Contractions without non-trivial invariant subspaces satisfying a positivity condition
Directory of Open Access Journals (Sweden)
Bhaggy Duggal
2016-04-01
Full Text Available Abstract An operator A ∈ B ( H $A\\in B(\\mathcal{H}$ , the algebra of bounded linear transformations on a complex infinite dimensional Hilbert space H $\\mathcal{H}$ , belongs to class A ( n $\\mathcal{A}(n$ (resp., A ( ∗ − n $\\mathcal{A}(*-n$ if | A | 2 ≤ | A n + 1 | 2 n + 1 $\\vert A\\vert^{2}\\leq\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}$ (resp., | A ∗ | 2 ≤ | A n + 1 | 2 n + 1 $\\vert A^{*}\\vert^{2}\\leq \\vert A^{n+1}\\vert^{\\frac{2}{n+1}}$ for some integer n ≥ 1 $n\\geq1$ , and an operator A ∈ B ( H $A\\in B(\\mathcal{H}$ is called n-paranormal, denoted A ∈ P ( n $A\\in \\mathcal{P}(n$ (resp., ∗ − n $*-n$ -paranormal, denoted A ∈ P ( ∗ − n $A\\in \\mathcal{P}(*-n$ if ∥ A x ∥ n + 1 ≤ ∥ A n + 1 x ∥ ∥ x ∥ n $\\Vert Ax\\Vert ^{n+1}\\leq \\Vert A^{n+1}x\\Vert \\Vert x\\Vert ^{n}$ (resp., ∥ A ∗ x ∥ n + 1 ≤ ∥ A n + 1 x ∥ ∥ x ∥ n $\\Vert A^{*}x\\Vert ^{n+1}\\leq \\Vert A^{n+1}x\\Vert \\Vert x\\Vert ^{n}$ for some integer n ≥ 1 $n\\geq 1$ and all x ∈ H $x \\in\\mathcal{H}$ . In this paper, we prove that if A ∈ { A ( n ∪ P ( n } $A\\in\\{\\mathcal{A}(n\\cup \\mathcal{P}(n\\}$ (resp., A ∈ { A ( ∗ − n ∪ P ( ∗ − n } $A\\in\\{\\mathcal{A}(*-n\\cup \\mathcal{P}(*-n\\}$ is a contraction without a non-trivial invariant subspace, then A, | A n + 1 | 2 n + 1 − | A | 2 $\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}-\\vert A\\vert^{2}$ and | A n + 1 | 2 − n + 1 n | A | 2 + 1 $\\vert A^{n+1}\\vert^{2}- {\\frac{n+1}{n}}\\vert A\\vert^{2}+ 1$ (resp., A, | A n + 1 | 2 n + 1 − | A ∗ | 2 $\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}-\\vert A^{*}\\vert^{2}$ and | A n + 2 | 2 − n + 1 n | A | 2 + 1 ≥ 0 $\\vert A^{n+2}\\vert^{2}- {\\frac{n+1}{n}}\\vert A\\vert^{2}+ 1\\geq0$ are proper contractions.
Scalability of Several Asynchronous Many-Task Models for In Situ Statistical Analysis.
Energy Technology Data Exchange (ETDEWEB)
Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Kolla, Hemanth [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Borghesi, Giulio [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2017-05-01
This report is a sequel to [PB16], in which we provided a first progress report on research and development towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system. This earlier work included a prototype implementation of a proposed solution, using a proxy mini-application as a surrogate for a full-scale scientific simulation code. The first scalability studies were conducted with the above on modestly-sized experimental clusters. In contrast, in the current work we have integrated our in situ analysis engines with a full-size scientific application (S3D, using the Legion-SPMD model), and have conducted nu- merical tests on the largest computational platform currently available for DOE science ap- plications. We also provide details regarding the design and development of a light-weight asynchronous collectives library. We describe how this library is utilized within our SPMD- Legion S3D workflow, and compare the data aggregation technique deployed herein to the approach taken within our previous work.
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework
2012-01-01
Background For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed. Results We present a sequence database search engine that is specifically designed to run efficiently on the Hadoop MapReduce distributed computing framework. The search engine implements the K-score algorithm, generating comparable output for the same input files as the original implementation. The scalability of the system is shown, and the architecture required for the development of such distributed processing is discussed. Conclusion The software is scalable in its ability to handle a large peptide database, numerous modifications and large numbers of spectra. Performance scales with the number of processors in the cluster, allowing throughput to expand with the available resources. PMID:23216909
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework
Directory of Open Access Journals (Sweden)
Lewis Steven
2012-12-01
Full Text Available Abstract Background For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed. Results We present a sequence database search engine that is specifically designed to run efficiently on the Hadoop MapReduce distributed computing framework. The search engine implements the K-score algorithm, generating comparable output for the same input files as the original implementation. The scalability of the system is shown, and the architecture required for the development of such distributed processing is discussed. Conclusion The software is scalable in its ability to handle a large peptide database, numerous modifications and large numbers of spectra. Performance scales with the number of processors in the cluster, allowing throughput to expand with the available resources.
A scalable multi-DLP pico-projector system for virtual reality
Teubl, F.; Kurashima, C.; Cabral, M.; Fels, S.; Lopes, R.; Zuffo, M.
2014-03-01
Virtual Reality (VR) environments can offer immersion, interaction and realistic images to users. A VR system is usually expensive and requires special equipment in a complex setup. One approach is to use Commodity-Off-The-Shelf (COTS) desktop multi-projectors manually or camera based calibrated to reduce the cost of VR systems without significant decrease of the visual experience. Additionally, for non-planar screen shapes, special optics such as lenses and mirrors are required thus increasing costs. We propose a low-cost, scalable, flexible and mobile solution that allows building complex VR systems that projects images onto a variety of arbitrary surfaces such as planar, cylindrical and spherical surfaces. This approach combines three key aspects: 1) clusters of DLP-picoprojectors to provide homogeneous and continuous pixel density upon arbitrary surfaces without additional optics; 2) LED lighting technology for energy efficiency and light control; 3) smaller physical footprint for flexibility purposes. Therefore, the proposed system is scalable in terms of pixel density, energy and physical space. To achieve these goals, we developed a multi-projector software library called FastFusion that calibrates all projectors in a uniform image that is presented to viewers. FastFusion uses a camera to automatically calibrate geometric and photometric correction of projected images from ad-hoc positioned projectors, the only requirement is some few pixels overlapping amongst them. We present results with eight Pico-projectors, with 7 lumens (LED) and DLP 0.17 HVGA Chipset.
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework.
Lewis, Steven; Csordas, Attila; Killcoyne, Sarah; Hermjakob, Henning; Hoopmann, Michael R; Moritz, Robert L; Deutsch, Eric W; Boyle, John
2012-12-05
For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed. We present a sequence database search engine that is specifically designed to run efficiently on the Hadoop MapReduce distributed computing framework. The search engine implements the K-score algorithm, generating comparable output for the same input files as the original implementation. The scalability of the system is shown, and the architecture required for the development of such distributed processing is discussed. The software is scalable in its ability to handle a large peptide database, numerous modifications and large numbers of spectra. Performance scales with the number of processors in the cluster, allowing throughput to expand with the available resources.
Large-Scale Multi-Dimensional Document Clustering on GPU Clusters
Energy Technology Data Exchange (ETDEWEB)
Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL
2010-01-01
Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteennode GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrates the potential of GPU clusters to efficiently solve massive data mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.
Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.
Ma, Chao; Lam, Fan; Johnson, Curtis L; Liang, Zhi-Pei
2016-02-01
To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain. © 2015 Wiley Periodicals, Inc.
Cai, Yunpeng; Sun, Yijun
2011-08-01
Taxonomy-independent analysis plays an essential role in microbial community analysis. Hierarchical clustering is one of the most widely employed approaches to finding operational taxonomic units, the basis for many downstream analyses. Most existing algorithms have quadratic space and computational complexities, and thus can be used only for small or medium-scale problems. We propose a new online learning-based algorithm that simultaneously addresses the space and computational issues of prior work. The basic idea is to partition a sequence space into a set of subspaces using a partition tree constructed using a pseudometric, then recursively refine a clustering structure in these subspaces. The technique relies on new methods for fast closest-pair searching and efficient dynamic insertion and deletion of tree nodes. To avoid exhaustive computation of pairwise distances between clusters, we represent each cluster of sequences as a probabilistic sequence, and define a set of operations to align these probabilistic sequences and compute genetic distances between them. We present analyses of space and computational complexity, and demonstrate the effectiveness of our new algorithm using a human gut microbiota data set with over one million sequences. The new algorithm exhibits a quasilinear time and space complexity comparable to greedy heuristic clustering algorithms, while achieving a similar accuracy to the standard hierarchical clustering algorithm.
A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.
Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain
2015-10-01
Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.
Scalable analysis of movement data for extracting and exploring significant places.
Andrienko, Gennady; Andrienko, Natalia; Hurter, Christophe; Rinzivillo, Salvatore; Wrobel, Stefan
2013-07-01
Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
EnsCat: clustering of categorical data via ensembling.
Clarke, Bertrand S; Amiri, Saeid; Clarke, Jennifer L
2016-09-15
Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat .
Directory of Open Access Journals (Sweden)
Sarmiza Pencea
2010-10-01
Full Text Available Clusters are complex economic structures in which similar companies, their up-stream and down-stream business partners, universities, research institutes, educational units, various service providers, diverse private and public institutions concentrate geografically, striving to get economies of agglomeration and scale, to capitalize on the resulting spill over effects, to cut costs, to better harness resources, to exchange information and experience, to improve quality, innovation, skills and productivity. By somehow unexpectedly combining competition and cooperation, they form a new, sophisticated stage in the evolution of production structures in quest of higher efficiency. This paper forays into the world of clusters and clusterization, which seem to increasingly capture the interest of businesses, scholars and policy makers. It looks at what clusters are, how they arise, what are their specific features, what benefits and challenges they can generate for companies and for the regions in which they locate and if and how they should be fostered by industrial policy interventions. The conclusion is that clusters can be very important development triggers and therefore they should be encouraged and nurtured by adequate policy measures. They should not only be used as a regular policy tool, but be placed at the very center of the development strategies of emerging economies.
A scalable synthesis of highly stable and water dispersible Ag 44(SR)30 nanoclusters
AbdulHalim, Lina G.
2013-01-01
We report the synthesis of atomically monodisperse thiol-protected silver nanoclusters [Ag44(SR)30] m, (SR = 5-mercapto-2-nitrobenzoic acid) in which the product nanocluster is highly stable in contrast to previous preparation methods. The method is one-pot, scalable, and produces nanoclusters that are stable in aqueous solution for at least 9 months at room temperature under ambient conditions, with very little degradation to their unique UV-Vis optical absorption spectrum. The composition, size, and monodispersity were determined by electrospray ionization mass spectrometry and analytical ultracentrifugation. The produced nanoclusters are likely to be in a superatom charge-state of m = 4-, due to the fact that their optical absorption spectrum shares most of the unique features of the intense and broadly absorbing nanoparticles identified as [Ag44(SR) 30]4- by Harkness et al. (Nanoscale, 2012, 4, 4269). A protocol to transfer the nanoclusters to organic solvents is also described. Using the disperse nanoclusters in organic media, we fabricated solid-state films of [Ag44(SR)30]m that retained all the distinct features of the optical absorption spectrum of the nanoclusters in solution. The films were studied by X-ray diffraction and photoelectron spectroscopy in order to investigate their crystallinity, atomic composition and valence band structure. The stability, scalability, and the film fabrication method demonstrated in this work pave the way towards the crystallization of [Ag44(SR)30]m and its full structural determination by single crystal X-ray diffraction. Moreover, due to their unique and attractive optical properties with multiple optical transitions, we anticipate these clusters to find practical applications in light-harvesting, such as photovoltaics and photocatalysis, which have been hindered so far by the instability of previous generations of the cluster. © 2013 The Royal Society of Chemistry.
Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis
Ferdosi, Bilkis J.; Roerdink, Jos B. T. M.
2011-01-01
High-dimensional data visualization is receiving increasing interest because of the growing abundance of high-dimensional datasets. To understand such datasets, visualization of the structures present in the data, such as clusters, can be an invaluable tool. Structures may be present in the full
DEFF Research Database (Denmark)
Christensen, Thomas Budde
The cluster theory attributed to Michael Porter has significantly influenced industrial policies in countries across Europe and North America since the beginning of the 1990s. Institutions such as the EU, OECD and the World Bank and governments in countries such as the UK, France, The Netherlands......, Portugal and New Zealand have adopted the concept. Public sector interventions that aim to support cluster development in industries most often focus upon economic policy goals such as enhanced employment and improved productivity, but rarely emphasise broader societal policy goals relating to e.......g. sustainability or quality of life. The purpose of this paper is to explore how and to what extent public sector interventions that aim at forcing cluster development in industries can support sustainable development as defined in the Brundtland tradition and more recently elaborated in such concepts as eco-industrialism...
Quality Scalability Compression on Single-Loop Solution in HEVC
Directory of Open Access Journals (Sweden)
Mengmeng Zhang
2014-01-01
Full Text Available This paper proposes a quality scalable extension design for the upcoming high efficiency video coding (HEVC standard. In the proposed design, the single-loop decoder solution is extended into the proposed scalable scenario. A novel interlayer intra/interprediction is added to reduce the amount of bits representation by exploiting the correlation between coding layers. The experimental results indicate that the average Bjøntegaard delta rate decrease of 20.50% can be gained compared with the simulcast encoding. The proposed technique achieved 47.98% Bjøntegaard delta rate reduction compared with the scalable video coding extension of the H.264/AVC. Consequently, significant rate savings confirm that the proposed method achieves better performance.
Current parallel I/O limitations to scalable data analysis.
Energy Technology Data Exchange (ETDEWEB)
Mascarenhas, Ajith Arthur; Pebay, Philippe Pierre
2011-07-01
This report describes the limitations to parallel scalability which we have encountered when applying our otherwise optimally scalable parallel statistical analysis tool kit to large data sets distributed across the parallel file system of the current premier DOE computational facility. This report describes our study to evaluate the effect of parallel I/O on the overall scalability of a parallel data analysis pipeline using our scalable parallel statistics tool kit [PTBM11]. In this goal, we tested it using the Jaguar-pf DOE/ORNL peta-scale platform on a large combustion simulation data under a variety of process counts and domain decompositions scenarios. In this report we have recalled the foundations of the parallel statistical analysis tool kit which we have designed and implemented, with the specific double intent of reproducing typical data analysis workflows, and achieving optimal design for scalable parallel implementations. We have briefly reviewed those earlier results and publications which allow us to conclude that we have achieved both goals. However, in this report we have further established that, when used in conjuction with a state-of-the-art parallel I/O system, as can be found on the premier DOE peta-scale platform, the scaling properties of the overall analysis pipeline comprising parallel data access routines degrade rapidly. This finding is problematic and must be addressed if peta-scale data analysis is to be made scalable, or even possible. In order to attempt to address these parallel I/O limitations, we will investigate the use the Adaptable IO System (ADIOS) [LZL+10] to improve I/O performance, while maintaining flexibility for a variety of IO options, such MPI IO, POSIX IO. This system is developed at ORNL and other collaborating institutions, and is being tested extensively on Jaguar-pf. Simulation code being developed on these systems will also use ADIOS to output the data thereby making it easier for other systems, such as ours, to
Subber, Waad; Sarkar, Abhijit
2014-01-01
Recent advances in high performance computing systems and sensing technologies motivate computational simulations with extremely high resolution models with capabilities to quantify uncertainties for credible numerical predictions. A two-level domain decomposition method is reported in this investigation to devise a linear solver for the large-scale system in the Galerkin spectral stochastic finite element method (SSFEM). In particular, a two-level scalable preconditioner is introduced in order to iteratively solve the large-scale linear system in the intrusive SSFEM using an iterative substructuring based domain decomposition solver. The implementation of the algorithm involves solving a local problem on each subdomain that constructs the local part of the preconditioner and a coarse problem that propagates information globally among the subdomains. The numerical and parallel scalabilities of the two-level preconditioner are contrasted with the previously developed one-level preconditioner for two-dimensional flow through porous media and elasticity problems with spatially varying non-Gaussian material properties. A distributed implementation of the parallel algorithm is carried out using MPI and PETSc parallel libraries. The scalabilities of the algorithm are investigated in a Linux cluster.
Natural product synthesis in the age of scalability.
Kuttruff, Christian A; Eastgate, Martin D; Baran, Phil S
2014-04-01
The ability to procure useful quantities of a molecule by simple, scalable routes is emerging as an important goal in natural product synthesis. Approaches to molecules that yield substantial material enable collaborative investigations (such as SAR studies or eventual commercial production) and inherently spur innovation in chemistry. As such, when evaluating a natural product synthesis, scalability is becoming an increasingly important factor. In this Highlight, we discuss recent examples of natural product synthesis from our laboratory and others, where the preparation of gram-scale quantities of a target compound or a key intermediate allowed for a deeper understanding of biological activities or enabled further investigational collaborations.
Providing scalable system software for high-end simulations
Energy Technology Data Exchange (ETDEWEB)
Greenberg, D. [Sandia National Labs., Albuquerque, NM (United States)
1997-12-31
Detailed, full-system, complex physics simulations have been shown to be feasible on systems containing thousands of processors. In order to manage these computer systems it has been necessary to create scalable system services. In this talk Sandia`s research on scalable systems will be described. The key concepts of low overhead data movement through portals and of flexible services through multi-partition architectures will be illustrated in detail. The talk will conclude with a discussion of how these techniques can be applied outside of the standard monolithic MPP system.
Scalable and Hybrid Radio Resource Management for Future Wireless Networks
DEFF Research Database (Denmark)
Mino, E.; Luo, Jijun; Tragos, E.
2007-01-01
The concept of ubiquitous and scalable system is applied in the IST WINNER II [1] project to deliver optimum performance for different deployment scenarios, from local area to wide area wireless networks. The integration in a unique radio system of a cellular and local area type networks supposes...... describes a proposal for scalable and hybrid radio resource management to efficiently integrate the different WINNER system modes. Index...... a great advantage for the final user and for the operator, compared with the current situation, with disconnected systems, usually with different subscriptions, radio interfaces and terminals. To be a ubiquitous wireless system, the IST project WINNER II has defined three system modes. This contribution...
Scalability limitations of VIA-based technologies in supporting MPI
Energy Technology Data Exchange (ETDEWEB)
BRIGHTWELL,RONALD B.; MACCABE,ARTHUR BERNARD
2000-04-17
This paper analyzes the scalability limitations of networking technologies based on the Virtual Interface Architecture (VIA) in supporting the runtime environment needed for an implementation of the Message Passing Interface. The authors present an overview of the important characteristics of VIA and an overview of the runtime system being developed as part of the Computational Plant (Cplant) project at Sandia National Laboratories. They discuss the characteristics of VIA that prevent implementations based on this system to meet the scalability and performance requirements of Cplant.
A Scalable Smart Meter Data Generator Using Spark
DEFF Research Database (Denmark)
Iftikhar, Nadeem; Liu, Xiufeng; Danalachi, Sergiu
2017-01-01
Today, smart meters are being used worldwide. As a matter of fact smart meters produce large volumes of data. Thus, it is important for smart meter data management and analytics systems to process petabytes of data. Benchmarking and testing of these systems require scalable data, however, it can...... be challenging to get large data sets due to privacy and/or data protection regulations. This paper presents a scalable smart meter data generator using Spark that can generate realistic data sets. The proposed data generator is based on a supervised machine learning method that can generate data of any size...
Platinum clusters with precise numbers of atoms for preparative-scale catalysis.
Imaoka, Takane; Akanuma, Yuki; Haruta, Naoki; Tsuchiya, Shogo; Ishihara, Kentaro; Okayasu, Takeshi; Chun, Wang-Jae; Takahashi, Masaki; Yamamoto, Kimihisa
2017-09-25
Subnanometer noble metal clusters have enormous potential, mainly for catalytic applications. Because a difference of only one atom may cause significant changes in their reactivity, a preparation method with atomic-level precision is essential. Although such a precision with enough scalability has been achieved by gas-phase synthesis, large-scale preparation is still at the frontier, hampering practical applications. We now show the atom-precise and fully scalable synthesis of platinum clusters on a milligram scale from tiara-like platinum complexes with various ring numbers (n = 5-13). Low-temperature calcination of the complexes on a carbon support under hydrogen stream affords monodispersed platinum clusters, whose atomicity is equivalent to that of the precursor complex. One of the clusters (Pt 10 ) exhibits high catalytic activity in the hydrogenation of styrene compared to that of the other clusters. This method opens an avenue for the application of these clusters to preparative-scale catalysis.The catalytic activity of a noble metal nanocluster is tied to its atomicity. Here, the authors report an atom-precise, fully scalable synthesis of platinum clusters from molecular ring precursors, and show that a variation of only one atom can dramatically change a cluster's reactivity.
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c...
Scalable Track Initiation for Optical Space Surveillance
Schumacher, P.; Wilkins, M. P.
2012-09-01
least cubic and commonly quartic or higher. Therefore, practical implementations require attention to the scalability of the algorithms, when one is dealing with the very large number of observations from large surveillance telescopes. We address two broad categories of algorithms. The first category includes and extends the classical methods of Laplace and Gauss, as well as the more modern method of Gooding, in which one solves explicitly for the apparent range to the target in terms of the given data. In particular, recent ideas offered by Mortari and Karimi allow us to construct a family of range-solution methods that can be scaled to many processors efficiently. We find that the orbit solutions (data association hypotheses) can be ranked by means of a concept we call persistence, in which a simple statistical measure of likelihood is based on the frequency of occurrence of combinations of observations in consistent orbit solutions. Of course, range-solution methods can be expected to perform poorly if the orbit solutions of most interest are not well conditioned. The second category of algorithms addresses this difficulty. Instead of solving for range, these methods attach a set of range hypotheses to each measured line of sight. Then all pair-wise combinations of observations are considered and the family of Lambert problems is solved for each pair. These algorithms also have polynomial complexity, though now the complexity is quadratic in the number of observations and also quadratic in the number of range hypotheses. We offer a novel type of admissible-region analysis, constructing partitions of the orbital element space and deriving rigorous upper and lower bounds on the possible values of the range for each partition. This analysis allows us to parallelize with respect to the element partitions and to reduce the number of range hypotheses that have to be considered in each processor simply by making the partitions smaller. Naturally, there are many ways to
Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip
2014-02-28
In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.
Message Passing Framework for Globally Interconnected Clusters
Hafeez, M.; Asghar, S.; Malik, U. A.; Rehman, A.; Riaz, N.
2011-12-01
In prevailing technology trends it is apparent that the network requirements and technologies will advance in future. Therefore the need of High Performance Computing (HPC) based implementation for interconnecting clusters is comprehensible for scalability of clusters. Grid computing provides global infrastructure of interconnecting clusters consisting of dispersed computing resources over Internet. On the other hand the leading model for HPC programming is Message Passing Interface (MPI). As compared to Grid computing, MPI is better suited for solving most of the complex computational problems. MPI itself is restricted to a single cluster. It does not support message passing over the internet to use the computing resources of different clusters in an optimal way. We propose a model that provides message passing capabilities between parallel applications over the internet. The proposed model is based on Architecture for Java Universal Message Passing (A-JUMP) framework and Enterprise Service Bus (ESB) named as High Performance Computing Bus. The HPC Bus is built using ActiveMQ. HPC Bus is responsible for communication and message passing in an asynchronous manner. Asynchronous mode of communication offers an assurance for message delivery as well as a fault tolerance mechanism for message passing. The idea presented in this paper effectively utilizes wide-area intercluster networks. It also provides scheduling, dynamic resource discovery and allocation, and sub-clustering of resources for different jobs. Performance analysis and comparison study of the proposed framework with P2P-MPI are also presented in this paper.
Deployment Strategies and Clustering Protocols Efficiency
Directory of Open Access Journals (Sweden)
Chérif Diallo
2017-06-01
Full Text Available Wireless sensor networks face significant design challenges due to limited computing and storage capacities and, most importantly, dependence on limited battery power. Energy is a critical resource and is often an important issue to the deployment of sensor applications that claim to be omnipresent in the world of future. Thus optimizing the deployment of sensors becomes a major constraint in the design and implementation of a WSN in order to ensure better network operations. In wireless networking, clustering techniques add scalability, reduce the computation complexity of routing protocols, allow data aggregation and then enhance the network performance. The well-known MaxMin clustering algorithm was previously generalized, corrected and validated. Then, in a previous work we have improved MaxMin by proposing a Single- node Cluster Reduction (SNCR mechanism which eliminates single-node clusters and then improve energy efficiency. In this paper, we show that MaxMin, because of its original pathological case, does not support the grid deployment topology, which is frequently used in WSN architectures. The unreliability feature of the wireless links could have negative impacts on Link Quality Indicator (LQI based clustering protocols. So, in the second part of this paper we show how our distributed Link Quality based d- Clustering Protocol (LQI-DCP has good performance in both stable and high unreliable link environments. Finally, performance evaluation results also show that LQI-DCP fully supports the grid deployment topology and is more energy efficient than MaxMin.
Ben abdallah, R.; Breloy, A.; El Korso, M. N.; Lautru, D.; Hafdallah Ouslimani, H.
2017-10-01
We consider the problem of signal subspace estimation from a given sample set. We rely on the Bayesian framework developped in [1] to obtain minimum mean square distance (MMSD) estimators, which minimize the expected distance between the true projection matrix UU T and its estimate ÛÛ T . In this work, we extend the estimators of [1] to the context of linear model with Gaussian sources, with respectively Bingham and von Mises Fisher priors for the basis Ū. Numerical simulations are given in order to assess the performance of the proposed estimator. The interest of the considered approach is finally illustrated on a real data set, for a Space Time Adaptive Processing (STAP) application.
McMahon, Nicole D; Aster, Richard C.; Yeck, William; McNamara, Daniel E.; Benz, Harley M.
2017-01-01
The 6 November 2011 Mw 5.7 earthquake near Prague, Oklahoma is the second largest earthquake ever recorded in the state. A Mw 4.8 foreshock and the Mw 5.7 mainshock triggered a prolific aftershock sequence. Utilizing a subspace detection method, we increase by fivefold the number of precisely located events between 4 November and 5 December 2011. We find that while most aftershock energy is released in the crystalline basement, a significant number of the events occur in the overlying Arbuckle Group, indicating that active Meeker-Prague faulting extends into the sedimentary zone of wastewater disposal. Although the number of aftershocks in the Arbuckle Group is large, comprising ~40% of the aftershock catalog, the moment contribution of Arbuckle Group earthquakes is much less than 1% of the total aftershock moment budget. Aftershock locations are sparse in patches that experienced large slip during the mainshock.
Energy Technology Data Exchange (ETDEWEB)
Olofsson, K. Erik J., E-mail: erik.olofsson@ee.kth.se [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden); Brunsell, Per R.; Drake, James R. [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden)
2012-12-15
Highlights: Black-Right-Pointing-Pointer Unstable plasma response safely measured using special signal processing techniques. Black-Right-Pointing-Pointer Prediction-capable MIMO models obtained. Black-Right-Pointing-Pointer Computational statistics employed to show physical content of these models. Black-Right-Pointing-Pointer Multifold cross-validation applied for the supervised learning problem. - Abstract: A multibatch formulation of a multi-input multi-output closed-loop subspace system identification method is employed for the purpose of obtaining control-relevant models of the vacuum-plasma response in the magnetic confinement fusion experiment EXTRAP T2R. The accuracy of the estimate of the plant dynamics is estimated by computing bootstrap replication statistics of the dataset. It is seen that the thus identified models exhibit both predictive capabilities and physical spectral properties.
Quicksilver: Middleware for Scalable Self-Regenerative Systems
2006-04-01
standard best practice in the area, and hence helped us identify problems that can be justified in terms of real user needs. Our own group may write a...semantics, generally lack efficient, scalable implementations. Systems aproaches usually lack a precise formal specification, limiting the
Scalable learning of probabilistic latent models for collaborative filtering
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre
2015-01-01
Collaborative filtering has emerged as a popular way of making user recommendations, but with the increasing sizes of the underlying databases scalability is becoming a crucial issue. In this paper we focus on a recently proposed probabilistic collaborative filtering model that explicitly...
PSOM2—partitioning-based scalable ontology matching using ...
Indian Academy of Sciences (India)
B Sathiya
2017-11-16
Nov 16, 2017 ... Abstract. The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity.
Cognition-inspired Descriptors for Scalable Cover Song Retrieval
van Balen, J.M.H.; Bountouridis, D.; Wiering, F.; Veltkamp, R.C.
2014-01-01
Inspired by representations used in music cognition studies and computational musicology, we propose three simple and interpretable descriptors for use in mid- to high-level computational analysis of musical audio and applications in content-based retrieval. We also argue that the task of scalable
Scalable Directed Self-Assembly Using Ultrasound Waves
2015-09-04
at Aberdeen Proving Grounds (APG), to discuss a possible collaboration. The idea is to integrate the ultrasound directed self- assembly technique ...difference between the ultrasound technology studied in this project, and other directed self-assembly techniques is its scalability and...deliverable: A scientific tool to predict particle organization, pattern, and orientation, based on the operating and design parameters of the ultrasound
Scalable electro-photonic integration concept based on polymer waveguides
Bosman, E.; Steenberge, G. van; Boersma, A.; Wiegersma, S.; Harmsma, P.J.; Karppinen, M.; Korhonen, T.; Offrein, B.J.; Dangel, R.; Daly, A.; Ortsiefer, M.; Justice, J.; Corbett, B.; Dorrestein, S.; Duis, J.
2016-01-01
A novel method for fabricating a single mode optical interconnection platform is presented. The method comprises the miniaturized assembly of optoelectronic single dies, the scalable fabrication of polymer single mode waveguides and the coupling to glass fiber arrays providing the I/O's. The low
Coilable Crystalline Fiber (CCF) Lasers and their Scalability
2014-03-01
highly power scalable, nearly diffraction-limited output laser. 37 References 1. Snitzer, E. Optical Maser Action of Nd 3+ in A Barium Crown Glass ...Electron Devices Directorate Helmuth Meissner Onyx Optics Approved for public release; distribution...lasers, but their composition ( glass ) poses significant disadvantages in pump absorption, gain, and thermal conductivity. All-crystalline fiber lasers
Efficient Enhancement for Spatial Scalable Video Coding Transmission
Directory of Open Access Journals (Sweden)
Mayada Khairy
2017-01-01
Full Text Available Scalable Video Coding (SVC is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC. In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET. Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL and enhancement layers (ELs and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP and the interlayer base mode (ILBM method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP and the full-base mode (FBM method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.
Scalable power selection method for wireless mesh networks
CSIR Research Space (South Africa)
Olwal, TO
2009-01-01
Full Text Available This paper addresses the problem of a scalable dynamic power control (SDPC) for wireless mesh networks (WMNs) based on IEEE 802.11 standards. An SDPC model that accounts for architectural complexities witnessed in multiple radios and hops...
Estimates of the Sampling Distribution of Scalability Coefficient H
Van Onna, Marieke J. H.
2004-01-01
Coefficient "H" is used as an index of scalability in nonparametric item response theory (NIRT). It indicates the degree to which a set of items rank orders examinees. Theoretical sampling distributions, however, have only been derived asymptotically and only under restrictive conditions. Bootstrap methods offer an alternative possibility to…
2011-12-01
which includes the current from regenerative braking . Repeated UAC cycles are used as the model input to generate the surface temperature Ts to test...battery thermal dynamics is the key to an effective thermal management system and to main- tain safety, performance, and life longevity of these Li-Ion...the current and surface temperature of the battery, which are the commonly mea- sured signals in a vehicle battery management system . It is shown that
Reliable Radiation Hybrid Maps: An Efficient Scalable Clustering-based Approach
The process of mapping markers from radiation hybrid mapping (RHM) experiments is equivalent to the traveling salesman problem and, thereby, has combinatorial complexity. As an additional problem, experiments typically result in some unreliable markers that reduce the overall quality of the map. We ...
AllahTavakoli, Y.; Bagheri, H.; Safari, A.; Sharifi, M.
2012-04-01
This paper is mainly aiming to prove that the stripy noises in the map of earth's surface mass-density changes derived from GRACE Satellites gravimetry, is due to a dissatisfaction of Compact Picard Condition (CPC) with the GRACE data in the inversion of the Newton Integral Equation over the thin layer of earth; and hence the paper proposes the regularization strategies as efficient tools to treat the Ill-posedness and consequently to de-strip the data. First of all, we preferred to slightly modify the mathematical model of earth's surface mass-density changes developed creatively first by J. Wahr and et.al (1998), according to the all their previous assumptions plus taking into consideration the effect of the earth topography. By the modification we expect that some uncertainties in the prior model have been reduced to some extent. Then we analyzed the CPC on the model and we demonstrated how to perform Generalized Tikhonov regularization in Sobolev subspace for overcoming the instability of the problem. Then we applied the strategy in some simulations and case studies to validate our ideas. The simulations confirm that the stripy noises in the GRACE-derived map of the mass-density changes are due to the CPC dissatisfaction and furthermore the case studies show that Generalized Tikhonov regularization in Sobolev subspace is an influential filtering tool to de-strip the noisy data. Also, the case studies interestingly show that the effect of the topography is comparable to the effect of the load Love numbers on the Wahr's model; hence it may be taken into consideration when the load Love numbers have been taken into account.
Salvesen
1999-11-01
The care of patients with cluster headache has at least two goals: 1) immediately abolishing an ongoing attack and 2) stopping or shortening a bout (a cluster period). The fierceness and the relative brevity of the attacks dictate the use of a fast-acting agent. There are probably three agents fulfilling these criteria: sumatriptan (by subcutaneous injection), oxygen (inhaled through a face mask), and ergotamines (by injection or, perhaps, sublingual tablets). An abundance of data from controlled studies as well as recent clinical experience probably favors sumatriptan as the most effective alternative, the most significant drawback being its high cost. Oxygen inhalation is free of side effects and may be effective but is inconvenient to use. Ergotamines in tablet form act less rapidly, and there are more contraindications to their use. In short-term prophylaxis, however, ergotamine may still be a drug of choice if the timing of the attacks allows planned use of the drug shortly before the attack. If the timing is more irregular, steroids may at least temporarily break a cycle (eg, prednisolone, 60 or 80 mg/d, gradually tapered to zero in 3 to 4 weeks). If more long-lasting prophylaxis is needed or expected, lithium carbonate, 900 mg/d, or verapamil, 360 mg/d, both have reasonable response rates. As for chronic cluster headache, lithium probably will still be the drug of choice. For a very limited group of patients with chronic cluster headache, surgery may be a last resort. The best surgical options are probably radiofrequency rhizotomy or microvascular decompression of the trigeminal nerve.